This PDF file contains the front matter associated with SPIE Proceedings Volume 11312, including the Title Page, Copyright Information, Table of Contents, Author and Conference Committee lists.

In this work, we present a new concept for dynamic beam attenuation, the z-aligned sheet-based dynamic beam attenuator (z-sbDBA). Like the previously presented sbDBA, it allows dynamically adapting the x-ray intensity over the projection angle and also the fan beam angle by changing the tilt angle of the z-sbDBA. In contrast to the sbDBA, however, the absorbing sheets of a z-sbDBA are parallel to the detector rows. In addition, the height of the absorbing sheets varies over the fan angle – very low near the central beam, increasing toward larger fan beam angles. This facilitates designing the absorption profile of the z-sbDBA, which was not feasible with the sbDBA. Due to the changed orientation of the attenuation sheets, pronounced structures along the fan beam width are avoided, reducing the risk of ring artifacts in the reconstructed image.

A prototype of the z-sbDBA, mounted on a drive, has been built and investigated. Using a clinical computed tomography (CT) scanner, we experimentally demonstrate that variable and smooth intensity profiles can be realized by the controlled change of the angular position of the z-sbDBA. Reconstructed images do not reveal substantial artifacts, thus proving the necessary stability of the acquisition technique. We also show that the variance across a reconstructed image can be changed as a function of the tilt angle.

Our experimental results demonstrate that the new z-sbDBA concept maintains the main advantage of the sbDBA concept, which is the dynamic fluence field modulation (FFM) of the emitted x-ray beam. In addition, our findings show that due to the improved z-sbDBA structuring several drawbacks of the sbDBA can be overcome by a) avoiding pronounced structures along the fan beam angle, b) requiring only small tilt angles and c) allowing for a flexible design of the transmission profile propagated toward the patient.

We present further progress on the implementation of C-arm CT imaging with the extended line-ellipse-line (LEL) trajectory. This novel data acquisition geometry is designed to enhance image quality in interventional radiology. Previously, we showed that robust extended LEL data acquisition is feasible using a state-of-the-art multi-axis robotic C-arm (ARTIS pheno, Siemens Healthcare GmbH, Germany) and we also showed that accurate reconstruction from real data can be obtained using an iterative algorithm. The extensive computational effort required by such an algorithm is however not suitable for clinical translation. Reconstruction using a filtered- backprojection (FBP) formula would be practical. To use such a formula, there needs to be a technique to handle imperfections in the data acquisition geometry, which result from mechanical vibrations and gravity effects. We recently presented such a technique, but this development was only carried out for a single cycle of the LEL trajectory. In this work, we address the more challenging issue of reconstructing the volume covered by multiple cycles of the trajectory. Specifically, we propose an extension of our single cycle approach to multiple cycles. We successfully demonstrate that our procedure now allows seamless volume reconstruction from real data using a cone-beam performance phantom as well as an anthropomorphic head phantom. Our results bring the extended LEL trajectory closer to clinical deployment for improved image quality in interventional radiology. Further work will focus on increasing the number of views to avoid few view artifacts and on thoroughly demonstrating image quality benefits.

Orthopedic tomosynthesis is emerging as an attractive alternative to digital radiography (DR), with increased sensitivity for some clinical tasks, including fracture diagnosis and staging and follow-up of arthritis. Commercially available digital tomosynthesis (DTS) systems are complex, room-sized devices. A compact tomosynthesis system for extremity imaging (TomoE) was previously demonstrated using carbon nanotube (CNT) x-ray source array technology. The purpose of this study was to evaluate the prototype device in preparation for an Institutional Review Board (IRB)- approved patient imaging study and evaluate initial patient images.

A tabletop device was constructed using a short CNT x-ray source array, operated in three positions, and a flat panel digital detector. Twenty-one x-ray projection images were acquired at incident angles from -20 to +20 degrees in various clinical orientations, with entrance doses matched to commercial in-room DTS scanners. The projection images were reconstructed with an iterative reconstruction technique in 1mm slices. Cadaveric specimen and initial participant images were reviewed by radiologists for feature conspicuity and diagnostic accuracy.

TomoE image quality was found to be superior to DR, with reconstruction slices exhibiting visual conspicuity of trabecular bone, delineation of joint space, bone erosions, fractures, and clear depiction of normal anatomical features. The scan time was fifteen seconds with mechanical translation. Skin entrance dose was verified to be 0.2mGy. TomoE device image quality has been evaluated in cadaveric specimens and dose was calibrated for a patient imaging study. Initial patient images depict a high level of anatomical detail an increase in diagnostic value compared to DR.

Purpose: We investigate the feasibility of slot-scan dual-energy x-ray absorptiometry (DXA) on robotic x-ray platforms capable of synchronized source and detector translation. This novel approach will enhance the capabilities of such platforms to include quantitative assessment of bone quality using areal bone mineral density (aBMD), normally obtained only with a dedicated DXA scanner. Methods: We performed simulation studies of a robotized x-ray platform that enables fast linear translation of the x-ray source and flat-panel detector (FPD) to execute slot-scan dual-energy (DE) imaging of the entire spine. Two consecutive translations are performed to acquire the low-energy (LE, 80 kVp) and high-energy (HE, 120 kVp) data in <15 sec total time. The slot views are corrected with convolution-based scatter estimation and backprojected to yield tiled long-length LE and HE radiographs. Projection-based DE decomposition is applied to the tiled radiographs to yield (i) aBMD measurements in bone, and (ii) adipose content measurement in bone-free regions. The feasibility of achieving accurate aBMD estimates was assessed using a high-fidelity simulation framework with a digital body phantom and a realistic bone model covering a clinically relevant range of mineral densities. Experiments examined the effects of slot size (1 – 20 cm), scatter correction, and patient size/adipose content (waist circumference: 77 – 95 cm) on the accuracy and reproducibility of aBMD. Results: The proposed combination of backprojection-based tiling of the slot views and DE decomposition yielded bone density maps of the spine that were free of any apparent distortions. The x-ray scatter increased with slot width, leading to aBMD errors ranging from 0.2 g/cm² for a 5 cm slot to 0.7 g/cm² for a 20 cm slot when no scatter correction was applied. The convolution-based correction reduced the aBMD error to within 0.02 g/cm² for slot widths <10 cm. Reproducible aBMD measurements across a range of body sizes (aBMD variability <0.1 g/cm²) were achieved by applying a calibration based on DE adipose thickness estimates from peripheral body sites. Conclusion: The feasibility of accurate and reproducible aBMD measurements on an FPD-based x-ray platform was demonstrated using DE slot scan trajectories, backprojection-domain decomposition, scatter correction, and adipose precorrection.

This work aims to combine compressed sensing reconstruction with a deep learning framework to leverage their individual strengths and enable 123-view moderate-dose sparse-view reconstruction for diagnostic CT imaging systems. Specifically, linear FBP reconstruction was applied to reconstruct sparse data, followed by a trained U-Net to remove artifacts resulting from undersampling. This methodology has been exploited in many other deep learning applications for CT imaging. However, this approach may be subjected to the generalizability issue in which the reconstruction can either remove real lesions or add lesions that may not exist. This will be demonstrated in our results. In our proposed work, the output of the network is used as the prior image for a prior image constrained compressed sensing (PICCS) reconstruction. This step helps to ensure the reconstructed image is consistent with the measured data. Finally, the PICCS reconstructed image is further cleaned up by a trained light duty U-Net to improve noise texture and reduce noise to generate the final reconstructed image. Both simulation data and human subject data were used to validate the proposed image reconstruction framework. In simulation studies, it is demonstrated that the final output corrected the distorted structures in the deep learning-only reconstruction with respect to the shape, size and contrast of the structures. The final images also appeared streak-free with more natural noise texture when compared with the PICCS-only reconstruction. In human subject validation, the false positive lesion-like structures in the deep learning prior image were eliminated in the final output.

Low-dose computed tomography (CT) has attracted much attention in clinical applications since X-ray radiations can cause serious health risks to patients. Sparse-view CT imaging is one of the major ways to reduce radiation dose. However, if reconstructing the sparse-view CT images with conventional filtered backprojection (FBP) algorithm, image quality may be significantly degraded due to the severe streaking artifacts. Therefore, iterative sparse-view CT image reconstruction (IR) algorithms have been developed and utilized to improve image quality. One drawback of using the IR algorithms is they usually spend long computation time. Additionally, adjusting and optimizing the hyper-parameters that are needed during iteration procedures is also time-consuming, some- times may even depend on individual experience. These potential drawbacks strongly limit the wide applications of IR algorithms. Aiming at partially overcome such difficulties, in the present work, we propose a deep iterative reconstruction (DIR) framework to generalize the conventional IR algorithms by mapping them into the deep neural network (DNN) technique. With this proposed DIR algorithms, the prior term, the data fidelity term, and the hyper-parameters can all be represented and learned by network. By doing so, some generalized iterative models can be used to perform high quality sparse-view CT image reconstructions. Numerical experiments based on clinical patient data demonstrated that the proposed DIR algorithms can mitigate the streaking artifacts more effectively while well preserving the subtle structures.

Helical CT has been widely used in clinical diagnosis. Sparsely spaced multidetector in z direction can increase the coverage of the detector provided limited detector rows. It can speed up volumetric CT scan, lower the radiation dose and reduce motion artifacts. However, it leads to insufficient data for reconstruction. That means reconstructions from general analytical methods will have severe artifacts. Iterative reconstruction methods might be able to deal with this situation but with the cost of huge computational load. In this work, we propose a cascaded dual-domain deep learning method that completes both data transformation in projection domain and error reduction in image domain. First, a convolutional neural network (CNN) in projection domain is constructed to estimate missing helical projection data and converting helical projection data to 2D fan-beam projection data. This step is to suppress helical artifacts and reduce the following computational cost. Then, an analytical linear operator is followed to transfer the data from projection domain to image domain. Finally, an image domain CNN is added to improve image quality further. These three steps work as an entirety and can be trained end to end. The overall network is trained using a simulated lung CT dataset with Poisson noise from 25 patients. We evaluate the trained network on another three patients and obtain very encouraging results with both visual examination and quantitative comparison. The resulting RRMSE is 6.56% and the SSIM is 99.60%. In addition, we test the trained network on the lung CT dataset with different noise level and a new dental CT dataset to demonstrate the generalization and robustness of our method.

A fast scan with a high helical pitch is desirable for many CT exams, such as pediatric, chest, and some of cardiovascular exams, to suppress patient motion artifacts. However, on a single-source scanner, the pitch typically cannot exceed ~1.5 without generating image distortion within the entire scanning field of view due to insufficient data acquired in a fast pitch mode. In this work, we developed a deep convolutional neural network-based approach to reducing artifacts on images reconstructed from insufficient data acquired in an ultra-fast-pitch mode (𝑝𝑝 ≥ 2.0). This custom-designed neural network, referred to as Ultra-fast-pitch image reconstruction neural network (UFP-net) consists of functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the fast-pitch mode. The UFP-net was trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss. Projection data at a regular pitch (𝑝𝑝 = 1.0) and a fast-pitch (𝑝𝑝 = 3.0) were simulated using 10 patient CT cases to generate training and validation datasets. Compared to filtered-back-projection (FBP), the UFP-net largely suppressed image artifacts and restored anatomical details. The structural similarity index (SSIM) was significantly improved (Mean SSIM: UFP-net 0.9, FBP 0.6), and the root-mean-square-error (RMSE) was largely reduced (Mean RMSE: UFP-net 57 HU, FBP 273 HU). The proposed method has the potential to enable ultra-fast-pitch data acquisition on single-source CT scanners to improve scanning speed while maintaining image quality.

Advancements in deep learning and GPU computing have exponentially driven the application of neural networks to classic medical imaging problems: denoising, segmentation, artifact removal, etc. Deep learning solutions are particularly attractive for processing multi-channel, volumetric image data, where processing and reconstruction methods are often computationally expensive. Convolutional neural networks (CNNs) are commonly applied to multi-channel image data by matching the number of network input channels to the number of data channels, learning explicit relationships between channels. This provides a high degree of specificity to a particular problem, but may fail to generalize to a broader class of closely related problems. We propose a solution to this generalization problem in the context of spectral x-ray CT, where the scanning kVps (energy bins) and contrast are often variable. Specifically, we propose a novel CNN architecture which handles variable numbers of input channels, variable noise levels between channels, and variable modes of spectral contrast. We demonstrate our architecture in the application of preclinical, photon-counting, micro-CT, effectively replacing 1-2 hours of iterative reconstruction, with <10 minutes of analytical reconstruction and CNN regularization. Experimental validation shows the effectiveness of our approach when applied to both in vivo photon-counting validation data (4 energy thresholds) and to simulated, dual-energy CT data virtually acquired with an energy integrating detector. In both cases, the results output by the CNN provide greater spectral accuracy than analytical reconstruction alone, but suffer from some degradation of spatial resolution. We conclude by proposing several extensions of our work to better preserve spatial resolution.

With the development of deep learning (DL), many deep learning (DL) based algorithms have been widely used in the low-dose CT imaging and achieved promising reconstruction performance. However, most DL-based algorithms need to pre-collect a large set of image pairs (low-dose/high-dose image pairs) and trains networks in a supervised end-to-end manner. Actually, it is not feasible in clinical to obtain such a large amount of paired training data, especially for high-dose ones. Therefore, in this work, we present a semi-supervised learned sinogram restoration network (SLSR-Net) for low-dose CT image reconstruction. The presented SLSR-Net consists of supervised sub-network and unsupervised sub-network. Specifically, different from the traditional supervised DL networks which only use low-dose/high-dose sinogram pairs, the presented SLSR-Net method is capable of feeding only a few supervised sinogram pairs and massive unsupervised low-dose sinograms into the network training procedure. The supervised pairs are used to capture critical features (i.e., noise distribution, and tissue characteristics) latent in a supervised way and the unsupervised sub-network efficiently learns these features using a conventional weighted least-squares model with a regularization term. Moreover, another contribution of the presented SLSR-Net method is to adaptively transfer learned feature distribution from supervised subnetwork with the paired sinograms to unsupervised sub-network with unlabeled low-dose sinograms to obtain high-fidelity sinogram with a Kullback-Leibler divergence. Finally, the filtered backprojection algorithm is used to reconstruct CT images from the obtained sinograms. Real patient datasets are used to evaluate the performance of the presented SLSR-Net method and the corresponding experimental results show that compared with the traditional supervised learning method, the presented SLSR-Net method achieves competitive performance in terms of noise reduction and structure preservation in low-dose CT imaging.

Recently, multi-energy inter-pixel coincidence counter (MEICC) has been proposed for charge sharing correction and compensation for photon counting detectors (PCDs), which uses energy-dependent coincidence counters between a pixel-of-interest (POI) and its neighboring pixels and keeps the book of charge sharing events. A Monte Carlo (MC) simulation study was performed to assess the performance of MEICC; however, the performance might have been overestimated in a previous study. The charge sharing increases the number of photons recorded at a PCD pixel at the expense of the spatial resolution and the effect on the spatial resolution was not taken into account. The aims of this study were, first, to outline a spatially-resolved irradiation scheme for a normalized spatial resolution, and second, to assess the performance of MEICC. The MC program was modified to set the x-ray spectrum and intensity for the POI and those for the neighboring pixels separately. Data were then generated by varying two basis materials, water and bone, by a small amount only for the POI, while those for neighboring pixels remained unchanged. Cramér–Rao lower bound (CRLB) of water and bone estimation was computed from the changes in the mean and covariance matrix estimated. This setting allowed for assessing the CRLB at the same spatial resolution regardless of with or without charge sharing. The CRLBs at the normalized spatial resolution were worse than those at not-normalized spatial resolutions. With eight energy windows, MEICC provided the CRLB comparable to the ideal PCD with no charge sharing. The proposed MEICC has a potential to address charge sharing in PCDs.

Recently we have reported indirect X-ray photon-counting imaging using CMOS photon detectors (CPD) and have shown its high spatial resolution with MTF of over 0.7 at 10LP/mm [1]. However, at that time its energy resolution potential and the quantum efficiency were totally unknown. Thus, there was a question about whether it can detect relatively low energy X-ray photons, for example around 20keV used for mammography, with sufficient quantum efficiency while eliminating counting errors.

In this study we exposed the CPD test devices to near single energy X-ray photons of 19.5keV adopting a clinical mammography equipment and additional Mo filters, and measured output intensity distributions. We also fitted our intensity distribution model to the results estimating signal yield per keV and parameters for signal variation.

We tested with 2 types of Hamamatsu Photonics CsI(Tl) scintillators. A CPD with J6675-01 scintillator plate has a signal distribution of 76% FWHM, still showing sufficient capability of photon counting with a little loss of actual signals. The signal yield is about 3.6 e-/keV. In the meanwhile, a CPD with J6675 has a superior distribution of 49% FWHM with signal yield of 7.1 e-/keV.

In both types, the dominant factor of the variation is not shot noise or sensor noise but something that is proportional to the signal intensity. This variation factor is possibly attributed to random noise of scintillation intensity, which is about 28% rms in J6675-01 and 17% rms in J6675.

One of the existing prototype detector systems for full-field photon-counting CT is a silicon detector developed by our group. Spatial resolution is clinically important to resolve small details and can enable more efficient phase-contrast imaging. However, improving the resolution is difficult as decreasing the pixel size is associated with technical challenges. By integrating CMOS electronics into the silicon sensor, it is possible to reduce the pixel size drastically while also introducing on-sensor data processing capabilities. In this work, we evaluate the feasibility of measuring the charge cloud shape of Compton interactions in a silicon strip detector to increase the spatial resolution. With an incident spectrum of 140 kVp, Compton interactions constitute 66.2% of the detected interactions. By combining a Monte Carlo photon simulation with a charge transport model, we study the charge cloud distributions and induced currents as functions of the interaction position. For a simulated silicon strip detector with a pixel size of 12×500 μm² , we present a method in which the interaction position can be determined. For an ideal case without electronic noise an average absolute error of 0.65 μm is obtained in the direction along the wafer and 13.08 μm in the trans-wafer direction. With simulated electronic noise and a lowest threshold of 0.88 keV the corresponding values are 1.38 μm and 122.83 μm. Our results show that the proposed method has the potential to very significantly increase the spatial resolution in a full-field photon-counting detector for CT.

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT applications with high-fidelity spectral information, transforming X-ray CT into a functional and molecular imaging modality, while maintaining the high spatial resolution, fast scanning times, and relatively low cost. We present our novel dual source pre-clinical micro-CT prototype system that combines a photon counting detector (PCD) and an energy integrating detector (EID) in a single system. We compare performance for PCD-only with combined hybrid (i.e. PCD and EID) CT imaging with equal dose. All CT data sets (EID-only, PCD-only, and hybrid) were reconstructed using our iterative, multi-channel algorithm based on the split Bregman method and regularization with rank-sparse kernel regression. We used a post-reconstruction decomposition method with iodine (I), gold (Au), gadolinium (Gd), and calcium (Ca) basis functions. Performance was assessed using micro-CT phantoms. Our results show the spatial resolution of the PCD-only and the hybrid reconstructions were similar and slightly better than for EID-only reconstruction with ~3.5 lp/mm (10% MTF). The NPS of the hybrid reconstruction was similar to PCD-only reconstruction. By co-reconstructing EID and PCD data, we achieved better image quality in the material decomposition but marginal differences in terms of concentration accuracy. Hybrid spectral micro- CT can benefit both nanotechnology and cancer research by providing imaging that can help test and optimize various NPs for theranostics.

Practical photon counting detectors that have been adopted for commercial use are typically based on crystalline or polycrystalline materials. However, these types of materials are challenging to scale to large-area medical imaging applications because of yield and cost issues associated with the crystal growth and bonding technology required to interface the sensor with the readout IC. An alternate approach is to use a large-area-compatible, mature, direct conversion X-ray-detection sensor such as amorphous selenium (a-Se). The technical challenges for photon counting with a-Se lie in overcoming (1) the slow carrier-transport material property of a-Se, which leads to count-rate limitations due to pile-up, and (2) the lower X-ray-to-charge conversion gain, which degrades SNR and can be resolved by improved design of pixel readout circuits. In this paper, we address the a-Se material limitation by leveraging a unipolar charge sensing detector design. We demonstrate that the proposed unipolar charge sensing detector provides an effective method to detect charge of the polarity type having a higher mobility-lifetime product, obviating the need for detection of the opposite polarity slow transport charge. Transient signal measurements indicate that a quasi depth independent signal rise-time is achieved with the unipolar charge sensing detector. Moreover, two orders of magnitude improvement is observed compared to the conventional a-Se detector rise-time (0.15 μs vs. 25 μs).

Wide-angle digital breast tomosynthesis (DBT) gives better depth resolution and tissue separation while the microcalcification (MC) detectability is impacted by many factors, such as detector performance, focal spot motion (FSM), increased noise due to scatter radiation, angular dose distribution and image reconstruction methods. This study aims at developing an in-silico experimental pipeline to compare these factors’ influence on the MC detectability, so that new system design can be proposed to improve the performance. The VICTRE tool developed by Food and Drug Administration was used to generate a 49 mm-thick anthropomorphic digital breast phantom and the projection images using Monte Carlo simulation. We inserted 38 MC clusters with size of 120 μm at 40 mm from the bottom of the phantom. The Monte Carlo simulation used parameters of a clinical wide-angle DBT system, with 25 projections over 50-degree angular range, and 28 kVp W/Rh energy spectrum. Projection images were simulated under different scenarios: uniform/nonuniform angular dose distribution, with/without FSM of 2 mm, with/without scatter radiation, and TFT/CMOS detector types. A four-alternative forced choice (4AFC) methodology was employed to evaluate the MC detectability. The percentage correct and visibility score under all scenarios were compared. The results show that MC detectability decreases with the presence of either increased noise due to scattered radiation, or image blur due to FSM. Nonuniform angular dose distribution improves the detectability of MCs when using FBP reconstruction with narrow slice thickness filter. MC sharpness is improved with CMOS detector with smaller pixel size and lower electronic noise.

The limited angle and limited number of projections in digital breast tomosynthesis (DBT) produce under-sampled datasets that may compromise calcification detection. Small breast lesions, such as microcalcifications, may not be discernible without sufficient sampling in the reconstructed DBT images. We propose a virtual clinical trial (VCT) method to evaluate the calcification detection in DBT using computer simulations of breast phantoms, images, and virtual readers. We used multiple-reader multiple-case (MRMC) receiver operating characteristic (ROC) analyses to evaluate the performance of channelized Hotelling observers (CHOs) in calcification detection. The angular motion path of the x-ray source was varied to simulate different DBT acquisition geometries. We simulated continuous and step-and-shoot x-ray source motion and three angular motion paths: ±7.5°, ±15°, and ±25°. The detection of calcifications is affected by the angular motion path, particularly for the ±25° angular range, combined with continuous tube motion, larger detector element sizes (0.14 mm) and larger reconstructed voxel sizes (0.10 mm). When an angular range of ±25° is compared to ±7.5°, the difference in the area under the curve (AUC) is -0.030 (d’ ratio=0.633) and -0.067 (d’ ratio=0.584), for one and two- voxel calcifications (0.1 mm³ and 0.2 mm³), respectively. There is no significant difference in calcification detection using images acquired with ±7.5° and ±15°. The results provide insight on the impact of angular range for calcification detection, an ongoing limitation of tomosynthesis.

Aim: Investigate 3D structured DBT phantoms with lesion models for use in the evaluation of synthetic mammography (SM) imaging performance.

Methods: 4 phantoms were investigated: CDMAM, L1, CIRS BR3D and Modular DBT Phantom (two different inserts). The phantoms were imaged on recent DBT models: Fujifilm Amulet Innovality (ST mode), GE HC Senographe Pristina, Hologic 3Dimensions, IMS Giotto Class and Siemens Mammomat Revelation. Images were acquired at automatic exposure control (AEC) level, half AEC and twice AEC. SM was calculated. The CDMAM and L1 phantom were read by human readers via a 4-Alternative Forced Choice method and thresholds were established. CIRS BR3D and Modular DBT Phantom were analysed by counting visible lesions.

Results: The scores obtained from the phantoms had the same tendencies among systems. The phantoms highlight many specific characteristics of the SM algorithms such as tuning contrast enhancement to a range of sizes. The phantoms confirm, as in 2D and DBT, an impact of dose on detectability of microcalcification-like inserts but not on masses. None of the phantoms evaluate the SM for different glandular tissue or thickness distributions.

Conclusion: For all phantoms, SM found a number of lesion-like targets and an impact of dose as expected. Whether these phantom readings are representative for quality in SM in real practice is not yet proven. More elaborated sensitivity studies should be done prior to the use of the phantoms in routine QC. Ultimately, accurate assessment of SM may have to be done via virtual trials.

Purpose: In this work we present equivalent breast thickness and dose sensitivity of a next iteration 3D structured breast phantom with lesion models to demonstrate its potential use for quality assurance measurements in breast imaging. Methods: PMMA equivalent thickness was determined employing the automatic exposure control (AEC) of Siemens Mammomat Inspiration and Siemens Mammomat Revelation. A 2D projection image of the phantom was acquired and the corresponding AEC settings recorded as reference. Equivalent PMMA thickness was found by interpolating between three PMMA thicknesses with mAs values close to the reference settings selected by AEC. Dose sensitivity of the reconstructed digital breast tomosynthesis (DBT) images was assessed by two experienced readers using a four alternative forced choice (4-AFC) study. Three different dose levels for lesion models and microcalcifications were evaluated. Results: PMMA equivalent thickness of the phantom was 46.8 mm and 47.0 mm for measurements on Siemens Mammomat Inspiration and Siemens Mammomat Revelation which equals to a breast equivalent thickness of 55.5 mm and 55.8 mm, respectively, compared to a physical phantom thickness of 53.5 mm. For lesion models dose sensitiviy of the detectability was not obvious. For microcalcification the diameter threshold was found to increase for decreasing dose from high dose to AEC to low dose. Conclusions: We found the measured equivalent breast thickness of our phantom to be close to its physical thickness. It can be concluded that changes in dose can be detected by the presented phantom for the tested dose levels.

This study introduces a methodology for generating high resolution signal profiles of microcalcification (MC) grains for validating breast CT (bCT) systems. A physical MC phantom was constructed by suspending calcium carbonate grains in an agar solution emulating MCs in a fibroglandular tissue background. Additionally, small Teflon spheres (2.4 mm diameter) were embedded in the agar solution for the purpose of fiducial marking and assessment of segmentation accuracy. The MC phantom was imaged on a high resolution (34 μm) commercial small-bore μCT scanner at high dose, and the images were used as the gold-standard for assessing MC size and for generating high resolution signal profiles of each MC. High-dose bCT scans of the MC phantom suspended in-air were acquired using 1×1 binning mode (75 μm dexel pitch) by averaging three repeat scans to produce a single low-noise reconstruction of the MC phantom. The high resolution μCT volume data set was then registered with the corresponding bCT data set after correcting for the bCT system spatial resolution. Microcalcification signal profiles constructed using low-noise bCT images were found to be in good agreement with those generated using the μCT scanner with all differences <10% within the VOI surrounding each MC. The MC signal profiles were used as detection templates for a non-prewhitening-matched-filter model observer for scans acquired in a realistic breast phantom at 3, 6, and 9 mGy mean glandular dose. MC detectability using signal templates derived from bCT were shown to be in good agreement with those generated using μCT.

Based on chemical composition, two types of microcalcifications are found within the breast. Type I calcifications consist of calcium oxalate (CaOx), whereas Type II calcifications are composed of calcium phosphates, mainly hydroxyapatite (CaHa). Previous tissue analysis studies^{1, 2} have shown that Type I calcifications are typically found in benign lesions, and are not associated with carcinoma. Type II calcifications, on the other hand, can be found in both benign and malignant breast lesions. It was estimated¹ that lesions with solely Type I calcifications represented 12-17% of benign biopsies performed. Therefore, an imaging method that could differentiate calcified lesions with solely CaOx calcifications might help in reducing unnecessary breast biopsies. Previous studies^3-5 have shown that dual-energy mammography can be an effective tool in discriminating between these two classes of calcifications. In this work we investigate if a convolutional neural network (CNN) can be trained to distinguish between these two types of calcified clusters, using dual-energy x-ray mammograms. We implement a Monte Carlo mammography simulator package to model a dual-energy FFDM system and use a realistic anthropomorphic digital breast phantom model⁶ with embedded microcalcifications. A Resnet CNN architecture was used to classify the ROIs containing either CaOX, or CaOx clusters. We report classification accuracy results in terms ROC curve and the corresponding area under it.

Dense breast tissue has been shown to reduce the sensitivity and specificity of breast cancer detection. Estimation of breast density (BD) in digital breast tomosynthesis (DBT) may provide masking risk assessment in quasi three dimensions (3D). Dual energy (DE) mammography has been used for breast tissue decomposition, which may provide more accurate BD map for DBT projections. This study aims to investigate the feasibility of DE DBT for volumetric BD estimation and 3D masking risk assessment. A compressed digital breast phantom (with ground truth for BD) was simulated using a virtual clinical trial software (VICTRE). Low and high energy projection images of the phantom were simulated emulating the Siemens Mammomat Inspiration DBT system. DE decomposition was performed for each projection angle using a library of calibrated DE attenuation data for various combinations of breast thickness and glandularity. The resulted BD maps for all projection angles were used for simple-back-projection image reconstruction, and the dense regions for each image slice were segmented based on the reconstructed voxel values. This method was applied to clinical images from DE DBT acquisition after scatter correction. Our results show that DE decomposition provides consistent BD map for each projection angle and throughout the entire breast (including the periphery with thickness roll-off), and consistent measurements of the total breast volume, fibroglandular tissue volume and volumetric BD. The 3D segmented fibroglandular tissue map can be used to assess the masking risk for the DBT image volume.

Mammography and breast CT are important tools for breast cancer screening and diagnosis. Current implementations are limited by scattered radiation and/or spatial resolution. In this work, we propose and develop a slot scan-based system to be used in both mammography and CT mode that can limit scatter and collect sparse CT data for improved image quality at low radiation exposures. Monte Carlo simulations of an anthropomorphic breast phantom show a factor of 10 reduction in scattering amplitude with our slot scan-based system compared to that of a full-field detector mammography system (area mode). Similarly, slot-scan improved the MTF (particularly the low-frequency response) compared to an area detector. Investigation of sparse CT sampling with doubly sparse acquisition data return better quality reconstruction, for which our slot-scanning system is capable, over angle-only projection. Thus, a system with the combined ability for slot-scanning mammography and slot-scanning breast CT has the potential to deliver improved dose-efficient imaging performance and become viable breast cancer screening and diagnostic tools.

To reduce noise and enhance the contrast-to-noise ratio (CNR) of microcalcifications (MCs) in digital breast tomosynthesis (DBT), we conducted a study to investigate the feasibility of performing denoising on the projection views (PVs) using a deep convolutional neural network (DCNN) before reconstruction. We fine-tuned a modularized adaptive processing neural network (MAPNN) based on a pretrained model for CT image denoising. Four phantom DBTs containing over 700 simulated MCs of 3 nominal sizes were scanned with a DBT system that acquired 9 PVs within a 25° scan angle at two dose level settings to form the training and validation sets. Nine human subject DBTs were used as an independent test set. We trained the DCNN with low dose PVs as the input and the corresponding high dose PVs as the reference. We marked the MCs in the PVs and designed a loss function for DCNN training that balanced the effect of noise reduction and signal preservation. The loss function was a weighted sum of the perceptual loss, the adversarial loss and the CNR loss. A visual comparison of the DBT volumes reconstructed from the denoised PVs indicated that the proposed method could reduce noise and preserve the texture of the background without blurring subtle MC signals. A quantitative comparison showed a significant CNR improvement (p < 0.0001) in both the validation phantom and the human subject DBTs.

The recent development of compact, cold cathode X-ray emitters presents the opportunity to use multiple emitters arranged in an array to perform medical imaging from a stationary source. This study considers some of the potential advantages in their application to digital breast tomosynthesis and breast specimen imaging. It presents modelling results from simulated images based on a validated toolkit that models the X-ray source, breast tissue and X-ray image receptor performance. It also presents results from physical experiments using flat panel source geometries. Stationary sources eliminate focal spot motion blur which can improve the visualization of small features such as microcalcifications. A rectangular array of emitters can provide enhanced depth resolution compared to a linear sweep of emitter positions, as the projection images are obtained over a ‘sweep’ in two directions. Moreover, a rectangular array gives the opportunity to reduce the source to image distance (SID). Halving this distance reduces the required beam current by four times, which could significantly reduce the size, weight, and cost of an overall system, albeit with an increase in the angular width of the focal spot. Both the simulation and physical experiments show that depth resolution is considerably improved when using a rectangular array in comparison to a linear array. If a rectangular array is used at half the SID, and a quarter of the power, then this benefit is maintained, even though the focal spot has a larger angular width. This could provide enhanced imaging from lower-cost, smaller devices compared with conventional systems.

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost- effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target size were study variables, and the OSEM postfiltering was task-optimized based on count level. A psychometric analysis of observer performance for the selected task found that the HOTV algorithm allowed a two-fold reduction in dose compared to the optimized OSEM algorithm.

Most existing fully automatic or semi-automatic medical imaging segmentation methods start from reconstructed images. However, a framework for joint segmentation and image reconstruction can be beneficial because both tasks can be mutually dependent. Better segmentation can improve image reconstruction and vice-versa. We propose to perform joint PET image reconstruction and fully automatic PET image segmentation, using the CT image from a PET-CT scanner as a given input. Within a unified framework, our proposed method generates a PET image and a segmentation mask utilizing two connected trained networks: 1) a network dedicated to denoising the PET image with boundary information from the segmentation network. While reconstructing the PET image, the algorithm exploits the denoised image recovered from the trained network. 2) a segmentation network dedicated to estimating the lesion and background (e.g., liver) masks using PET/CT information. A boundary indicator image is generated based on the gradients of segmentation masks. We simulated extremely low-count PET, typical for Y-90 imaging, where traditional segmentation and reconstruction methods tend to perform poorly. For PET reconstruction, proposed method using true boundary improves CNR (RMSE) by 28.9 % (49.1%) and 16.8 % (13.2%) compared to EM and proposed method without using boundary. For multi-modal segmentation, our proposed method improved global Dice score in tumor by 70.6% compared to our proposed segmentation framework using only CT information.

Medical image reconstruction is often an ill-posed inverse problem. In order to address such ill-posed inverse problems, prior knowledge of the sought after object property is usually incorporated by means of regularization. For example, sparsity-promoting regularization in a suitable transform domain is widely used to reconstruct images with diagnostic quality from noisy and/or incomplete medical data. However, sparsity-promoting regularization may not be able to comprehensively describe the actual prior information of the objects being imaged. Deep generative models, such as generative adversarial networks (GANs) have shown great promise in learning the underlying distribution of images. Prior distributions for images estimated using GANs have been employed as a means of regularization with impressive results in several linear inverse problems in computer vision that are also relevant to medical imaging. However, in practice, it can be difficult for a GAN to comprehensively describe prior distributions, which can potentially lead to a lack of fidelity between the reconstructed image and the observed data. Recently, an image-adaptive GAN-based reconstruction method (IAGAN) was proposed to guarantee stronger data consistency by adapting the trained generative model parameters to the observed measurements. In this work, for the first time, we apply the IAGAN method to reconstruct images from undersampled magnetic resonance imaging (MRI) measurements. A state-of-the-art GAN model called Progressive Growing of GANs (ProGAN) was trained on a large number of ground truth images from the NYU fastMRI dataset, and the learned generator was subsequently employed in the IAGAN framework to reconstruct high fidelity images from retrospectively undersampled experimental k-space data in the validation dataset. It is demonstrated that by use of the GAN-based reconstruction method with noisy and/or incomplete measurements, we can potentially recover fine structures in the object that are relevant for medical diagnosis that may be difficult to achieve using traditional reconstruction methods relying on sparsity-promoting penalties.

Structural MRI is the standard-of-care imaging modality for screening and diagnosis of most neurological conditions. However, the ability to reliably evaluate brain MRIs for disease characterization (whether by machines or experts) is often hampered by the presence of artifacts such as magnetic field inhomogeneity, aliasing, or patient motion; some of which may not be visually apparent. Reliable quality assessment of brain MRI scans would allow for excluding noisy acquisitions and reducing errors in subsequent downstream analyses. Since visual inspection is impractical for large volumes of data and subject to inter-observer variability, there is a need for accurate, automated Quality Assessment (QA) of MR images. Previous studies have investigated image quality metrics (IQMs) in order to quantify the effect of specific artifacts and quality degradation in an MR image. There has also been some recent work in developing machine learning models which use IQMs to enable robust QA across multiple sites. We build on this approach by leveraging a total of 64 IQMs (quantifying noise, artifacts, information, and general brain measurements) within a Dense Neural Network (DNN) model for QA of brain MRI scans using the publicly available multi-site ABIDE-I cohort (17 sites, 1102 subjects). In attempting to predict the "mean opinion score" of MR image quality (as assessed by expert radiologists), the DNN model yielded an accuracy of 87.3±2.1% in training (15 sites, 885 subjects) which generalized to a 79.5±1.9% accuracy in validation (2 sites, 246 subjects). This initial DNN model suggests the promise of deep learning to improve automated QA of MRI scans in large multi-site, multi-scanner cohorts.

Direct active matrix flat panel imagers (AMFPIs) using amorphous selenium (a-Se) offer high intrinsic spatial resolution but have limited x-ray quantum efficiency at general radiographic energies due to selenium’s low atomic number. Conversely, indirect AMFPIs using inorganic scintillators typically have superior x-ray quantum efficiency at these energies, but inferior spatial resolution and increased noise due to optical effects in the scintillator. These inherent limitations motivate alternative AMFPI designs to further improve detector xray sensitivity and signal-to-noise performance. Towards this goal, this work constructs and experimentally investigates the x-ray imaging performance of a novel direct-indirect prototype imager referred to as Hybrid AMFPI. The imager comprises a direct conversion a-Se layer that may be coupled to an interchangeable scintillator screen through a transparent blocking layer and bias electrode. In this direct-indirect “hybrid” configuration, a-Se serves as both an x-ray and optical sensor. Readout is performed by a thin-film transistor array with 85 μm pixel pitch. The prototype imager’s x-ray sensitivity, modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE) are measured in a direct AMFPI configuration (i.e. a-Se alone) and in a Hybrid configuration under identical x-ray exposure conditions and the results are compared. Contrast-detail and spatial resolution phantoms are also imaged using direct, Hybrid and indirect AMFPI configurations under identical exposure conditions to evaluate differences in their imaging performance.

A new imaging x-ray detector with 194μm pixel conventional FPD imaging mode for large area visualization with detector field-of-views (FOV) ranging from 12 inch to 6 inch and a 76μm pixel high resolution high-definition (Hi-Def) zoom mode for smaller region-of-interest (ROI) visualization with FOV’s from 3 to 1.5 inches in one detector panel was built to improve visualization of endovascular devices during neurovascular interventions. In this work we investigate the effect of simultaneous use of both imaging modes on entrance kerma area product (KAP) and scatter at the detector. The entrance KAP and the scatter reaching the detector during simultaneous acquisition is the same as the large FOV FPD mode acquisitions, both higher than just Hi-Def mode acquisition. When a 0.7mm thick differential ROI Cu attenuator with a circular 10mm hole was aligned to the Hi-Def detector area, the entrance air kerma is reduced by 70%. To study the effect of scatter on image quality, Hi-Def images of the devices such as guide catheter and stents placed on a head equivalent phantom were acquired with the x-ray field opened to 8inch FOV, with and without the grid. Compared to the 3 inch Hi-Def FOV, with the grid the contrast of the guide catheter tip was reduced by 11% when the FOV was opened to 8 inch; however, without the grid the reduction in contrast was 36%. With the ROI attenuator and no grid the contrast was reduced by 16% when the FOV was opened to 8inch.

For a light burst generated by an x ray-scintillator interaction, the quasi-isotropic spreading of optical quanta determines that both the light collection efficiency and the spread function of the light burst depend on the depth (z) of interaction: the closer the initial position of light burst to the light sensor, the less light loss and spatial resolution degradation a detector system encounters. For scintillator-based indirect-conversion detectors, the variation of the x-ray conversion gain and the MTF along the z direction contributes to a significant fraction of DQE loss and is known as the Lubberts effect. Understanding the physics behinds the effect and developing solutions to reduce the effect have led to tremendous improvements in the performance of indirect-conversion detectors. In contrast, for direct-conversion photon counting detectors (PCDs), since no optical quanta generation or transport is involved, there is no classical Lubberts effect. However, the transport properties of the generated charge carriers in semiconductors do depend on their travel distance. As a result, the performance of direct conversion detectors may depend on z. This dependence can be considered as a counterpart of the classical Lubberts effect. The purpose of this work is to analyze the contributions of a variety of physical mechanisms that may contribute to a Lubberts-like effect in direct-conversion PCDs. The results show that the Lubberts-like effect is primarily caused by the depth-dependent charge trapping and recombination likelihood. Based upon the physics analysis, PCD electrode design can be optimized to minimize the Lubberts-like effect and the associate DQE loss.

For active matrix flat-panel imagers (AMFPIs), relatively high levels of additive noise compared to the average imaging signal per interacting x-ray limits DQE under conditions of low dose per frame – degrading imaging performance in applications such as digital breast tomosynthesis. The development of particle-in-binder, polycrystalline mercuric iodide (PIB HgI2) as an alternative converter is of interest since the material can provide significantly larger imaging signal per x-ray than converters based on CsI:Tl or a-Se. In order to address the high degree of charge trapping exhibited by HgI2 (leading to unacceptably large image lag), incorporation of a Frisch grid structure into the bulk of the converter is under investigation. Earlier theoretical studies demonstrated that such a grid can greatly reduce the contribution of hole charge to imaging signal – which should reduce the effects of charge trapping. In those studies, the grid was assumed to be positioned between two polycrystalline HgI2 layers – a configuration referred to as a floating grid. In this paper, early results from a theoretical examination of the performance of converters containing a grid supported on insulating pillars over an underlying AMFPI array and a single layer of PIB HgI2 material, are reported. The simulation results indicate that, while charge accumulating on the pillars changes the shape of electric fields that allow collection of electron signal, high electron collection efficiency accompanied by substantial suppression of hole signal can nevertheless be maintained through judicious selection of grid design and operational conditions. This work is supported by NIH grant R01- EB022028.

Cassette type digital radiography detectors consisting of indirect conversion flat panel detectors (FPDs) find extensive applications in general radiography. Recently, these detectors are being employed not only for conventional hospital and clinical use but also in the field of home medical care. Therefore, image quality improvement, X-ray dose reduction, and weight reduction are essential for such detectors. To achieve these goals, we have developed novel FPDs with high image quality and light weight by combining the irradiation side sampling (ISS) system technology and flexible photodiode and thin film transistor (PD-TFT) technology. The ISS system enables effective detection of light emitted from a scintillator near the photodiode, resulting in higher sensitivity and resolution than those obtained with common penetration side sampling (PSS) systems. In addition, by employing a 40-μm flexible polyimide film as the PD-TFT array substrate instead of a conventional 0.5-mm rigid glass, the X-ray attenuation and scattering at the substrate are expected to be reduced, resulting in further improvements in sensitivity and resolution. We have developed two types of ISS system flexible FPDs with Gd₂O₂S:Tb (GOS) and CsI as scintillators. These FPDs realized higher sensitivities, modulation transfer functions, and detective quantum efficiencies than the conventional ISS system rigid FPDs for both the scintillators. In addition, we achieved approximately 35% weight reduction with the flexible FPDs compared to similarly sized conventional rigid FPDs.

Microcolumnar CsI:Tl scintillator screens have been the gold standard in X-ray imaging for many years due to their high density, high atomic number, and scintillation efficiency. The structured screens provide an improvement in performance by channeling the light to the detector, improving detection efficiency and spatial resolution. We have taken this concept a step further by laser-machining the CsI:Tl scintillator to provide pixels that match the detector pixels. This allows for still thicker CsI:Tl layers up to 700 μm pixelated with pitch of 100 μm to match CMOS flat panel pixels, thus improving X-ray absorption and resolution. We are investigating the applications of CMOS detectors with pixelated scintillators for imaging of bone microarchitecture on diagnostic Cone Beam CT (CBCT) systems to provide improved quantitative metrics for diagnosis of osteoporosis and osteoarthritis.

The scintillator design includes reflective coatings applied to the laser-cut grooves to improve optical isolation between pixels. Such coatings are created by atomic layer deposition (ALD), a unique approach, which permits formation of reflectors over inter-pixel grooves with aspect ratios as high as 140:1. Here we present initial results quantifying performance gains in CMOS detector resolution and their impact on the quality of bone microstructure segmentation. We demonstrate 77% gain in spatial resolution at 2 lp/mm and extension of the limiting resolution from 3 lp/mm to 4.5 lp/mm for the CMOS detector with a pixelated screen compared to a commercial sensor. In a bench-top CBCT study emulating diagnostic systems for orthopedic applications (extremity CBCT), we achieved >0.75 correlations in metrics of trabecular microarchitecture between pixelated CsI:Tl based CBCT and gold-standard micro-CT. The pixelated scintillator is expected to have significant impact for many other applications including mammography and digital radiography, where resolution and dose efficiency (DQE) of the detector are of critical importance.

This Conference Presentation, "Impacts of pulse pileup, charge sharing, and anti-charge sharing on the noise power spectra of photon counting detectors: theoretical analysis and experimental demonstrations," was recorded at Medical Imaging 2020 held in Houston, Texas, United States.

Iodinated contrast agent is frequently used in computed tomography (CT) imaging to enhance organ contrast enhancement and improve diagnostic sensitivity. Despite this importance, there currently is a lack of standardization in contrast administration protocol across institutions, leading to many safety and clinical diagnostic risks. To solve this, we built three liver contrast enhancement/perfusion models: two using simple linear regression and another by combining a pre-existing pharmacokinetics mathematical model with clinical data with the eventual goal of individualizing contrast administration protocol to optimize contrast-enhanced CT imaging for each patient. These models primarily use patient attributes, such as height, weight, sex, age and contrast administration information, and bolus tracking information to make such predictions. 418 Chest/Abdomen/Pelvis CT scans were used in this study. 75% of cases were used to train these models and the rest were used to test the prediction accuracy. Pearson’s correlation coefficient test was used to find the correlations between the patient attributes and contrast enhancement in liver parenchyma. Weight, height, BMI, and lean body mass were found to be statistically significant predictors for contrast enhancement (P<0.05), with weight as the strongest predictor. Of the predictive models, we found that including bolus tracking information increases predictive accuracy (r2=0.75 v. 0.42) and that in the absence of bolus tracking information, combining clinical data with pre-existing pharmacokinetics model may provide the needed enhancement curve.

In recent years, there has been an increasing interest in replacing digital subtraction angiography (DSA) as method of choice for the diagnostic imaging of patients suffering from lower extremity peripheral arterial disease (PAD). Due to small vessel diameters and suboptimal resolution, examinations of below-the-knee arteries however remain extremely challenging. The advent of wide beam CT scanners allows to perform multiple CT acquisitions over a wide patient volume. A sequence of these CT acquisitions at timed intervals could provide additional hemodynamic information, and as such allows to track a contrast bolus that propagates through the arterial conduit. The aim of this study was to evaluate the accuracy and precision of ow velocity measurements using time-resolved computed tomography angiography (CTA). To this end, we constructed a mechanical ow phantom (single lumen, 6 mm inner-diameter). Six consecutive time-resolved CTA acquisitions were performed at a constant ow rate to achieve six reference velocities (21.2 mm/s, 38.9 mm/s, 60.1 mm/s, 81.4 mm/s, 99.0 mm/s and 120.3 mm/s). The mean centerline ow velocity was obtained from the contrast propagation over three different segmental lengths (160 mm, 80 mm and 40 mm) and then compared to the reference ow velocity. The results of this study suggest that mean ow velocities within the range of typical blood ow velocities in the below-the-knee arteries (40 mm/s - 70 mm/s), can be accurately measured with high precision in a 6 mm ow phantom using time-resolved CTA when considering a minimal path length of 80 mm.

Catheter-based vascular interventions performed in the interventional radiology suite rely heavily on digital subtraction angiography (DSA) for intraprocedural evaluation of vessel morphology. Recently, DSA methods have been reported which quantify blood flow based on the tracking of temporal oscillations in contrast agent density through vessels. This signal, termed contrast pulsatility, arises naturally from the mixing of a fixed rate contrast injection with periodic arterial blood flow. Although these tools promise quantitative intraprocedural evaluation of flow, quantification is limited by a decline in contrast pulsatility strength in distal arterial vasculature, and, in venous structures, minimally observable or no pulsatility. To improve pulsatility strength in DSA images, a custom dual-barrel power injector was constructed that dynamically modulates the injected contrast flow (or concentration). Each barrel can follow sinusoidal and square wave injection profiles of specified frequency, amplitude, phase, and duty cycle. Improvement in contrast pulsatility was evaluated in a vessel phantom with simulated 60 bpm arterial flow and low-frequency sinusoidal contrast injections of 0.25, 0.50, and 1.0Hz. The ability to establish contrast pulsatility in a venous structure was evaluated in a porcine study. In the arterial phantom, sinusoidal contrast injections at 0.25Hz, 0.5Hz, and 1.0Hz displayed increased distal contrast pulsatility relative to a DC contrast injection at the same mean rate, with energy spectral density (ESD) ratios of 215, 59.9, and 5.3 respectively. In the animal study, a custom 0.67Hz square wave injection into the inferior vena cava showed a large improvement in pulsatility relative to DC injection (ESD ratio of 7208).

Quantitative perfusion maps of cerebral blood flow, cerebral blood volume, and transit time generated using dynamic imaging data enable physicians to evaluate and prescribe the optimal plan of care for stroke patients. Validation is needed to increase the accuracy and reproducibility of this data, which can vary depending on the scanning technique and post-processing algorithm. In this work, we expand the XCAT brain phantom to incorporate a realistic model of the contrast agent dynamics in the cerebral vasculature and establish the ground truth to which the perfusion maps can be compared. For a specific stroke case, each tissue region's flow demand is calculated and used to determine the feeding flow in the upstream arteries and draining flow in the downstream veins. Using the flow values and a contrast agent injection curve, the model calculates the input and output concentration curves for each structure in the brain. The concentration curve within each structure is then calculated as the difference between the total amount of contrast agent that has entered and exited the structure up until that timepoint. A dynamic simulation framework utilizes the curves to define the contrast agent concentration within the phantom at each time point and generates simulated CT perfusion imaging data sets compatible with commercially available post-processing software. This development provides a realistic set of ground truth test data that enables quantitative validation and optimization of perfusion imaging and post-processing methods for stroke assessment.

Quantitative estimation of contrast agent concentration is made possible by spectral CT and material decomposition. There are several approaches to modulate the sensitivity of the imaging system to obtain the different spectral channels required for decomposition. Spectral CT technologies that enable this varied sensitivity include source kV-switching, dual-layer detectors, and source-side filtering (e.g., tiled spatial-spectral filters). In this work, we use an advanced physical model to simulate these three spectral CT strategies as well as hybrid acquisitions using all combinations of two or three strategies. We apply model-based material decomposition to a water-iodine phantom with iodine concentrations from 0.1 to 5.0 mg/mL. We present bias-noise plots for the different combinations of spectral techniques and show that combined approaches permit diversity in spectral sensitivity and improve low concentration imaging performance relative to the those strategies applied individually. Better ability to estimate low concentrations of contrast agent has the potential to reduce risks associated with contrast administration (by lowering dosage) or to extend imaging applications into targets with much lower uptake.

By integrating three absorption gratings with a medical X-ray tube and a medical flat panel detector, a preclinical X-ray multi-contrast lung imaging prototype with a large field-of-view up to 40 × 50 cm² was developed in this work. The prototype system provides phase-contrast and dark-field images simultaneously in addition to the conventional X-ray absorption imaging. Unlike the traditional grating-based phase-contrast imaging (GPCI) system, the scanning object is placed behind the pattern-generating grating G1, reducing the radiation dose to probably half of the original system design. For a fast scan process, three absorption gratings inducing a large field-of-view were utilized. Besides, the X-ray tube, the three gratings and the detector are all fixed on the same mechanical arm, scanning the object along the vertical direction. A numerical simulation platform was developed to help optimize the imaging parameters. Finally, the preliminary experimental results of a full-body enthanized rabbit demonstrate the capability of the multi-contrast prototype system, and its dark-field images are capable of providing additional diagnostic value for the diagnosis of lung diseases. Direct evaluation of the potential clinical unity of the multi-contrast lung imaging can be further performed on this prototype system.

Three-material decomposition is crucial for material quantification when more than two elemental materials, including a K-edge material, are presented in an image object. In principle, three-material decomposition requires a triple energy scan which cannot be directly accomplished using a conventional dual energy CT system. In this work, a new scheme to enable three-material decomposition by employing phase contrast CT was presented. When a grating interferometer is added, a conventional absorption dual energy CT system can be upgraded to a phase contrast dual energy CT system which provides an additional phase signal related to the real part of the refractive index of an image object, along with the absorption signal under two different x-ray spectra. In this work, a three-material decomposition method was proposed for the aforementioned dual energy phase contrast CT system. Physical experimental studies were performed on a benchtop x-ray Talbot-Lau interferometer system to validate the proposed method. A physical phantom, containing calcium and iodine inserts of known concentrations, was used as the image object. A rotation-rotation dual energy phase contrast CT scan was performed under 40 and 80 kVp tube potentials. For each view angle, a phase stepping procedure with five phase steps was performed. After the phase retrieval procedure and image reconstruction using the standard filtered-back projection, the solutions were decomposed into the calcium, iodine and water bases based on the proposed decomposition method. For all the solutions, the relative quantification errors of the concentrations were within 10%.

X-ray phase contrast imaging has been proposed as a way to improve contrast in soft tissue. Phase retrieval methods have been developed which only require a single image acquisition, allowing phase contrast imaging to be used in combination with computed tomography without a dangerously large dose applied to the subject. Many of the existing methods use approximations regarding the knowledge of object properties as well as make assumptions such as a constant ratio of absorption and phase properties throughout the object.¹ Our group has previously developed a phase retrieval algorithm which only requires a single image when using energy-resolving photon counting detectors and uses no additional approximations regarding object properties. This allows us to decompose phase properties even if the object is composed of a large number of materials with very close material properties. Our recent results on simple phantoms show that we can accurately retrieve multiple material properties corresponding to absorption and phase effects for a range of different materials. The strength of our spectral decomposition method is compounded by the use of photon counting detectors with zero dark noise. In this paper we demonstrate the first propagation-based phase contrast tomography study of a biological sample using our method. Our phase retrieval method yields excellent results with very low counts - 1 second or less per projection acquisitions using a weak microfocal X-Ray tube. We show this via known phantoms as well as satisfactory preliminary results from biological samples. Additional studies will explore dose aspects using mouse models in our benchtop micro computed tomography platform and compare against attenuation-based computed tomography.

Studies have shown that the conventionally estimated visibility and differential phase signals in grating-based Talbot-Lau imaging system are intrinsically biased signals. Since such bias are mainly caused by applying the conventional signal estimation approach on noisy data, therefore, it remains an open question whether there has a better signal estimation method to reduce such bias. To answer this question, we proposed an end-to-end supervised deep computed signal estimation network (XP-NET) to extract the three unknown signals, i.e., the absorption, the dark-field, and the phase contrast. Numerical phase stepping data generated from natural images are utilized to train the network. Afterwards, both numerical and experimental studies are performed to validate the performance of the proposed XP-NET method. Results show that for high radiation dose levels, signals retrieved from the XP-NET method are identical as obtained from the conventional analytical method. However, the XP-NET method has the capability of reducing phase signal bias by as much as 15% when the radiation dose levels gets lower. As the phase signal becomes more unbiased, the phase images get more accurate.

This work investigates a metal artifact reduction technique that combines weighted masking of projection data corrupted by metal, improved photon-counting detector modeling, and the constrained ‘one-step’ spectral CT image reconstruction (cOSSCIR) algorithm. The cOSSCIR algorithm directly estimates the basis material maps from the photon counts data using an optimization algorithm that places constraints on the basis maps. The improved photon-counting detector spectral modeling improves the accuracy of the polyenergetic forward model, which is expected to reduce beam hardening artifacts due to metal. This study also explores weighting schemes to reduce the contribution of counts measurements corrupted by metal during reconstruction, including selective masking across energy window. Unlike two-step decomposition approaches, cOSSCIR does not require energy windows to be registered, thus enabling energy-selective masking of data corrupted by metal. Preliminary feasibility of the proposed methods was investigated through experimental photon-counting CT acquisition of a tissue specimen with metal inserts. The cOSSCIR algorithm estimated acrylic and aluminum basis maps which were combined to form a 50 keV effective monoenergetic image. The effective monoenergetic image reconstructed by cOSSCIR from all counts data demonstrated reduced streak and view aliasing artifacts compared to the reference filtered backprojection image. Weighting of the data corrupted by metal further reduced the remaining beam hardening artifacts, with weighted masking further reducing the streak artifacts.

Accurate quantitation of bone density variation would allow monitoring changes due to aging, disease progression, therapy outcomes or drug response. We present results from our material decomposition method using spectral computed tomography (CT) with a photon counting detector (PCD) for estimating quantitative bone density variations in healthy and arthritic mice. Limited samples of healthy mice and collagen antibody-induced arthritis (CAIA) mice were imaged on our home-built benchtop micro CT system with a wide area photon counting detector with a high resolution 55 micrometer pixel size. A material decomposition algorithm was applied in the image domain to separate and quantify bone and soft tissue variations. The resulting bone densities were quantified globally and locally for both mice. Higher bone densities were seen in the control mice over their arthritis counterparts by about 6-12%. Based on our preliminary results, material decomposition of multi-energy CT images collected with a PCD appears promising for bone density quantification. Our results show that these methods could be successfully applied to arthritis diagnosis and treatment monitoring in a mouse model. We will further investigate the utility of our methods to simultaneously quantify bone density as well as tagged contrast agents. These investigations are expected to help with longitudinal imaging studies to understand disease progression in animal models.

The introduction of photon-counting detectors in x-ray computed tomography raises the question of how reconstruction algorithms should be adapted to photon-counting measurement data. The transition from energyintegrating to photon-counting detectors introduces new effects into the data model, such as pure Poisson statistics and increased cross talk between detector pixels, (e.g. due to charge sharing), but it is still not known in detail how these effects can be treated accurately by the reconstruction algorithm. In this work, we propose a new reconstruction method based on penalized-likelihood reconstruction that incorporates these effects. By starting from a simple, easily-solved reconstruction problem and adding correction terms for the additional physical effects, we obtain a series expansion for the solution to the image reconstruction problem. This approach serves the twofold purpose of (1) yielding a new, potentially faster method of incorporating complex detector models in the reconstruction process and (2) providing insight into the impact of the non-ideal physical effects on the reconstructed image. We investigate the potential for reconstructing images from simulated photon-counting energy-resolving CT data with the new algorithm by including correction terms representing pure Poisson statistics and interpixel cross talk; and we investigate the impact of these physical effects on the reconstructed images. Results indicate that using two correction terms gives good agreement with the converged solution, suggesting that the new method is feasible in practice. This new approach to image reconstruction can help in developing improved reconstruction algorithms for photon-counting CT.

There is potential to improve CT imaging by adding spectral capabilities as given by photon counting detectors (PCD). Here we describe and assess performance of a new spectral micro-CT prototype system using a CdTe-based PCD with 100-μm pixel size (model XC-Thor, made by Direct Conversion) benefitting from anti-coincidence corrections. To assess the PCD in terms of spectral response, a Ba-133 nuclear source was scanned using full spectrum scanning by sweeping thresholds with 2 keV increments. In a different experiment, we used small vials containing water, iodine (I), gadolinium (Gd), and gold (Au) placed on the surface of the detector and acquired X-ray data in full spectrum mode to verify that the PCD threshold positions yielded the expected changes in contrast around the K edges of these elements. Detector performance was assessed using micro-CT phantoms and during cancer experiments in mice injected with nanoparticle (NP)-based contrast agents. Both phantom and mouse micro-CT data were reconstructed using our iterative, multi-channel algorithm based on the split Bregman method and regularization with rank-sparse kernel regression. A post-reconstruction decomposition method was used. The system is capable of high resolution (11.9 lp/mm, 10% MTF) tomographic imaging. Despite the anti-coincidence corrections, the spectral performance of the PCD is, however, not perfect, and it seems to be affected mostly at lower keVs, making accurate iodine decomposition challenging. Our cancer imaging results illustrate that our spectral micro-CT can benefit both nanotechnology and cancer research by providing an imaging method that can help test/optimize various nanoparticle for theranostics.

Virtual non-contrast (VNC) CT images derived from multi-energy CT data have demonstrated many valuable clinical applications. However, VNC CT has not yet established itself as a technology that can reliably replace the true non-contrast CT. One commonly observed phenomenon is an erroneous removal or reduction of calcium signal in VNC images. The purpose of this work is to develop a photon counting CT-based method to decouple the iodine signal from the calcium signal to achieve VNC CT imaging. Thanks to the energy resolving capability of the photon counting detector (PCD), a photon counting CT enables all single kV CT data to carry additional spectral information needed for VNC CT reconstruction. However, the energy discriminating capability of a real PCD system is far from ideal, which can severely degrade the fidelity of the encoded spectral information and the efficiency of the material decomposition. In this work, a physics-based model of the PCD energy response function was developed and experimentally validated. By leveraging this model, a method was developed to correct the distorted spectral information in the measured PCD energy bin data, allowing the true post image object spectrum to be estimated to accomplish accurate three-material decomposition and VNC CT reconstruction. Both numerical simulation and experimental results demonstrated that the proposed spectral distortion correction method can effectively improve the CT number accuracy of both iodine-containing vessels and calcium-containing bony structures in VNC CT images.

Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete dataset for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved from the initial 0.2 frame per second (fps) to 1 fps which indicated a factor of five improvement in temporal resolution. In this paper, a new technique was introduced to further improve temporal resolution from 1.0 fps to about 7.5 fps which indicates a factor of more than 30 times improvement in temporal resolution for slow C-arm CBCT acquistions. This new technique was developed for multi-sweep data acquistion protocol and it is referred to as enhanced SMART-RECON (eSMART-RECON) in this paper. Numerical phantoms with ground truth and in vivo human subject data were used to validate the proposed eSMART-RECON method.

Interventional guidance aims at providing the radiologist with detailed information about the location and orientation of interventional tools such as guide wires and stents. Most commonly, this is done by acquiring fluoroscopic images using an interventional C-arm system. Due to its projective nature, fluoroscopy is restricted to provide information from two spatial dimensions, preventing an exact 3D localization of the interventional tools. Analogous to computed tomography for diagnostic imaging, four-dimensional (three spatial dimensions plus the temporal dimension) interventional guidance has the potential to drastically improve both the speed and accuracy of such interventions, but is currently impractical due to the excessively high dose that would be necessary for continuous cone-beam CT (CBCT) scanning at high frame rates.

In this work we develop a novel deep learning-based approach to reconstruct interventional tools from only four x-ray projections. We train and test this deep tool reconstruction (DTR) network on simulated data. Only small deviations from the ground truth (GT) reconstruction of the tools were observed, both quantitatively and qualitatively, showing that deep learning-based four-dimensional interventional guidance has the potential to overcome the drawbacks of conventional interventional guidance in the future.

Purpose: Cone-beam CT is increasingly used for 3D guidance in interventional radiology (IR), but long image acquisition time results in degradation from complex deformable motion of soft-tissue structures. Deformable motion compensation with multi-region autofocus optimization was shown to improve image quality. However, the high dimensionality and non-convexity of the optimization problem challenge its convergence. This work presents preliminary development and early results obtained from an automatic learning-based decision framework to obtain local estimates of basic properties of the deformable motion field, coupled to a preconditioning strategy to simplify the optimization. Methods: Deformable motion properties are estimated with a deep convolutional neural network (CNN) consisting of a concatenation of custom-designed residual blocks. The preliminary design provided an estimate of the local motion amplitude on an 8x8 grid covering an axial slice of a motion-contaminated CBCT volume. The decision framework is coupled to a preconditioning strategy that effectively favors more likely solutions through motion amplitude-driven spatially-varying regularization of the motion trajectory and spatially varying selection of the search range for the optimization problem. The network was trained on simulated data generated from publicly available CT datasets, including simple motion fields. Results: Predictions of local motion amplitude showed good agreement with the true values, with root mean squared error (RMSE) < 10 mm for the complete range of motion distributions explored (sufficient for the intended purpose of initialization). Combination of amplitude prediction with spatially varying regularization and search range setting resulted in improved motion compensation after 1000 iterations of the preconditioned multi-motion autofocus in an example case with complex deformable motion. Extensive validation in a large dataset of complex, multi-motion patterns is underway. Conclusion: The proposed approach shows promising initial results and the potential for automatic local motion estimation with learning-based methods. Pending ongoing development to extend this initial development, the method could simplify and accelerate complex deformable motion compensation with spatially varying preconditioning of the motion estimation.

Model-based iterative CT reconstruction suffers from its high computational cost. To increase the reconstruction speed, algorithms using a momentum acceleration can be combined with the ordered subset approach. As shown in previous works, the incorporation of ordered subsets strongly improves the reconstruction time but makes the outcome of the reconstruction difficult to predict due to its empirical nature. Specifically, when the number of subsets is too high, divergence or convergence to an unsatisfactory result can easily appear. In this work, we propose a new combination of ordered subsets with momentum to achieve fast reconstruction in a more robust manner. Our approach, referred to as EOS-MFISTA, is an Efficient Ordered Subset strategy based on the steps of MFISTA, a monotonic version of FISTA. In short, EOS-MFISTA uses objective function values to decide on the next update step and does this with little increase in computational effort. The performance of EOS-MFISTA is evaluated on real CT data of a head phantom and a human chest. Starting from a zero image, 200 iterations of EOS-MFISTA are calculated and compared with other algorithms. Quantitative analyses based on the RMSE show that EOS-MFISTA is much more robust than OS-FISTA, especially when the number of subsets is increased to accelerate reconstruction. Visual inspection of results obtained with 50 iterations further supports these findings.

We report on the modeling, characterization, benchmarking, and optimization of an interventional cone beam CT system based on a dual layer X-ray detector by means of physics based simulations.

By Monte Carlo methods, we log the interaction and dose deposition (i.e. signal generation) of X-ray photons in the dual layer geometry, including scattering processes and fluorescence photon emission. From the spatial dose distribution inside the detection volume, we derive typical detector properties like X-ray spectral responses, detective quantum efficiencies 𝐷𝑄𝐸(0), and noise characteristics for particular detector layouts.

We apply these results in subsequent full system simulations to generate 3D imaging scans of dual layer spectral projections, for custom virtual phantoms containing inserts of e.g. blood sediment or iodine with different concentrations. These simulated images are used to calculate key performance indicators of the imaging system, like e.g. receiver operating characteristic based analysis of material separation capabilities.

We present a new concept of full-scale cone-beam computed tomography (CT) spectral imaging with simultaneous scatter correction capability using a spectral modulator and flying focal spot technology. The design of the spectral modulator and the utilization of flying focal spot are investigated, revealing the feasibility of using the flying focal spot to improve the spectral data sampling for a stationary modulator. Based on our analytical analysis, with the flying focal spot, the spectral projection ray misalignment can be reduced by a factor of more than 8. For modulator design, common materials such as Cu, ZnSe, Mo, Ag and Sn could be good candidates for better energy separation at a given attenuation of photon flux. A proof-of-concept study of a Catphan600 phantom and an anthropomorphic chest phantom was conducted on a tabletop cone-beam CT system using a copper modulator and by moving the X-ray tube position by 1.24 mm in the X and Z directions to mimic the flying focal spot. We found the low and high energy rays were well aligned, while the scatter distribution remained constant between the flying focal spot positions.

Dual Energy CT is a modern imaging technique that is utilized in clinical practice to acquire spectral information for various diagnostic purposes including the identification, classification, and characterization of different liver lesions. It provides additional information that, when compared to the information available from conventional CT datasets, has the potential to benefit existing computer vision techniques by improving their accuracy and reliability. In order to evaluate the additional value of spectral versus conventional datasets when being used as input for machine learning algorithms, we implemented a weakly-supervised Convolutional Neural Network (CNN) that learns liver lesion localization and classification without pixel-level ground truth annotations. We evaluated the lesion classification (healthy, cyst, hypodense metastasis) and localization performance of the network for various conventional and spectral input datasets obtained from the same CT scan. The best results for lesion localization were found for the spectral datasets with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV virtual mono-energetic images, respectively, while lesion localization distances of 10.58 ± 17.65 mm were measured for the conventional dataset. In addition, the 40 keV virtual mono-energetic datasets achieved the highest overall lesion classification accuracy of 0.899 compared to 0.854 measured for the conventional datasets. The enhanced localization and classification results that we observed for spectral CT data demonstrates that combining machine-learning technology with spectral CT information may improve the clinical workflow as well as the diagnostic accuracy.

Energy-integrating-detector (EID)-based triple-beam multi-energy CT (TB-MECT) was recently implemented on a dual-source (DS) CT platform by mounting a z-axis split-filter (0.05 mm Au, 0.6 mm Sn) on one of the two tubes. The purpose of this work is to perform a feasibility animal study on this new MECT platform for a small bowel imaging task using two contrast agents, iodine and bismuth. Optimal triple-beam configurations, 70/Au140/Sn140 kV were determined in a phantom study for this task and applied in the animal study for best material decomposition imaging performance. The results demonstrated that the TB-MECT can successfully separate and quantify the two contrast agents from one single scan for the task of small bowel imaging.

Proton radiotherapy has the potential to provide clinically effective treatment and highly conformal dose delivery when the rapid dose falloff at the end of its proton-beam range is correctly aligned to the distal margin of the clinical target volume. However, in current clinical practice an additional 2-3.5% safety margin must be added to the proton range to account for uncertainties in the estimation of proton-beam range when using stopping-power ratios (SPRs) derived from single-energy CT scans. Several approaches have been proposed to estimate stopping power by using dual-energy CT (DECT) and have been shown through theoretical analysis to outperform single-energy CT (SECT) under the presence of tissue composition and density variations. Our lab previously proposed a joint statistical image reconstruction (JSIR) method built on a basis-vector model (BVM) tissue parameterization for SPR estimation, which was shown to perform comparatively better than other DECT image- and sinogram-domain decomposition approaches on simulated as well as experimental data. This comparison, however, assumed theoretical SPR values calculated from the samples’ known compositions and densities as ground truth and used the mean excitation energy and effective electron density from ICRU reports along with a simplified version of the Bethe-Bloch equation to determine SPR reference values. Furthermore, CT scans were acquired with an assumed ideal point source at a narrow beam collimation; thus, the signal formation assumed by our JSIR process neglected scatter and off-focal radiation. In this paper, we verify the accuracy of our method by comparing the SPR values derived from JSIR-BVM to direct measurements of relative SPR, as well as present a preliminary study on the impact of fan-beam scatter radiation on JSIR-BVM SPR prediction accuracy.

As medical imaging technologies continue to accelerate in complexity, application, and multiplicity of design choices and use features, they should ideally be evaluated and optimized through human clinical trials. However, such trials are often impossible and impractical. Virtual imaging trials (VITs) offer a solution allowing scientists to conduct “human” trials of emerging imaging concepts and technologies via quick and cost effective simulations. VIT gives researchers a new experimental paradigm to answer fundamental questions that can only be practically answered using the precise controls and known ground truth afforded in the virtual domain. VIT is possible through realistic and accurate modeling of a) the patient, b) the imaging system, and c) the image interpretation process. This paper discusses these topics and offers demonstration examples of select applications of VIT in CT imaging.

The purpose of this study was to develop, validate, and evaluate a method for measuring noise texture directly from patient CT images (i.e., “in vivo”). The method identifies target regions within patient scans which are least likely to have major contribution of patient anatomy, detrends these regions locally, and measures noise power spectrum (NPS) there using previously phantomvalidated techniques. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom at three phases of contrast enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120kV at 3 dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, I50) algorithms using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of 1) a measurement in parenchymal regions of anatomy-subtracted (i.e. “noise only”) scans, and 2) a measurement in the same region of “noise-free” (pre-noise-insertion) images. To assess the performance of the in vivo NPS, the accuracy and bias of NPS average frequency (f_avg), and integrated noise magnitude were reported across the simulated scan population representing 2 reconstruction algorithms, 3 kernels, 3 dose levels, and 3 liver vasculature-to-parenchyma contrast levels. Pearson and Spearman correlation coefficient pairs were 0.97 and 0.93, and 1.0 and 0.99 for f_avg and noise magnitude, respectively. Finally, the NPS estimation method was further deployed on clinical cases to assess the feasibility of clinical analysis.

X-ray fluorescence tomography (XFCT) using nanoparticles (NPs) as contrast agents shows promise for high-spatial- resolution 3D molecular imaging. The technique has been demonstrated experimentally on phantoms as well as in small animals, i.e., mice. Parallel to experimental development, simulations play a key role in investigating the performance of existing and proposed XFCT arrangements. Up until recently, however, realistic simulations of small-animal XFCT have been unavailable due to the lack of appropriate, accurate and accelerated Monte Carlo (MC) based tools.

We have developed the tools necessary to simulate highly realistic small-animal XFCT. Our MC tool, XRFGPU is based on the accelerated open-source MC-GPU and allows simulations with speeds >1000x faster than other existing MC codes capable of similar simulations. Using available high-resolution digital mouse phantoms, we can predict the performance of any XFCT imaging arrangement and produce the realistic data needed for future development of small-animal XFCT. Here we validate the simulations against our laboratory arrangement, capable of 200 μm spatial-resolution XFCT of mice. Comparisons using a phantom confirms that the simulations are accurate. Lastly, we compare simulated images with images acquired in vivo using our laboratory arrangement showing how simulations can be used to enhance the interpretation of experimental data.

Training of deep learning-based segmentation algorithms requires large datasets of annotated images. Obtaining large clinical datasets, particularly for interventional imaging, is difficult and annotating vessels manually in the images is very time-consuming. Simulated images can be used to supplement clinical datasets. We developed a technique for the simulation of realistic, anatomically- and physiologically-motivated hepatic artery trees and subsequent synthesis of fluoroscopic images. The proposed approach creates a network of main feeding arteries for each of the eight liver segments as defined by the Couinaud classification system. A constrained constructive optimization based approach was then used to connect a set of randomly generated endpoints within each segment to the corresponding feeding artery. Vessel curvature was created using cubic splines and the generated vasculature was inserted into the digital XCAT phantom. The simulated 2D fluoroscopic images were generated using ray tracing and included focal spot blur, detector blur and Poisson noise. The length ratio (1.1 ± 1.7) and two parameters from Murray’s law, branching angles (7.9 ± 7.7^𝑜 mean absolute difference from Murray’s law) and radius ratio (1.0 ± 0.1) of the generated vasculature were in accordance with values reported in literature (1.3, Murray’s law applied to branching angles, and 1.0 respectively). Simulated vasculature included main branches for each of the eight Couinaud segments, where 87% of all connected endpoints terminated in the same segment. Simulated 2D projection images were analyzed using a vessel phantom study with contrast-enhanced tubes (0.305-3.353 mm diameter). The normalized root mean squared difference between the measured and simulated vessel profiles averaged 3.5%. In conclusion, the proposed method provides realistic simulated fluoroscopic images of the liver vasculature and could prove useful for the training of machine learning based algorithms for vessel segmentation.

Coronary artery disease (CAD) is one of the leading causes of death world-wide. Because of its great clinical importance, technological and diagnostic advances occur to combat it at a similarly high rate. With the number of clinical trials that would be necessary, this becomes infeasible and virtual clinical trials (VCT) are necessary. These require virtual patients and virtual pathologies. A generative adversarial network (GAN) was used to create a library of coronary plaques which were physiologically validated with finite element analysis. The resulting plaque library consists of a large number of realistic, variable virtual pathologies for use in VCTs.

In photon-counting CT, detector energy thresholds directly affect image quality attributes such as contrast and noise. The purpose of this study was to identify optimum energy thresholds using a comprehensive virtual clinical trial platform. The virtual trial was done using a computational, anthropomorphic phantom and a photon-counting CT simulator. The phantom (adult male, 50^th percentile body mass index) was chosen from the library of extended cardiac-torso (XCAT) phantoms. A vessel growth algorithm was used to model detailed hepatic arteries within the liver. Five computational lesions were inserted to the liver. The phantom was “imaged” with iodinated contrast at three contrast phases, i.e. arterial, portal venous, and delayed phases. All virtual sinograms were simulated using a validated CT simulator (DukeSim) modeling a prototype photon-counting scanner (CounT, Siemens Healthcare). The simulator included an energy- and threshold-dependent detector model accounting for X-ray and electronic effects and noise. At each contrast phase, the phantom was imaged at 35 detector energy threshold combinations with five lower-energy thresholds (20-40 keV) and seven upper-energy thresholds (50-80 keV). For each scan, two “threshold” images (photons detected beyond a threshold) and one “bin” image (photons detected between the two thresholds) were acquired. Noise magnitude, image contrast, and contrast-to-noise ratio (CNR) were measured in the aorta and the liver lesions. Optimum energy thresholds were identified as those yielding higher CNRs. From the simulations, noise magnitude was found to increase and CNR decrease with the increase in energy thresholds. Keeping the low threshold constant, the noise decreased and CNR increased with the increase in the upper energy threshold. Therefore, our results suggest that a combination of a lower threshold at 20-30 keV and upper threshold at 80 keV maximize the image quality. This study demonstrates an application of a virtual trial platform to quantify and optimize the quality of photon-counting CT.

Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties of material basis volumes can be complex. For example, spatial resolution, noise, and cross-talk can depend on acquisition parameters, regularization, patient size, and anatomical target. In this work, we propose a set of prospective prediction tools for the generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, as well as noise correlation. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.

Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DECBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.

Photon-counting spectral computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among all channels. In addition, reconstruction of each energychannel image suffers photon count starving problem. To make full use of the correlation among different channels to suppress the data noise and enhance the tissue texture in reconstructing each energy-channel image, this paper proposed a tensor convolutional neural network (TCNN) architecture to learn a tissue-specific texture prior for PCCT reconstruction. Specifically, we first model the spatial texture prior information in each individual channel using a convolution neural network, and then extract the correlation information among different energy channels by merging the multi-channel networks. Finally, we integrate the TCNN as a prior into Bayesian reconstruction framework. To evaluate the tissue texture preserving performance of the proposed method for each channel, a vivid clinical phantom which can simulate the real tissue textures was employed. The improvement associated with TCNN is remarkable relative to simultaneous algebraic reconstruction technique (SART) and tensor dictionary learning (TDL) based reconstruction. The proposed method produced promising results in terms of not only preserving texture feature but also suppressing image noise in each channel. The proposed method outperforms the competing methods in both visual inspection and quantitative indexes of root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).

CT imaging is one of the primary diagnostic tools utilized in modern radiology departments. Current stateof- the-art spectral CT imaging systems have been implemented using advanced x-ray source and/or detector technologies that have enabled image objects to be rapidly scanned using two distinct x-ray spectra (i.e., different effective beam energies). In this paper, we study the possibility to extract the encoded spectral information from the measured data when a single polychromatic x-ray spectrum is used to acquire data using an energy integration detector. Based upon our physical analysis, a physics-based deep neural network architecture, termed the Deep Spectral Imaging Network, was trained to demonstrate the feasibility of achieving spectral CT imaging using an energy integration detector and a single-kV acquisition.

Dual-energy CT (DECT) has become increasingly popular in practice due to its unique capability of material differentiation. One typical implementation of DECT is to use fast kV switching acquisition technique, which rapidly alternates the X-ray tube voltage between two predetermined kVs in a frequent manner. However, usage of such technique may be limited in practice, as it typically requires sophisticated hardware of high cost and lacks of dose efficiency due to difficulty in tube current modulation. One possible solution is to reduce the frequency of voltage switching during acquisition. However, this alternative approach may potentially compromise the image quality, as it results in sparse measurements for both kVs. In this paper, we proposed a cascaded deep-learning reconstruction framework for sparse-view kV-switching DECT, where two deep convolutional neural networks were employed in the reconstruction, one completing the missing views in the sinogram space and the other improving image quality in the image space. We demonstrated the feasibility of proposed method using sparse-view kV-switching data simulated from rotate-rotate DECT scans with phantom and clinical data. Experimental results show that the proposed method on sparse-view kV-switching data achieve comparable image quality and quantitative accuracy as compared to traditional method on fully-sampled rotate-rotate data

Purpose: Chest tomosynthesis is an attractive alternative to computed tomography (CT) for lung nodule screening, but reductions in image quality caused by radiation scatter remains an important limitation. Conventional anti-scatter grids result in higher patient dose, and alternative approaches are needed. The purpose of this study was to validate a lowdose patient-specific approach to scatter correction for an upcoming human imaging study.

Method: A primary sampling device (PSD) was designed and scatter correction algorithm incorporated into an experimental stationary digital chest tomosynthesis (s-DCT) system for this study to directly compute scatter from the primary beam information. Phantom and an in-vivo porcine subject were imaged. Total scan time was measured and image quality evaluated.

Results: Comparison of reconstruction slice images from uncorrected and scatter-corrected projection images reveals improved image quality, with increased feature conspicuity. Each scan in the current setup required twelve seconds, in addition to one second for PSD retraction, for a total scan time of 25 seconds.

Conclusions: We have evaluated the prototype low-dose, patient-specific scatter correction methodology using phantom studies in preparation for a clinical trial. Incorporating only 5% of additional patient dose, the reconstruction slices exhibit increased visual conspicuity of anatomical features, with the primary drawback of increased total scan time. Though used for tomosynthesis, the technique can be easily translated to digital radiography in lieu of an anti-scattering grid.

Metal artifact remains a challenge in cone-beam CT images. Many two-pass metal artifact reduction methods have been proposed, which work fairly well, but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Furthermore, two-pass methods increase the total reconstruction time. In this study, a projection-based metal detection and correction method with a dual layer detector is investigated. The dual layer detector provides dual energy images with perfect temporal and spatial registration in each projection, which aid in the identification of metal. A simple phantom with metal wires (copper) and a needle (steel) is used to evaluate the projection-based metal artifact reduction method from a dual layer scan and compared with that of a single layer scan. Preliminary results showed enhanced ability to identify metal regions, leading to substantially reduced metal artifact in reconstructed images. In summary, an effective single-pass, projection-domain method using a dual layer detector has been demonstrated, and it is expected to be robust against truncation and motion.

Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories.

Metal artifacts are a well-known problem in computed tomography - particularly in interventional imaging where surgical tools and hardware are often found in the field-of-view. An increasing number of interventional imaging systems are capable of non-circular orbits providing one potential avenue to avoid metal artifacts entirely by careful design of the orbital trajectory. In this work, we propose a general design methodology to find complete data solution by applying Tuy’s condition for data completeness. That is, because metal implants effectively cause missing data in projections, we propose to find orbital designs that will not have missing data based on arbitrary placement of metal within the imaging field-of-view. We present the design process for these missing-data-free orbits and evaluate the orbital designs in simulation experiments. The resulting orbits are highly robust to metal objects and show greatly improved visualization of features that are ordinarily obscured.

Two-dimensional anti-scatter grids (2D-ASGs) have been developed to selectively capture scattered photons while preserving image signal in flat-panel cone-beam CT systems (CBCT). However, 2D-ASGs affect the response of detector elements underneath grid-septa, producing grid-line artifacts (GLA), which render traditional gain-and-offset corrections ineffective. GLA in the projection images lead to ring-artifacts in CBCT reconstructions, which undermine the improvements in image quality associated with 2D-ASGs. We propose a novel implementation of an exposuredependent gain-correction and notch-Fourier filtering of the projection data to minimize GLA-related ring-artifacts in CBCT. A pixel-by-pixel gain-factor was calculated by dividing the intensities of a flat-field image (with no-ASG) by the intensities of a flat-field image with added ASG, at different exposure levels. Exposure levels were modified using copper filtration of the x-ray beam at six-different thicknesses (0 to 2.5 mm, 0.5 mm increments). Exposure-dependent gain-factors were stored in a multidimensional array and pixel-by-pixel exposure response was characterized using nonlinear curve-fitting. The exposure-dependent gain-correction was applied to 215 projection images of a 14 cm water phantom using a cobalt-chrome 2D-ASG. Residual faint grid-lines were removed using a customized Fourier-notch filter prior to Parker-weighted FDK reconstruction. Traditional gain-and-offset correction produced severe ring-artifacts (i.e., σ = 833.64 HU) when compared to the exposure-dependent gain correction (i.e., σ = 76.16 HU). Additionally, Fouriernotch filtering improved CT number accuracy by 43 HU. Our results suggest that characterization of the exposuredependent response of GLA-affected pixels can minimize ring-artifacts and improve CT-number accuracy, thus eliminating some of the difficulties of 2D-ASG implementation in CBCT systems.

Compared to conventional X-ray transmission imaging techniques, imaging systems based on field-emissive cathode based conformal transmission imaging schemes have smaller volumes. This imaging system combines multiple point sources to complete very low dose imaging of the region of interest using a small cone beam, and finally obtain high quality images by image restoration methods. However, more precise system geometry is required by the system architecture of the new imaging scheme and the special image-forming method. If the geometric parameters that are inconsistent with the actual use are used in image restoration, it will cause the geometric distortion in the final restored image. In this work, a proposed geometric calibration applied to the new imaging scheme. First, we use a partial point source on the periphery of the array ray source to project the side ball phantom of the imaged object. Then, a cost function that associates the geometrical parameters with the degree to which the back-projections of the ball phantom in projections from different point sources is constructed. Finally, the particle swarm optimization (PSO) is used for minimizing the cost function, and the resultant value of the cost function is the actual geometric parameter. With this calibration method, transmission imaging and geometric calibration will be done simultaneously. Besides, this calibration method does not depend on a complex calibration phantom and is simple and effective. In addition, the PSO can accelerate the implementation of the algorithm. The effectiveness and accuracy of this calibration method are verified by two sets of simulation experiments.

To suppress artifacts in X-ray digital tomosynthesis, a method which combines 3D and 2D region growing was proposed. However, it could not extract small metals such as pins, due to the discontinuity of the metal in the 3D projection data. A novel method is proposed to separate the metal from the projection data by combining pattern matching between neighboring projection angles and interpolating image pixels of enlarged projection data for each projection angle. The proposed method was compared with the conventional method based on 3D region growing. In a phantom experiment, the proposed method reduced the artifacts close to the metals. At a distance within 1 mm from the metal object, the mean absolute error evaluated without metal artifact correction (No MAC), with the conventional and proposed methods were 461.2, 461.2, and 100.2, respectively, and an improvement of more than 78% was demonstrated. When the distance from the object was shorter, the artifact became more significant in the No MAC and conventional methods, so the effectiveness of the proposed method was higher. Applying a suitable artifact correction on the basis of metal extraction makes it possible to effectively reduce artifacts in DT images.

Digital tomosynthesis (DTS) has been used in diagnosis and radiation therapy due to its performance and benefits. DTS is able to provide 3D images with good depth resolution comparing to conventional radiography and reduce radiation dose comparing to computed tomography (CT). However, DTS scans with limited scan angles and a few projections lead to the insufficiency in data acquisition. Such a drawback causes the alteration of image characteristics and the distortion of image quality in DTS imaging. These issues can be magnified by the geometric variations of the DTS systems and the imaging strategies. In this study, the effect of geometric variations on image characteristics was evaluated by the DTS systems simulated with various X-ray source scan trajectories and angles. The DTS images were analyzed in terms of noise property, contrast-to-noise ratio (CNR), and spatial resolution. The results showed that the quality of DTS images was highly dependent on X-ray source scan trajectories and angles, and the characteristics of DTS images varied according to their acquisition geometries. Therefore, the geometries and strategies for DTS imaging should be appropriately determined for optimizing their systems and applications.

Digital chest tomosynthesis that provides a reconstructed 3D chest image is a superior technique to detect chest diseases. As it is difficult to detect diseases like lung cancer with conventional 2D digital chest X-ray technology (CXT), digital chest tomosynthesis improves upon the many of the limitations inherent in the 2D digital CXT. In this study, we report a digital chest tomosynthesis system (D-CTS) that can generate multi X-ray information for the reconstruction of a 3D Xray chest image. The D-CTS reported herein employs an array of carbon nanotube (CNT) emitter-based cold cathode electron-guns that are triggered in sequence to provide a gantry-less system (Figure 1). The CNTs are achieved by direct growth on a metal substrate and have a spaghetti-like structure (Figure 2) with fast response to electrical bias under vacuum conditions. Unlike conventional rotating type systems with gantries, our CTS has the advantage of less motion blur in image acquisition, given its stationary position. Additionally, the switching from one electron-gun (e-gun) to the next is much faster than the speed of conventional gantries, allowing faster acquisition time t required for digital operation. This system shows outstanding field emission property for taking X-ray images. The design, fabrication process and imaging processing of the multi-beam CNT X-ray system will be discussed during the presentation.

Fluorescence molecular imaging is a promising tool for molecular tracking, and thus can visualize the vascular structure of small animal. However, with the strong scattering in biological tissues, traditional fluorescence molecular imaging in the visible spectrum or the first near-infrared spectrum (NIR-I, 700-900 nm) has a limitation for high-resolution vascular imaging. Recently, the novel in vivo fluorescence molecular imaging in the longer second near-infrared window (NIR-IIb, 1500-1700 nm) is successfully developed for small animal imaging. The NIR-IIb window affords high imaging resolution and deep tissue penetration because of the diminished photon scattering effect, which is suitable for vascular imaging. However, the clinical applications of NIR-IIb fluorescence molecular imaging have been severely limited for lack of the clinical fluorophores. Here, we show that the clinically available dye, indocyanine green (ICG), can also emit the NIR-IIb signal, which provides high-resolution vascular imaging of small animal. We construct a novel imaging system for NIR-I and NIR-IIb imaging simultaneously and perform two vascular imaging experiments. The results demonstrate that the NIR-IIb imaging using ICG shows great superiority for high-resolution vascular imaging of small animal compared with NIR-I imaging. It is believed that this study will facilitate the preclinical and clinical applications of NIR-IIb molecular imaging using ICG in the future.

Stress fracture is a common condition in athletes and military personnel yet objective methods for detection and tracking of stress fractures (e.g., MR and scintigraphy) remain complex and expensive. Methods involving ultrasound are in common use; however, they are thus far subjective (relying on patient report of pain). The goal of this work is to develop quantitative and sensitive ultrasonic evaluation methods that could be deployed broadly in smaller clinical setting for use by a wide range of medical professionals. Such methods would complement beam-formed images of the bone surface by adding information about even sub-wavelength features. We have demonstrated that small imperfections on the bone surface significantly alter the characteristics of the reflectivity as made evident in the relative magnitude of the specular vs. diffuse scatter. In this paper we will present our work toward developing methods for acquiring, enhancing, visualizing, and evaluating such effects. We will present results from measurements with ex vivo bone samples and phantoms, and discuss the ultimate applicability of our methods to in vivo diagnosis.

Bioluminescence tomography (BLT) is a promising molecular imaging tool in monitoring non-invasively physiological and pathological processes in vivo at the cellular and molecular levels. And the radiative transfer equation (RTE) has successfully been used as a standard model for describing the propagation of visible and near infrared photons trough biological tissues. However in practical application, implementation of RTE is extremely complicated for complex biological tissue. And several approximations of the RTE were applied to model the light transport in a turbid medium, such as the diffusion equation (DE) and the simplified spherical harmonic approximation equation (SP_N). However, DE provides a high computational efficiency and is only valid in the high scattering region, while SP_N has a large demand for memory space, which makes it difficult for SP_N to be used on the fine mesh and limits its application in practice. In this paper, we provided a new finite element mesh regrouping-based hybrid light transport model in BLT. Based on the optical property of biological tissue, the finite element mesh were grouped into high-scattering and low-scattering regions. And based on the theory of light transport, hybrid third-order simplified spherical harmonic approximate–diffusion equation model (HSDM) was used to forward light transport model. In numerical simulation experiments, accuracy and efficiency of our proposed method were evaluated. Results showed that the hybrid light transport model achieved a better balance between accuracy and efficiency compared with the DE and the SP₃ models. And it was best suited as a light transport model for Bioluminescence Tomography.

Contrast sensitivity decreases in keratoconus subjects even before the decrease is presented at high contrast visual acuity. The aim of our study was to estimate the subject’s contrast sensitivity and visual acuity in different keratoconus stages. In our study, there were included 45 keratoconus subjects (77 eyes) with the first, second and third keratoconus stages. Visual acuity and contrast sensitivity were measured at 3 m with the best possible spectacle correction and without vision correction using FrACT program 3.9.3. In the study, contrast sensitivity was measured at following spatial frequencies – 1, 3, 5, 7, 9, 11, 13 and 15 cpd. Contrast sensitivity analyse showed that there was a statistically significant difference among the contrast sensitivity of the first, second and third keratoconus stages in all spatial frequencies. If the keratoconus stage increased, the contrast sensitivity decreased more. The median visual acuity of first stage keratoconus subjects with spectacle correction was 0.56 (0.36, 0.65) decimal units, second stage keratoconus – 0.38 (0.21; 0.47) and third stage – 0.13 (0.08, 0.22).

Retrospective 4D image construction from continuously acquired 2D slices is a necessary step to achieve high-quality 4D images. Self-gating methods, which extract breathing signals only from image information without any external gating technology, have much potential, such as in pediatric patients with thoracic insufficiency syndrome (TIS) who suffer from extreme malformations of the chest wall, diaphragm, and spine, leading to breathing that is very complex with lots of abnormal respiration cycles, including very deep or shallow cycles. Existing methods do not work well in this clinical scenario and most are not fully automatic, requiring some manual interactive operations. In this paper, we propose a fully automatic 4D dMRI construction method based on the concept of flux to address the 4D image construction from 2D slices of subjects with complex respiration. Firstly, we extract the breathing signal for each location based on the flux of the optical flow vector field of the body region from the image series. Then, we give a full analysis for all cycles and extract several normal ones and map them to one cosine respiration model for each location. After that, we re-sample one normal cycle from the respiration model for each location independently. All of these resampled normal cycles form the final constructed 4D image. Qualitative and quantitative evaluations on 25 subjects show that the proposed method can handle datasets from subjects with more complex respiration and achieves good self consistency results while maintaining time and space continuity.

The size and shape of an x-ray source’s focal spot is a critical factor in the imaging system’s overall spatial resolution. The conventional approach to imaging the focal spot uses a pinhole camera, but this requires careful, manual measurements. Instead, we propose a novel alternative, simply using the collimator available on many x-ray systems. After placing the edge of a collimator blade in the center of the beam, we can obtain an image of its edge spread function (ESF). Each ESF provides information about the focal spot distribution – specifically, the parallel projection of the focal spot in the direction parallel to the edge. If the edge is then rotated about the beam axis, each image provides a different parallel projection of the focal spot until a complete Radon transform of the focal spot distribution is obtained. The focal spot can then be reconstructed by the inverse Radon transform, or parallel-beam filtered backprojection. We conducted a study on a clinical C-arm system with 3 focal spot sizes (0.3, 0.6, 1.0 mm nominal size), comparing the focal spot obtained using the rotating edge method against the conventional pinhole approach. Our results demonstrate accurate characterization of the size and shape of the focal spot.

Computed tomography (CT) is a medical imaging technique which is widely used in various clinical applications. Since the applied radiation is potentially harmful to humans, the applied doses are carefully monitored during CT image acquisition. On the contrary, the CT image quality is not analyzed or monitored during normal clinical operation. A wide range of parameters such as patient anatomy, implants, cardiac or respiratory motion, pathological tissue variations or an inappropriately configured CT system can affect the image quality. Hence, an automated image quality assessment would be desirable to track the CT image quality in daily clinical routine. In this work, the image noise and spatial resolution were characterized and analyzed in order to determine the image quality. While the image noise was characterized by the noise power spectrum (NPS), the spatial resolution was described by the modulation transfer function (MTF). Both methods were applied to human torso phantom images or real thorax CT images. It was shown that both parameters are useful to distinguish between different image qualities. Lower CT image quality was correlated with higher noise amplitudes (measured by the NPS) and a lower spatial resolution (measured by the MTF). Challenges for future works remain in generating appropriately annotated clinical CT image databases as well as in automatically selecting suitable regions-of-interest within the image.

The quantification of radiomics features is impacted by a complex interplay of image quality and segmentation performance. To understand and optimize the process, it is highly beneficial to isolate the impact of each factor quantitatively. The purpose of this study was to develop a segmentation performance index (𝑠′) to describe the segmentation performance relative to an ideal estimation, and further apply the index across several segmentation algorithms and image quality conditions in the task of characterizing lesion morphology. First, an estimator was developed with prior knowledge of the lesion and the imaging resolution properties to represent the best that an ideal segmentation operator would be expected to yield given the information content available from the image. Using three anthropomorphic lesion models of a mean contrast of 600 HU and mean radius of 5 mm, the method was applied to three segmentation operators based on (1) active contour, (2) thresholding, and (3) a commercial vendor offering (Siemens Healthineers). Images of the lesions were simulated in a uniform background for a Siemens Flash filtered back-projection (FBP) B31f reconstruction with noise magnitude ranging from 10 HU to 230 HU. Images were simulated 50 times at each noise magnitude to assess test-retest variability. The segmentation masks were used to calculate a root mean square error (RMSE) across test-retest images for morphology and size radiomics features. The ideal estimation RMSE was calculated for each feature and was divided by the segmentation RMSE resulting in the segmentation performance index (𝑠′). The 𝑠′ values were evaluated as a function of increasing noise. Traditional segmentation quality metrics including dice coefficient, volume similarity, and Hausdorff distance were also calculated for each noise magnitude to compare against the 𝑠′ index. Traditional segmentation quality metrics are not reflective of radiomics performance. The 𝑠′ values, in contrast, were found to be both feature-specific and noisespecific, useful in characterizing the performance of segmentation algorithms in measuring radiomics features. This allows for the choice of segmentation algorithms to yield optimum radiomic performance for a given imaging condition and or protocol.

X-ray spectrum plays an essential role in CT applications. Since it is difficult to measure x-ray spectrum directly in practice, x-ray spectrum is always indirectly obtained by using transmission measurements through a calibration phantom of known thickness and materials. These methods are independent of CT image reconstruction and bring extra cost. In this study, we propose a parametric physical model based spectrum estimation algorithm for CT. First, a physical model contains six parameters is proposed to represent the x-ray spectrum, which is derived from the x-ray imaging physics. Second, a template image contains different material components can be obtained by segmenting CT reconstructed images with a simple method. And the estimated projection can be calculated by reprojecting the template image with the proposed spectrum model. Finally, the six parameters expressing the spectrum can be solved by minimizing the error between the estimated projection and real measurements. The effectiveness of the proposed method has been validated on the simulated data. Experimental results demonstrate the proposed method can estimate the accurate spectra at low and high energies and provide a good reconstruction of characteristic radiations.

Imaging phantoms are test patterns used to measure image quality in computer tomography (CT) systems. A new phantom platform (Mercury Phantom, Gammex) provides test patterns for estimating the task transfer function (TTF) or noise power spectrum (NPF) and simulates different patient sizes. Determining which image slices are suitable for analysis currently requires manual annotation of these patterns by an expert, as subtle defects may make an image unsuitable for measurement. We propose a method of automatically classifying these test patterns in a series of phantom images using deep learning techniques. By adapting a convolutional neural network based on the VGG19 architecture with weights trained on ImageNet, we use transfer learning to produce a classifier for this domain. The classifier is trained and evaluated with over 3,500 phantom images acquired at a university medical center. Input channels for color images are successfully adapted to convey contextual information for phantom images. A series of ablation studies are employed to verify design aspects of the classifier and evaluate its performance under varying training conditions. Our solution makes extensive use of image augmentation to produce a classifier that accurately classifies typical phantom images with 98% accuracy, while maintaining as much as 86% accuracy when the phantom is improperly imaged.

Purpose. Explore the potential value of displaying information collected by stationary intraoral tomosynthesis (sIOT) as multi-view synthetic radiographs, using vertical root fractures (VRFs) as a model system.

Methods. Filled and unfilled extracted tooth roots containing artificially-induced VRFs were imaged by sIOT and standard periapical radiography. sIOT collected 7 views across a 12° angle span, providing information for an image processing chain that included reconstruction, weighting, and forward projection to generate a set a synthetic two-dimensional (2D) images. Qualitative assessments of fracture conspicuity were used for comparison.

Results. The conspicuity of VRFs changed significantly with the angle of imaging, suggesting benefit to displaying a set of synthetic images across a span of viewing angles. Although high-density in-plane and out-of-plane artifacts, which could limit the conspicuity of VRFs, were prominent in the three-dimensional (3D) stack of reconstructed image slices, these artifacts were minimal in the synthetic radiographs. As such, some fractures were displayed more clearly in the synthetic 2D images compared to the reconstructed 3D image stack. Also, in some cases, the fractures were more conspicuous in the sIOT-generated synthetic images than the standard periapical radiographs.

Conclusion. Multi-view synthetic radiography can improve the display of VRFs in images generated by sIOT. As such, this approach to dental imaging may offer a useful clinical tool, with potential application to a host of imaging tasks.

Optical flow-based methods are commonly used to detect and correct patient motion in modalities such as cone-beam computed tomography. With such methods, the rotational motion deriving from the acquisition trajectory itself is obscuring the patient motion and therefore considered a perturbation. In this work, the question commonly posed in motion estimation is reversed. Instead of considering the rotational motion as obscuring the patient motion, it can be used to derive shape information about the patient. This is done by computing the optical flow from projection images in order to find point correspondences in different frames of the acquisition. Finally, projective geometry is used to localize a given pair of corresponding 2D image points in 3D object space.

The advantage of this method is that it allows for the localization of structures which are not contained within the scan field of view of truncated acquisitions but within an extended projection field of view. Therefore, it is shown that for certain high contrast structures, e.g. the skull, this localization method can be used to estimate the maximum extent of a patient from truncated projection data, which is an important information for extrapolation methods, or to localize highly absorbing structures outside of the scan field of view which contribute to the severity of the typically observed truncation artifacts.

Purpose: The aim of this study was to implement, validate and apply a tool for the assessment of the MTF and NPS of dental CBCT scanners using up-to-date measurement methods. Methods: The MTF was calculated using the circular edge method, and the NPS as the 2D Fourier transform of homogeneous background ROIs. The validation framework has been set-up as follows: images with known MTF/NPS are simulated, and from these images the MTF/NPS was calculated and the results compared. The influence of the phantom dimensions and the CNR on the MTF/NPS accuracy has been studied for a 10x10 cm² system FOV and corresponding voxel size of 0.25 mm³. For the practical measurements, two specific phantoms have been designed, based on the simulated results. Results: For the MTF the minimum number of slices needed to achieve an error of at most 5 % has been studied as a function of the CNR of the phantom rod insert (26 mm diameter). For the NPS, the number of 128x128 ROIs needed to achieve an error of at most 5 % have been determined. Measurement results on two different dental CBCT scanners for a selection of imaging conditions are presented. Conclusion: The simulation results provide guidelines for proper use of the developed tool to obtain accurate MTF and NPS measurements in practice. From the actual measurements it can be concluded that sometimes more information about system design parameters from the manufacturer is needed in order to explain the results with absolute certainty.

Three dimensional CT images have enabled dentists to visualize the overall anatomic structure of teeth and jawbones. Dental cone-beam CT(CBCT) images include a lot more information as compared to panoramic radiographs, but it is hard to assess overall teeth and jaw structures at a single glance. Although panoramic images facilitate the evaluation process of overall anatomic structures, they may include geometric distortions, blurs, and superimposition of multiple structures due to the spine and ghost effects. Some image viewers have a cut-viewing function that enables orthogonal viewing along the user-set dental arch. However, the orthogonal viewing looks different from the panoramic image physically taken by a panoramic scanner. To make more convenient use of CBCT images, we have developed an approach to synthesize panoramic images from the CBCT images. In the synthesis of the panoramic images, we removed the ghost and spine superimposition to improve the visibility of the synthesized panoramic images.

The panoramic image synthesis has been done in three steps. At first, we extracted the panoramic dental arch from the CBCT images. At the next step, we removed the ghost-inducing bone parts from the CBCT images. Lastly, we synthesized panoramic images by stitching thousands of partial-view panoramic projection images.

With the advantage of the synthesized panoramic image, the additional panoramic scan is not further necessary, and undesirable features of conventional panoramic radiographs (e.g., ghost artifacts or cervical spine superimposition) can be entirely removed.

The properties of carbon nanotubes (CNTs) show a variety of application possibilities in the next generation industries. Among them, application to a field emission device is expected due to a high emission current at a low critical electric field. Compared with conventional thermionic emission X-ray sources, field emission X-ray sources have many advantages. Their electric field can be turned on and off quickly, minimizing radiation exposure times. Additionally, the size of the imaging system can be reduced because no cooling device is required. Using these advantages, we created a micro computed tomography (CT) system based on a CNT field emission X-ray source. The main advantages of the micro- CT lie in the high-spatial resolution, sensitivity to skeletons and lungs, and low cost. We optimized the field emission characteristics of the CNTs and the system geometry for high resolution.

While two-dimensional antiscatter grids (2D grid) reduce scatter intensity substantially in Cone Beam Computed Tomography (CBCT), a small fraction of scattered radiation is transmitted through the 2D grid to the detector. Residual scatter limits the accuracy of CT numbers and interferes with the correction of grid’s septal shadows, or footprint, in projections. If grid’s septal shadows are not adequately suppressed in projections, it will lead to ring artifacts in CBCT images. In this work, we present a new method to correct residual scatter transmitted through the grid by employing the 2D grid itself as a residual scatter measurement device.

Our method, referred as grid-based scatter sampling (GSS), exploits the spatial modulation of primary x-ray fluence by 2D grid’s septal shadows. The shape of the signal modulation pattern varies as a function of residual scatter intensity registered by detector pixels. Such a variation in signal pattern was employed to measure residual scatter intensity in each projection, and subsequently, residual scatter was subtracted from each projection.

To validate the GSS method, CBCT imaging experiments were conducted using a 2D antiscatter grid prototype in a linac mounted CBCT system. The effect of GSS method on the ring artifact reduction was quantified by measuring noise in CBCT images. In addition, the nonuniformity of Hounsfield Units (HU) and HU accuracy was measured in both head and pelvis-sized phantoms.

In qualitative evaluations, GSS method successfully reduced ring artifacts caused by 2D grid’s footprint. Image noise reduced by 23% due to reduction of ring artifacts. HU nonuniformity in water-equivalent sections was reduced from 20 HU to 10 HU, and streak artifacts between high density inserts were reduced. The phantom size dependent variations in HU was also reduced after application of GSS method. Without GSS method, HU of density inserts reduced by 9% on the average when phantom size was increased from head to pelvis. With GSS method, HU values reduced only by 5% under the same conditions.

In summary, GSS method complements the 2D grid’s scatter suppression performance, by correcting the scatter transmitted through the grid. This approach does not require additional scatter-measurement hardware, such as beam-stop arrays, since the grid itself is employed as the scatter measurement device. By suppressing residual scatter in projections, our proposed method successfully reduced artifacts caused by 2D grid’s footprint, and further improved CT number accuracy.

In CT system, the distance between the scanned object and the detector gives a significant impact on the total scatter intensity arriving at the detector. As a result, if an object is scanned off-center, its relative distance to the detector is changing when CT system is rotating, and the detector received scatter intensity will also be changing throughout the scan. In general only after the location information is obtained from the entire 360 degree raw data, the precise scatter radiation can be predicted. This could slow down reconstruction performance a lot. In this paper, we would like to propose a purely projection based scatter correction algorithm, which can be processed independently inside each projection. The promising results of the proposed algorithm suggests the variance of scatter received at different locations could be well predicted, meanwhile the reconstruction speed could be well maintained.