This PDF file contains the front matter associated with SPIE Proceedings Volume 9783, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

A conventional 3D/4D digital subtraction angiogram (DSA) requires two rotational acquisitions (mask and fill) to compute the log-subtracted projections that are used to reconstruct a 3D/4D volume. Since all of the vascular information is contained in the fill acquisition, it is hypothesized that it is possible to reduce the x-ray dose of the mask acquisition substantially and still obtain subtracted projections adequate to reconstruct a 3D/4D volume with noise level comparable to a full dose acquisition. A full dose mask and fill acquisition were acquired from a clinical study to provide a known full dose reference reconstruction. Gaussian noise was added to the mask acquisition to simulate a mask acquisition acquired at 10% relative dose. Noise in the low-dose mask projections was reduced with a weighted edge preserving (WEP) filter designed to preserve bony edges while suppressing noise. 2D log-subtracted projections were computed from the filtered low-dose mask and full-dose fill projections, and then 3D/4D-DSA reconstruction algorithms were applied. Additional bilateral filtering was applied to the 3D volumes. The signal-to-noise ratio measured in the filtered 3D/4D-DSA volumes was compared to the full dose case. The average ratio of filtered low-dose SNR to full-dose SNR was 1.07 for the 3D-DSA and 1.05 for the 4D-DSA, indicating the method is a feasible approach to restoring SNR in DSA scans acquired with a low-dose mask. The method was also tested in a phantom study with full dose fill and 22% dose mask.

Multiplanar reconstruction (MPR) in digital breast tomosynthesis (DBT) allows tomographic images to be portrayed in various orientations. We have conducted research to determine the resolution of tomosynthesis MPR. We built a phantom that houses a star test pattern to measure resolution. This phantom provides three rotational degrees of freedom. The design consists of two hemispheres with longitudinal and latitudinal grooves that reference angular increments. When joined together, the hemispheres form a dome that sits inside a cylindrical encasement. The cylindrical encasement contains reference notches to match the longitudinal and latitudinal grooves that guide the phantom’s rotations. With this design, any orientation of the star-pattern can be analyzed. Images of the star-pattern were acquired using a DBT mammography system at the Hospital of the University of Pennsylvania. Images taken were reconstructed and analyzed by two different methods. First, the maximum visible frequency (in line pairs per millimeter) of the star test pattern was measured. Then, the contrast was calculated at a fixed spatial frequency. These analyses confirm that resolution decreases with tilt relative to the breast support. They also confirm that resolution in tomosynthesis MPR is dependent on object orientation. Current results verify that the existence of super-resolution depends on the orientation of the frequency; the direction parallel to x-ray tube motion shows super-resolution. In conclusion, this study demonstrates that the direction of the spatial frequency relative to the motion of the x-ray tube is a determinant of resolution in MPR for DBT.

We have developed a clinically ready first generation stationary breast tomosynthesis system (s-DBT). In the s-DBT system, focal spot blur associated with x-ray source motion is completely eliminated, allowing for rapid acquisition of projection images over a larger angular span without changing the acquisition time.

In the phantom studies the 1st generation s‐DBT system has demonstrated 30% higher spatial resolution than the corresponding continuous motion DBT systems. The system is currently being evaluated for its diagnostic performance in 100 patient clinical evaluation against FFDM. Initial results indicate that the s­‐DBT system can produce increased lesion conspicuity and comparable MC visibility. However due to x­‐ray flux limitations, certain large size patients have to be excluded. Recent studies have shown that increasing the angular span beyond 30° can be beneficial for enhanced depth resolution. We report the preliminary characterization of the 2nd generation s-­DBT system with a new CNT x-­ray source array, increased tube flux and a larger angular span. Increasing x‐ray tube flux allows for a larger patient population and dual energy imaging. Results indicate that the system delivers more than twice the flux, allowing for imaging of all size patients with acquisition time of 2­‐4 seconds. A 7° increase in angular span over 1st generation decreased the ASF by 37%. Additionally, the 2nd generation s‐DBT system utilizing a specific AFVR reconstruction method resulted in a 92% increase in the in plane resolution over CM DBT system, and a 37% increase in spatial resolution over the 1st generation s-‐DBT system.

The coronary artery calcium score (CACS) measures the buildup of calcium on the coronary artery wall and has been shown to be an important predictor of the risk of coronary artery diseases (CAD). Currently CACS is measured using CT, though the relatively high cost and high radiation dose has limited its adoption as a routine screening procedure. Digital Chest Tomosynthesis (DCT), a low dose and low cost alternative to CT, and has been shown to achieve 90% of sensitivity of CT in lung disease screening. However commercial DCT requires long scanning time and cannot be adapted for high resolution gated cardiac imaging, necessary for CACS. The stationary DCT system (s- DCT), developed in our lab, has the potential to significantly shorten the scanning time and enables high resolution cardiac gated imaging. Here we report the preliminary results of using s-DCT to estimate the CACS. A phantom heart model was developed and scanned by the s-DCT system and a clinical CT in a phantom model with realistic coronary calcifications. The adapted fan-beam volume reconstruction (AFVR) method, developed specifically for stationary tomosynthesis systems, is used to obtain high resolution tomosynthesis images. A trained cardiologist segmented out the calcifications and the CACS was obtained. We observed a strong correlation between the tomosynthesis derived CACS and CT CACS (r² = 0.88). Our results shows s-DCT imaging has the potential to estimate CACS, thus providing a possible low cost and low dose imaging protocol for screening and monitoring CAD.

The aim of this study was to compare the detection of microcalcification clusters by human observers in breast images using 2D-mammography and narrow (15°/15 projections) and wide (50°/25 projections) angle digital breast tomosynthesis (DBT). Simulated microcalcification clusters with a range of microcalcification diameters (125 μm-275 μm) were inserted into 6 cm thick simulated compressed breasts. Breast images were produced with and without inserted microcalcification clusters using a set of image modelling tools, which were developed to represent clinical imaging by mammography and tomosynthesis. Commercially available software was used for image processing and image reconstruction. The images were then used in a series of 4-alternative forced choice (4AFC) human observer experiments conducted for signal detection with the microcalcification clusters as targets. The minimum detectable calcification diameter was found for each imaging modality: (i) 2D-mammography: 164±5 μm (ii) narrow angle DBT: 210±5 μm, (iii) wide angle DBT: 255±4 μm. A statistically significant difference was found between the minimum detectable calcification diameters that can be detected by the three imaging modalities. Furthermore, it was found that there was not a statistically significant difference between the results of the five observers that participated in this study. In conclusion, this study presents a method that quantifies the threshold diameter required for microcalcification detection, using high resolution, realistic images with observers, for the comparison of DBT geometries with 2D-mammography. 2Dmammography can visualise smaller detail diameter than both DBT imaging modalities and narrow-angle DBT can visualise a smaller detail diameter than wide-angle DBT.

Dual-energy contrast-enhanced digital mammography (DE CE-DM) uses an iodinated contrast agent to image the perfusion and vasculature of the breast. DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. We hypothesized that the optimal DE subtraction weighting factor is thickness-dependent, and developed a method for determining breast tissue composition and thickness in DE CE-DM. Phantoms were constructed using uniform blocks of 100% glandular-equivalent and 100% adipose-equivalent material. The thickness of the phantoms ranged from 3 to 8 cm, in 1 cm increments. For a given thickness, the glandular-adipose composition of the phantom was varied using different combinations of blocks. The logarithmic LE and logarithmic HE signal intensities were measured; they decrease linearly with increasing glandularity for a given thickness. The signals decrease with increasing phantom thickness and the x-ray signal decreases linearly with thickness for a given glandularity. As the thickness increases, the attenuation difference per additional glandular block decreases, indicating beam hardening. From the calibration mapping, we have demonstrated that we can predict percent glandular tissue and thickness when given two distinct signal intensities. Our results facilitate the subtraction of tissue at the boundaries of the breast, and aid in discriminating between contrast agent uptake in glandular tissue and subtraction artifacts.

Virtual clinical trials are an emergent approach for the rapid evaluation and comparison of various breast imaging technologies and techniques using computer-based modeling tools. A fundamental requirement of this approach for mammography is the use of realistic looking breast anatomy in the studies to produce clinically relevant results. In this work, a biologically inspired approach has been used to simulate realistic synthetic breast phantom blocks for use in virtual clinical trials. A variety of high and low frequency features (including Cooper’s ligaments, blood vessels and glandular tissue) have been extracted from clinical digital breast tomosynthesis images and used to simulate synthetic breast blocks. The appearance of the phantom blocks was validated by presenting a selection of simulated 2D and DBT images interleaved with real images to a team of experienced readers for rating using an ROC paradigm. The average areas under the curve for 2D and DBT images were 0.53±.04 and 0.55±.07 respectively; errors are the standard errors of the mean. The values indicate that the observers had difficulty in differentiating the real images from simulated images. The statistical properties of simulated images of the phantom blocks were evaluated by means of power spectrum analysis. The power spectrum curves for real and simulated images closely match and overlap indicating good agreement.

An anthropomorphic digital breast phantom has been developed with the goal of generating random voxelized breast models that capture the anatomic variability observed in vivo. This is a new phantom and is not based on existing digital breast phantoms or segmentation of patient images. It has been designed at the outset to be modality agnostic (i.e., suitable for use in modeling x-ray based imaging systems, magnetic resonance imaging, and potentially other imaging systems) and open source so that users may freely modify the phantom to suit a particular study. In this work we describe the modeling techniques that have been developed, the capabilities and novel features of this phantom, and study simulated images produced from it. Starting from a base quadric, a series of deformations are performed to create a breast with a particular volume and shape. Initial glandular compartments are generated using a Voronoi technique and a ductal tree structure with terminal duct lobular units is grown from the nipple into each compartment. An additional step involving the creation of fat and glandular lobules using a Perlin noise function is performed to create more realistic glandular/fat tissue interfaces and generate a Cooper’s ligament network. A vascular tree is grown from the chest muscle into the breast tissue. Breast compression is performed using a neo-Hookean elasticity model. We show simulated mammographic and T₁-weighted MRI images and study properties of these images.

Spectral imaging is the acquisition of multiple images of an object at different energy spectra. In mammography, dual-energy imaging (spectral imaging with two energy levels) has been investigated for several applications, in particular material decomposition, which allows for quantitative analysis of breast composition and quantitative contrast-enhanced imaging. Material decomposition with dual-energy imaging is based on the assumption that there are two dominant photon interaction effects that determine linear attenuation: the photoelectric effect and Compton scattering. This assumption limits the number of basis materials, i.e. the number of materials that are possible to differentiate between, to two. However, Rayleigh scattering may account for more than 10% of the linear attenuation in the mammography energy range. In this work, we show that a modified version of a scanning multi-slit spectral photon-counting mammography system is able to acquire three images at different spectra and can be used for triple-energy imaging. We further show that triple-energy imaging in combination with the efficient scatter rejection of the system enables measurement of Rayleigh scattering, which adds an additional energy dependency to the linear attenuation and enables material decomposition with three basis materials. Three available basis materials have the potential to improve virtually all applications of spectral imaging.

Mammography is currently the standard imaging modality used to screen women for breast abnormalities and, as a result, it is a tool of great importance for the early detection of breast cancer. Physical phantoms are commonly used as surrogates of breast tissue to evaluate some aspects of the performance of mammography systems. However, most phantoms do not reproduce the anatomic heterogeneity of real breasts. New fabrication technologies, such as 3D printing, have created the opportunity to build more complex, anatomically realistic breast phantoms that could potentially assist in the evaluation of mammography systems. The primary objective of this work is to present a simple, easily reproducible methodology to design and print 3D objects that replicate the attenuation profile observed in real 2D mammograms. The secondary objective is to evaluate the capabilities and limitations of the competing 3D printing technologies, and characterize the x-ray properties of the different materials they use. Printable phantoms can be created using the open-source code introduced in this work, which processes a raw mammography image to estimate the amount of x-ray attenuation at each pixel, and outputs a triangle mesh object that encodes the observed attenuation map. The conversion from the observed pixel gray value to a column of printed material with equivalent attenuation requires certain assumptions and knowledge of multiple imaging system parameters, such as x-ray energy spectrum, source-to-object distance, compressed breast thickness, and average breast material attenuation. A detailed description of the new software, a characterization of the printed materials using x-ray spectroscopy, and an evaluation of the realism of the sample printed phantoms are presented.

Breast ultrasound tomography is an emerging imaging modality to reconstruct the sound speed, density, and ultrasound attenuation of the breast in addition to ultrasound reflection/beamforming images for breast cancer detection and characterization. We recently designed and manufactured a new synthetic-aperture breast ultrasound tomography prototype with two parallel transducer arrays consisting of a total of 768 transducer elements. The transducer arrays are translated vertically to scan the breast in a warm water tank from the chest wall/axillary region to the nipple region to acquire ultrasound transmission and reflection data for whole-breast ultrasound tomography imaging. The distance of these two ultrasound transducer arrays is adjustable for scanning breasts with different sizes. We use our breast ultrasound tomography prototype to acquire phantom and in vivo patient ultrasound data to study its feasibility for breast imaging. We apply our recently developed ultrasound imaging and tomography algorithms to ultrasound data acquired using our breast ultrasound tomography system. Our in vivo patient imaging results demonstrate that our breast ultrasound tomography can detect breast lesions shown on clinical ultrasound and mammographic images.

In this article, we present an easy-to-implement Multi-energy CT scanning strategy and a corresponding reconstruction method, which facilitate spectral CT imaging by improving the data efficiency the number-of-energy- channel fold without introducing visible limited-angle artifacts caused by reducing projection views. Leveraging the structure coherence at different energies, we first pre-reconstruct a prior structure information image using projection data from all energy channels. Then, we perform a k-means clustering on the prior image to generate a sparse dictionary representation for the image, which severs as a structure information constraint. We com- bine this constraint with conventional compressed sensing method and proposed a new model which we referred as Joint Clustering Prior and Sparsity Regularization (CPSR). CPSR is a convex problem and we solve it by Alternating Direction Method of Multipliers (ADMM). We verify our CPSR reconstruction method with a numerical simulation experiment. A dental phantom with complicate structures of teeth and soft tissues is used. X-ray beams from three spectra of different peak energies (120kVp, 90kVp, 60kVp) irradiate the phantom to form tri-energy projections. Projection data covering only 75◦ from each energy spectrum are collected for reconstruction. Independent reconstruction for each energy will cause severe limited-angle artifacts even with the help of compressed sensing approaches. Our CPSR provides us with images free of the limited-angle artifact. All edge details are well preserved in our experimental study.

Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.

In this work, we provide an initial characterization of a novel twin robotic X-ray system. This system is equipped with two motor-driven telescopic arms carrying X-ray tube and flat-panel detector, respectively. 2D radiographs and fluoroscopic image sequences can be obtained from different viewing angles. Projection data for 3D cone-beam CT reconstruction can be acquired during simultaneous movement of the arms along dedicated scanning trajectories. We provide an initial evaluation of the 3D image quality based on phantom scans and clinical images. Furthermore, initial evaluation of patient dose is conducted. The results show that the system delivers high image quality for a range of medical applications. In particular, high spatial resolution enables adequate visualization of bone structures. This system allows 3D X-ray scanning of patients in standing and weight-bearing position. It could enable new 2D/3D imaging workflows in musculoskeletal imaging and improve diagnosis of musculoskeletal disorders.

We propose an adapted method of our previously published five-dimensional (5D) motion compensation (MoCo) algorithm1, developed for micro-CT imaging of small animals, to provide for the first time motion artifact-free 5D cone-beam CT (CBCT) images from a conventional flat detector-based CBCT scan of clinical patients. Image quality of retrospectively respiratory- and cardiac-gated volumes from flat detector CBCT scans is deteriorated by severe sparse projection artifacts. These artifacts further complicate motion estimation, as it is required for MoCo image reconstruction. For high quality 5D CBCT images at the same x-ray dose and the same number of projections as todays 3D CBCT we developed a double MoCo approach based on motion vector fields (MVFs) for respiratory and cardiac motion. In a first step our already published four-dimensional (4D) artifact-specific cyclic motion-compensation (acMoCo) approach is applied to compensate for the respiratory patient motion. With this information a cyclic phase-gated deformable heart registration algorithm is applied to the respiratory motion-compensated 4D CBCT data, thus resulting in cardiac MVFs. We apply these MVFs on double-gated images and thereby respiratory and cardiac motion-compensated 5D CBCT images are obtained. Our 5D MoCo approach processing patient data acquired with the TrueBeam 4D CBCT system (Varian Medical Systems). Our double MoCo approach turned out to be very efficient and removed nearly all streak artifacts due to making use of 100% of the projection data for each reconstructed frame. The 5D MoCo patient data show fine details and no motion blurring, even in regions close to the heart where motion is fastest.

Mobile and compact C-arm systems are routinely used in interventional procedures for fluoroscopic CT imaging. The mechanical requirements guarantee for a maximum of flexibility and mobility but restrict the mechanical rotation range (e.g. 165°) and the lateral size of the field of measurement (FOM), typically about 160 mm. Recently, the rotate-plus-shift trajectory for the acquisition of complete datasets from 180° minus fan-angle has been published.^{1, 2} Here, we combine the rotate-plus-shift trajectory with a shifted detector approach for a fully motorized C-arm system. As the isocenter in non-centric C-arms can be freely chosen, the shifted detector can be equally well absorbed with an offset of the C parallel to the transaxial detector direction. The typical rotation range of 360° used in shifted detector trajectories is replaced by a double rotate-plus-shift scan requiring a rotation range of at least 180° minus fan-angle. The trajectory increasing the diameter of the FOM by up to a factor of two is presented and the practical application of variations with an asymmetric FOM is shown. For image reconstruction we use our modified FDK algorithm that is equipped with a generalized redundancy weight. The presented trajectory can increase the applicability and flexibility of C-arm systems and has the potential to perform intra-operative large volume control or overview scans and thus reduce the patient’s risk.

Striped ratio grids are a new concept for scatter management in cone-beam CT. These grids are a modification of conventional anti-scatter grids and consist of stripes which alternate between high grid ratio and low grid ratio. Such a grid is related to existing hardware concepts for scatter estimation such as blocker-based methods or primary modulation, but rather than modulating the primary, the striped ratio grid modulates the scatter. The transitions between adjacent stripes can be used to estimate and subtract the remaining scatter. However, these transitions could be contaminated by variation in the primary radiation. We describe a simple nonlinear image processing algorithm to estimate scatter, and proceed to validate the striped ratio grid on experimental data of a pelvic phantom. The striped ratio grid is emulated by combining data from two scans with different grids. Preliminary results are encouraging and show a significant reduction of scatter artifact.

Purpose: Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness. Methods: Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1–4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT. Results: Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data. Conclusion: The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.

C-arm-based cone-beam CT (CBCT) systems with flat-panel detectors are suitable for diagnostic knee imaging due to their potentially flexible selection of CT trajectories and wide volumetric beam coverage. In knee CT imaging, over-exposure artifacts can occur because of limitations in the dynamic range of the flat panel detectors present on most CBCT systems. We developed a straightforward but effective method for correction and detection of over-exposure for an Automatic Exposure Control (AEC)-enabled standard knee scan incorporating a prior low dose scan. The radiation dose associated with the low dose scan was negligible (0.0042mSv, 2.8% increase) which was enabled by partially sampling the projection images considering the geometry of the knees and lowering the dose further to be able to just see the skin-air interface. We combined the line integrals from the AEC and low dose scans after detecting over-exposed regions by comparing the line profiles of the two scans detector row-wise. The combined line integrals were reconstructed into a volumetric image using filtered back projection. We evaluated our method using in vivo human subject knee data. The proposed method effectively corrected and detected over-exposure, and thus recovered the visibility of exterior tissues (e.g., the shape and density of the patella, and the patellar tendon), incorporating a prior low dose scan with a negligible increase in radiation exposure.

For grating interferometer-based x-ray differential phase contrast (DPC) imaging systems, their noise performance is strongly dependent on both the visibility of the interference fringe pattern and the total number of photons used to acquire and extract the DPC signal. For a given interferometer, it is usually designed to work at a specific x-ray energy, therefore any deviation from the designed energy may result in certain visibility loss. In this work, a single photon counting detector (PCD) was incorporated into a DPC imaging system, which enabled photons with energies close to the designed operation energy of the interferometer to be selectively used for DPC signal extraction. This approach led to significant boost in the fringe visibility, but it also discarded x-ray photons with other energies incident on the detector and might result in degradations of the overall radiation dose efficiency of the DPC imaging systems. This work presents a novel singular value decomposition (SVD)-based method to leverage the entire spectrum of x-ray photons detected by the PCD, enabling both fringe visibility improvement and reduction in image noise. As evidenced by the results of experimental phantom studies, the contrast-to-noise ratio of the final DPC images could be effectively improved by the proposed method.

One of the most exciting aspects of the grating based x-ray differential phase contrast (DPC) acquisition method is the concurrent generation of the so-called dark field (DF) signal, along with the classical absorption signal and the novel DPC signal. The DF signal is associated with local distribution of small angle scatterers in an image object, while the absorption signal and DPC signal are often used to characterize the relatively uniform structure of the image object. Besides the endogenous image contrast, exogenous contrast media are often used in x-ray imaging to locally enhance the image signal. This paper proposes a potential contrast medium for DF signal enhancement: microbubbles (MBs). MBs have already been developed for clinical use in ultrasound imaging, and recent experimental studies have shown that MBs may also enhance the DF signal, although it remained unclear how the physical characteristics of the MBs quantitatively impact the DF signal. In this paper, a systematic study was performed to investigate the quantitative relationships between the DF signal and the following properties of MBs: size, concentration, shell thickness, size uniformity, and whether gold nanoparticles were attached. The experimental results demonstrated that, an increased MB size (about 4 microns) may generate a stronger DF signal for our DPC imaging system; additionally, a moderately increased shell thickness and the use of gold nanoparticles on the shell surface also resulted in further enhancement of the DF signal. These findings may provide critical information needed for using MBs as the contrast agent of x-ray DF imaging.

X-ray grating-based phase-contrast Talbot-Lau interferometry is a promising imaging technology that has the potential to raise soft tissue contrast in comparison to conventional attenuation-based imaging. Additionally, it is sensitive to attenuation, refraction and scattering of the radiation and thus provides complementary and otherwise inaccessible information due to the dark-field image, which shows the sub-pixel size granularity of the measured object. Until recent progress the method has been mainly limited to photon energies below 40 keV. Scaling the method to photon energies that are sufficient to pass large and spacious objects represents a challenging task. This is caused by increasing demands regarding the fabrication process of the gratings and the broad spectra that come along with the use of polychromatic X-ray sources operated at high acceleration voltages. We designed a setup that is capable to reach high visibilities in the range from 50 to 120 kV. Therefore, spacious and dense parts of the human body with high attenuation can be measured, such as a human knee. The authors will show investigations on the resulting attenuation, differential phase-contrast and dark-field images. The images experimentally show that X-ray grating-based phase-contrast radiography is feasible with highly absorbing parts of the human body containing massive bones.

X-ray phase contrast imaging has been investigated during the last two decades for potential benefits in soft tissue imaging. Long imaging time, high radiation dose and general measurement complexity involving motion of x-ray optical components have prevented the clinical translation of these methods. In all existing popular phase contrast imaging methods, multiple measurements per projection angle involving motion of optical components are required to achieve quantitatively accurate estimation of absorption, phase and differential phase. Recently we proposed an algorithmic approach to use spectral detection data in a phase contrast imaging setup to obtain absorption, phase and differential phase in a single-step. Our generic approach has been shown via simulations in all three types of phase contrast imaging: propagation, coded aperture and grating interferometry. While other groups have used spectral detector in phase contrast imaging setups, our proposed method is unique in outlining an approach to use this spectral data to simplify phase contrast imaging. In this abstract we show the first experimental proof of our single-shot phase retrieval using a Medipix3 photon counting detector in an edge illumination aperture (also referred to as coded aperture) phase contrast set up as well as for a free space propagation setup. Our preliminary results validate our new transport equation for edge illumination PCI and our spectral phase retrieval algorithm for both PCI methods being investigated. Comparison with simulations also point to excellent performance of Medipix3 built-in charge sharing correction mechanism.

Flat-panel cone-beam CT (FP-CBCT) is a promising imaging modality, partly due to its potential for high spatial resolution reconstructions in relatively compact scanners. Despite this potential, FP-CBCT can face difficulty resolving important fine scale structures (e.g, trabecular details in dedicated extremities scanners and microcalcifications in dedicated CBCT mammography). Model-based methods offer one opportunity to improve high-resolution performance without any hardware changes. Previous work, based on a linearized forward model, demonstrated improved performance when both system blur and spatial correlations characteristics of FP-CBCT systems are modeled. Unfortunately, the linearized model relies on a staged processing approach that complicates tuning parameter selection and can limit the finest achievable spatial resolution. In this work, we present an alternative scheme that leverages a full nonlinear forward model with both system blur and spatially correlated noise. A likelihood-based objective function is derived from this forward model and we derive an iterative optimization algorithm for its solution. The proposed approach is evaluated in simulation studies using a digital extremities phantom and resolution-noise trade-offs are quantitatively evaluated. The correlated nonlinear model outperformed both the uncorrelated nonlinear model and the staged linearized technique with up to a 86% reduction in variance at matched spatial resolution. Additionally, the nonlinear models could achieve finer spatial resolution (correlated: 0.10 mm, uncorrelated: 0.11 mm) than the linear correlated model (0.15 mm), and traditional FDK (0.40 mm). This suggests the proposed nonlinear approach may be an important tool in improving performance for high-resolution clinical applications.

We present a new algorithm that allows for raw data-based automated cardiac and respiratory intrinsic gating in cone-beam CT scans. It can be summarized in three steps: First, a median filter is applied to an initially reconstructed volume. The forward projection of this volume contains less motion information and is subtracted from the original projections. This results in new raw data that contain only moving and not static anatomy like bones, that would otherwise impede the cardiac or respiratory signal acquisition. All further steps are applied to these modified raw data. Second, the raw data are cropped to a region of interest (ROI). The ROI in the raw data is determined by the forward projection of a binary volume of interest (VOI) that includes the diaphragm for respiratory gating and most of the edge of the heart for cardiac gating. Third, the mean gray value in this ROI is calculated for every projection and the respiratory/cardiac signal is acquired using a bandpass filter. Steps two and three are carried out simultaneously for 64 or 1440 overlapping VOI inside the body for the respiratory or cardiac signal respectively. The signals acquired from each ROI are compared and the most consistent one is chosen as the desired cardiac or respiratory motion signal. Consistency is assessed by the standard deviation of the time between two maxima. The robustness and efficiency of the method is evaluated using simulated and measured patient data by computing the standard deviation of the mean signal difference between the ground truth and the intrinsic signal.

Purpose: Prompt and reliable detection of intracranial hemorrhage (ICH) has substantial clinical impact in diagnosis and treatment of stroke and traumatic brain injury. This paper describes the design, development, and preliminary performance characterization of a dedicated cone-beam CT (CBCT) head scanner prototype for imaging of acute ICH. Methods: A task-based image quality model was used to analyze the detectability index as a function of system configuration, and hardware design was guided by the results of this model-based optimization. A robust artifact correction pipeline was developed using GPU-accelerated Monte Carlo (MC) scatter simulation, beam hardening corrections, detector veiling glare, and lag deconvolution. An iterative penalized weighted least-squares (PWLS) reconstruction framework with weights adjusted for artifact-corrected projections was developed. Various bowtie filters were investigated for potential dose and image quality benefits, with a MC-based tool providing estimates of spatial dose distribution. Results: The initial prototype will feature a source-detector distance of 1000 mm and source-axis distance of 550 mm, a 43x43 cm² flat panel detector, and a 15° rotating anode x-ray source with 15 kW power and 0.6 focal spot size. Artifact correction reduced image nonuniformity by ~250 HU, and PWLS reconstruction with modified weights improved the contrast to noise ratio by 20%. Inclusion of a bowtie filter can potentially reduce dose by 50% and improve CNR by 25%. Conclusions: A dedicated CBCT system capable of imaging millimeter-scale acute ICH was designed. Preliminary findings support feasibility of point-of-care applications in TBI and stroke imaging, with clinical studies beginning on a prototype.

In this work, a newly developed reconstruction algorithm, Synchronized MultiArtifact Reduction with Tomographic RECONstruction (SMART-RECON), was applied to C-arm cone beam CT perfusion (CBCTP) imaging. This algorithm contains a special rank regularizer, designed to reduce limited-view artifacts associated with super- short scan reconstructions. As a result, high temporal sampling and temporal resolution image reconstructions were achieved using an interventional C-arm x-ray system. The algorithm was evaluated in terms of the fidelity of the dynamic contrast update curves and the accuracy of perfusion parameters through numerical simulation studies. Results shows that, not only were the dynamic curves accurately recovered (relative root mean square error ∈ [3%, 5%] compared with [13%, 22%] for FBP), but also the noise in the final perfusion maps was dramatically reduced. Compared with filtered backprojection, SMART-RECON generated CBCTP maps with much improved capability in differentiating lesions with perfusion deficits from the surrounding healthy brain tissues.

Currently, clinical acquisition of IV 3D-DSA requires two separate scans: one mask scan without contrast medium and a filled scan with contrast injection. Having two separate scans adds radiation dose to the patient and increases the likelihood of suffering inadvertent patient motion induced mis-registration and the associated mis-registraion artifacts in IV 3D-DSA images. In this paper, a new technique, SMART-RECON is introduced to generate IV 3D-DSA images from a single Cone Beam CT (CBCT) acquisition to eliminate the mask scan. Potential benefits of eliminating mask scan would be: (1) both radiation dose and scan time can be reduced by a factor of 2; (2) intra-sweep motion can be eliminated; (3) inter-sweep motion can be mitigated. Numerical simulations were used to validate the algorithm in terms of contrast recoverability and the ability to mitigate limited view artifacts.

When an automatic exposure control is introduced in C-arm cone beam CT data acquisition, the spectral inconsistencies between acquired projection data are exacerbated. As a result, conventional water/bone correction schemes are not as effective as in conventional diagnostic x-ray CT acquisitions with a fixed tube potential. In this paper, a new method was proposed to reconstruct several images with different degrees of spectral consistency and thus different levels of beam hardening artifacts. The new method relies neither on prior knowledge of the x-ray beam spectrum nor on prior compositional information of the imaging object. Numerical simulations were used to validate the algorithm.

We introduce a novel strategy for fluence field modulation (FFM) in x-ray CT using multiple aperture devices (MADs). MAD filters permit FFM by blocking or transmitting the x-ray beam on a fine (0.1-1 mm) scale. The filters have a number of potential advantages over other beam modulation strategies including the potential for a highly compact design, modest actuation speed and acceleration requirements, and spectrally neutral filtration due to their essentially binary action. In this work, we present the underlying MAD filtration concept including a design process to achieve a specific class of FFM patterns. A set of MAD filters is fabricated using a tungsten laser sintering process and integrated into an x-ray CT test bench. A characterization of the MAD filters is conducted and compared to traditional attenuating bowtie filters and the ability to flatten the fluence profile for a 32 cm acrylic phantom is demonstrated. MAD-filtered tomographic data was acquired on the CT test bench and reconstructed without artifacts associated with the MAD filter. These initial studies suggest that MAD-based FFM is appropriate for integration in clinical CT system to create patient-specific fluence field profile and reduce radiation exposures.

Physicians rely on CT Perfusion (CTP) images and quantitative image data, including cerebral blood flow, cerebral blood volume, and bolus arrival delay, to diagnose and treat stroke patients. However, the quantification of these metrics may vary depending on the computational method used. Therefore, we have developed a dynamic and realistic digital brain phantom upon which CTP scans can be simulated based on a set of ground truth scenarios. Building upon the previously developed 4D extended cardiac-torso (XCAT) phantom containing a highly detailed brain model, this work consisted of expanding the intricate vasculature by semi-automatically segmenting existing MRA data and fitting nonuniform rational B-spline surfaces to the new vessels. Using time attenuation curves input by the user as reference, the contrast enhancement in the vessels changes dynamically. At each time point, the iodine concentration in the arteries and veins is calculated from the curves and the material composition of the blood changes to reflect the expected values. CatSim, a CT system simulator, generates simulated data sets of this dynamic digital phantom which can be further analyzed to validate CTP studies and post-processing methods. The development of this dynamic and realistic digital phantom provides a valuable resource with which current uncertainties and controversies surrounding the quantitative computations generated from CTP data can be examined and resolved.

Quantitative computed tomography (CT) applications based on statistical iterative reconstruction algorithms require accurate models of the CT acquisition process, with a key component being the x-ray fan beam intensity. We present a method to experimentally determine the extra-focal radiation profile incident on individual CT detectors. Using a tungsten cylinder as a knife edge, a super-sampled signal was created from sinogram data, which traced the “occlusion” of the x-ray source as seen by a detector. By differentiating this signal and correcting for finite detector size and motion blur, the effective source profile can be recovered. Extra-focal scatter was found to be on the order of 1-3 percent of the focal beam intensity, with lower relative magnitude at the isocenter and increasing towards the edge of the fan beam, with its profile becoming asymmetric at large angles. The implications for reconstruction algorithms and QCT applications will be discussed.

Cerebral CT perfusion (CTP) imaging is playing an important role in the diagnosis and treatment of acute ischemic strokes. Meanwhile, the reliability of CTP-based ischemic lesion detection has been challenged due to the noisy appearance and low signal-to-noise ratio of CTP maps. To reduce noise and improve image quality, a rigorous study on the noise transfer properties of CTP systems is highly desirable to provide the needed scientific guidance. This paper concerns how noise in the CTP source images propagates to the final CTP maps. Both theoretical deviations and subsequent validation experiments demonstrated that, the noise level of background frames plays a dominant role in the noise of the cerebral blood volume (CBV) maps. This is in direct contradiction with the general belief that noise of non-background image frames is of greater importance in CTP imaging. The study found that when radiation doses delivered to the background frames and to all non-background frames are equal, lowest noise variance is achieved in the final CBV maps. This novel equality condition provides a practical means to optimize radiation dose delivery in CTP data acquisition: radiation exposures should be modulated between background frames and non-background frames so that the above equality condition is satisïnAed. For several typical CTP acquisition protocols, numerical simulations and in vivo canine experiment demonstrated that noise of CBV can be effectively reduced using the proposed exposure modulation method.

The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas approach was also investigated. We hypothesize that the auto-segmentation algorithm is sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT scans were expertly segmented into nine regions. A leave-one-out validation study was performed, where every case was automatically segmented with each of the remaining cases used as the expert atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with median error for each organ region below 2%. In the spinal canal region, the median error was 7% across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case atlas reduced the variation in the dose estimates and additional improvements may be possible with more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated segmentation algorithm to provide accurate organ dose estimates.

Energy-resolved photon-counting CT (PCCT) is promising for material decomposition with multi-contrast agents. However, corrections for non-idealities of PCCT detectors are required, which are still active research areas. In addition, PCCT is associated with very high cost due to lack of mass production. In this work, we proposed an alternative approach to performing multi-energy CT, which was achieved by acquiring triple or quadruple x-ray beam measurements on a dual-source CT scanner. This strategy was based on a “Twin Beam” design on a single-source scanner for dual-energy CT. Examples of beam filters and spectra for triple and quadruple x-ray beam were provided. Computer simulation studies were performed to evaluate the accuracy of material decomposition for multi-contrast mixtures using both tri-beam and quadruple-beam configurations. The proposed strategy can be readily implemented on a dual-source scanner, which may allow material decomposition of multi-contrast agents to be performed on clinical CT scanners with energy-integrating detector.

Respiration-correlated CT (4DCT) forms the basis of clinical 4D radiotherapy workflows for patients with thoracic and abdominal lesions. 4DCT image data, however, often suffers from motion artifacts due to unfulfilled assumptions during reconstruction and image/projection data sorting. In this work and focusing on low-pitch helical scanning protocols, two questionable assumptions are addressed: (1) the need for regular breathing patterns and (2) a constant correlation between the external breathing signal acquired for image/projection sorting and internal motion patterns. To counteract (1), a patient-specific upper breathing signal amplitude threshold is introduced to avoid artifacts due to unusual deep inspiration (helpful for both amplitude- and phase-based reconstruction). In addition, a projection data binning algorithm based on a statistical analysis of the patient's breathing signal is proposed to stabilize phase-based sorting. To further alleviate the need for (2), an image artifact metric is incorporated into and minimized during the reconstruction process. The optimized reconstruction is evaluated using 30 clinical 4DCT data sets and demonstrated to significantly reduce motion artifacts.

Detective Quantum Efficiency (DQE) is one of the most important image quality metrics for evaluating the spatial resolution performance of flat-panel x-ray detectors. In this work, we simulate the DQE of amorphous selenium (a-Se) xray detectors with a detailed Monte Carlo transport code (ARTEMIS) for modeling semiconductor-based direct x-ray detectors. The transport of electron-hole pairs is achieved with a spatiotemporal model that accounts for recombination and trapping of carriers and Coulombic effects of space charge and external applied electric field. A range of x-ray energies has been simulated from 10 to 100 keV. The DQE results can be used to study the spatial resolution characteristics of detectors at different energies.

Indirect conversion flat panel detectors (FPDs) based on amorphous silicon (a-Si) technology are widely used in digital X-ray imaging. In such FPDs a scintillator layer is used for converting X-rays into visible light photons. However, the lateral spread of these photons inside the scintillator layer reduces spatial resolution of the FPD. In this study, FPDs incorporating pixelated scintillators with a barrier rib structure were developed to limit lateral spread of light photons thereby improving spatial resolution. For the pixelated scintillator, a two-dimensional barrier rib structure was first manufactured on a substrate layer, coated with reflective materials, and filled to the rim with the scintillating material of gadolinium oxysulfide (GOS). Several scintillator samples were fabricated, with pitch size varying from 160 to 280 μm and rib height from 200 to 280 μm. The samples were directly coupled to an a-Si flat panel photodiode array with a pitch of 200 μm to convert optical photons to electronic signals. With the pixelated scintillator, the detector modulation transfer function was shown to improve significantly (by 94% at 2 cycle/mm) compared to a detector using an unstructured GOS layer. However, the prototype does show lower sensitivity due to the decrease in scintillator fill factor. The preliminary results demonstrated the feasibility of using the barrier-rib structure to improve the spatial resolution of FPDs. Such an improvement would greatly benefit nondestructive testing applications where the spatial resolution is the most important parameter. Further investigation will focus on improving the detector sensitivity and exploring its medical applications.

Digital Breast Tomosynthesis (DBT) requires excellent image quality in a dynamic mode at very low dose levels while Full Field Digital Mammography (FFDM) is a static imaging modality that requires high saturation dose levels. These opposing requirements can only be met by a dynamic detector with a high dynamic range. This paper will discuss a wafer-scale CMOS-based mammography detector with 49.5 μm pixels and a CsI scintillator. Excellent image quality is obtained for FFDM as well as DBT applications, comparing favorably with a-Se detectors that dominate the X-ray mammography market today. The typical dynamic range of a mammography detector is not high enough to accommodate both the low noise and the high saturation dose requirements for DBT and FFDM applications, respectively. An approach based on gain switching does not provide the signal-to-noise benefits in the low-dose DBT conditions. The solution to this is to add frame summing functionality to the detector. In one X-ray pulse several image frames will be acquired and summed. The requirements to implement this into a detector are low noise levels, high frame rates and low lag performance, all of which are unique characteristics of CMOS detectors. Results are presented to prove that excellent image quality is achieved, using a single detector for both DBT as well as FFDM dose conditions. This method of frame summing gave the opportunity to optimize the detector noise and saturation level for DBT applications, to achieve high DQE level at low dose, without compromising the FFDM performance.

Active matrix flat panel imagers (AMFPI) have become the dominant detector technology for digital radiography and fluoroscopy. For low dose imaging, electronic noise from the amorphous silicon thin film transistor (TFT) array degrades imaging performance. We have fabricated the first prototype solid-state AMFPI using a uniform layer of avalanche amorphous selenium (a-Se) photoconductor to amplify the signal to eliminate the effect of electronic noise. We have previously developed a large area solid-state avalanche a-Se sensor structure referred to as High Gain Avalanche Rushing Photoconductor (HARP) capable of achieving gains of 75. In this work we successfully deposited this HARP structure onto a 24 x 30 cm² TFT array with a pixel pitch of 85 μm. An electric field (E_Se) up to 105 Vμm^-1 was applied across the a-Se layer without breakdown. Using the HARP layer as a direct detector, an X-ray avalanche gain of 15 ± 3 was achieved at E_Se = 105 Vμm^-1. In indirect mode with a 150 μm thick structured CsI scintillator, an optical gain of 76 ± 5 was measured at E_Se = 105 Vμm^-1. Image quality at low dose increases with the avalanche gain until the electronic noise is overcome at a constant exposure level of 0.76 mR. We demonstrate the success of a solid-state HARP X-ray imager as well as the largest active area HARP sensor to date.

The ability to improve visualization of structural information in digital radiography without increasing radiation exposures requires improved image quality across all spatial frequencies, especially at high frequencies. The detective quantum efficiency (DQE) as a function of spatial frequency quantifies image quality given by an x-ray detector. We present a method of increasing DQE at high spatial frequencies by improving the modulation transfer function (MTF) and reducing noise aliasing. The Apodized Aperature Pixel (AAP) design uses a detector with micro-elements to synthesize desired pixels and provide higher DQE than conventional detector designs. A cascaded system analysis (CSA) that incorporates x-ray interactions is used for comparison of the theoretical MTF, noise power spectrum (NPS), and DQE. Signal and noise transfer through the converter material is shown to consist of correlated an uncorrelated terms. The AAP design was shown to improve the DQE of both material types that have predominantly correlated transfer (such as CsI) and predominantly uncorrelated transfer (such as Se). Improvement in the MTF by 50% and the DQE by 100% at the sampling cut-off frequency is obtained when uncorrelated transfer is prevalent through the converter material. Optimizing high-frequency DQE results in improved image contrast and visualization of small structures and fine-detail.

Sequential CT studies present an excellent opportunity to apply prior-image-based reconstruction (PIBR) methods that leverage high-fidelity prior imaging studies to improve image quality and/or reduce x-ray exposure in subsequent studies. One major obstacle in using PIBR is that the initial and subsequent studies are often performed on different scanners (e.g. diagnostic CT followed by CBCT for interventional guidance); this results in mismatch in attenuation values due to hardware and software differences. While improved artifact correction techniques can potentially mitigate such differences, the correction is often incomplete. Here, we present an alternate strategy where the PIBR itself is used to mitigate these differences. We define a new penalty for the previously introduced PIBR called Reconstruction of Difference (RoD). RoD differs from many other PIBRs in that it reconstructs only changes in the anatomy (vs. reconstructing the current anatomy). Direct regularization of the difference image in RoD provides an opportunity to selectively penalize spatial frequencies of the difference image (e.g. low frequency differences associated with attenuation offsets and shading artifacts) without interfering with the variations in unchanged background image. We leverage this flexibility and introduce a novel regularization strategy using a generalized Fourier penalty within the RoD framework and develop the modified reconstruction algorithm. We evaluate the performance of the new approach in both simulation studies and in physical CBCT test-bench data. We find that generalized Fourier penalty can be highly effective in reducing low-frequency x-ray artifacts through selective suppression of spatial frequencies in the reconstructed difference image.

Until today, several software-based approaches to increase the temporal resolution in cardiac computed tomography by estimating motion vector fields (MVFs) have been developed. Thereunder, the majority are motion compensation algorithms, which estimate the MVFs employing a three-dimensional registration routine working on reconstructions of multiple cardiac phases.^{2, 6, 7, 12} We present an algorithm that requires nothing more than the data needed for a short scan reconstruction for motion estimation and motion-compensated reconstruction, which both are based on the reconstruction of volumes from a limited angular range.^{2, 3, 7, 8} Those partial angle reconstructions are centered at different time points during the short scan and have a temporal resolution of about 10ms each. The MVFs are estimated by a constrained cost function optimization routine employing a motion artifact measuring cost function. During optimization, the MVFs are applied directly by warping the partial angle reconstructions, and the motion compensation is established by simply adding the shifted images. In order to enforce smooth vector fields and keep the number of parameters low, the motion is modeled by a low degree polynomial. Furthermore, to find a good estimation of the MVFs even in phases with rapid cardiac motion, the constrained optimization is re-initialized multiple times. The algorithm is validated with the help of a simulation study and applied to patient data, where motion- compensated reconstructions are performed in various cardiac phases. We show that the image quality can be improved, also in more rapid cardiac phases due to re-initialization of the optimization routine.

Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text file. Several instructional documents are provided to help the users extract information from DICOM-CT-PD files, including a dictionary file for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.

Iterative reconstruction methods have become very popular and show the potential to reduce dose. We present a limit to the maximum dose reduction possible with new reconstruction algorithms obtained by analyzing the information content of the raw data, assuming the reconstruction algorithm does not have a priori knowledge about the object or correlations between pixels. This limit applies to the task of estimating the density of a lesion embedded in a known background object, where the shape of the lesion is known but its density is not. Under these conditions, the density of the lesion can be estimated directly from the raw data in an optimal manner. This optimal estimate will meet or outperform the performance of any reconstruction method operating on the raw data, under the condition that the reconstruction method does not introduce a priori information. The raw data bound can be compared to the lesion density estimate from FBP in order to produce a limit on the dose reduction possible from new reconstruction algorithms. The possible dose reduction from iterative reconstruction varies with the object, but for a lesion embedded in the center of a water cylinder, it is less than 40%. Additionally, comparisons between iterative reconstruction and filtered backprojection are sometimes confounded by the effect of through-slice blurring in the iterative reconstruction. We analyzed the magnitude of the variance reduction brought about by through-slice blurring on scanners from two different vendors and found it to range between 11% and 48%.

When the scan field of view (SFOV) of a CT system is not large enough to enclose the entire cross-section of a patient, or the patient needs to be intentionally positioned partially outside the SFOV for certain clinical CT scans, truncation artifacts are often observed in the reconstructed CT images. Conventional wisdom to reduce truncation artifacts is to complete the truncated projection data via data extrapolation with different a priori assumptions. This paper presents a novel truncation artifact reduction method that directly works in the CT image domain. Specifically, a discriminative dictionary that includes a sub-dictionary of truncation artifacts and a sub-dictionary of non-artifact image information was used to separate a truncation artifact-contaminated image into two sub-images, one with reduced truncation artifacts, and the other one containing only the truncation artifacts. Both experimental phantom and retrospective human subject studies have been performed to characterize the performance of the proposed truncation artifact reduction method.

We present a novel reconstruction for motion correction of non-cardiac organs. With non-cooperative patients or in emergency case, breathing motion or motion of the skull may compromise image quality. Our algorithm is based on the optimization of either motion artefact metrics or data-driven metrics. This approach was successfully applied in cardiac CTA [1]. While motion correction of the coronary vessels requires a local motion model, global motion models are sufficient for organs like the lung or the skull. The parameter vector for the global affine motion is estimated iteratively, using the open source optimization library NLOPT. The image is updated using motion compensated reconstruction in each of the iterations. Evaluation of the metric value, e.g. the image entropy, provides information for the next iteration loop. After reaching the fixed point of the iteration, the final motion parameters are used for a motion-compensated full quality reconstruction. In head imaging the motion model is based on translation and rotation, in thoracic imaging the rotation is replaced by non-isotropic scaling in all three dimensions. We demonstrate the efficiency of the method in thoracic imaging by evaluating PET-CT data from free-breathing patients. In neuro imaging, data from stroke patients showing skull tremor were analyzed. It was shown that motion artefacts can be largely reduced and spatial resolution was restored. In head imaging, similar results can be obtained using motion artefact metrics or data-driven metrics. In case of image-based metrics, the entropy of the image proved to be superior. Breathing motion could also be significantly reduced using entropy metric. However, in this case data driven metrics cannot be applied because the line integrals associated to the ROI of the lung have to be computed using the local ROI mechanism [2] It was shown that the lung signal is corrupted by signals originating from the complement of the lung. Thus a meaningful optimization of a data-driven cost function is not possible.

Photon-counting CT (PCCT) may yield potential value for many clinical applications due to its relative immunity to electronic noise, increased geometric efficiency relative to current scintillating detectors, and the ability to resolve energy information about the detected photons. However, there are a large number of parameters that require optimization, particularly the energy thresholds configuration. Fast and accurate estimation of signal and noise in PCCT can benefit the optimization of acquisition parameters for specific diagnostic tasks. Based on the acquisition parameters and detector response of our research PCCT system, we derived mathematical models for both signal and noise. The signal model took the tube spectrum, beam filtration, object attenuation, water beam hardening, and detector response into account. The noise model considered the relationship between noise and radiation dose, as well as the propagation of noise as threshold data are subtracted to yield energy bin data. To determine the absolute noise value, a noise look-up table (LUT) was acquired using a limited number of calibration scans. The noise estimation algorithm then used the noise LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuation. Validation of the estimation algorithms was performed on our whole-body research PCCT system using semianthropomorphic water phantoms and solutions of calcium and iodine. The algorithms achieved accurate estimation of signal and noise for a variety of scanning parameter combinations. The proposed method can be used to optimize energy thresholds configuration for many clinical applications of PCCT.

The energy resolving capabilities of Photon Counting Detectors (PCD) in Computed Tomography (CT) facilitate energy-sensitive measurements. The provided image-information can be processed with Dual Energy and Multi Energy algorithms. A research PCD-CT firstly allows acquiring images with a close to clinical configuration of both the X-ray tube and the CT-detector. In this study, two algorithms (Material Decomposition and Virtual Non-Contrast-imaging (VNC)) are applied on a data set acquired from an anesthetized rabbit scanned using the PCD-CT system. Two contrast agents (CA) are applied: A gadolinium (Gd) based CA used to enhance contrasts for vascular imaging, and xenon (Xe) and air as a CA used to evaluate local ventilation of the animal's lung. Four different images are generated: a) A VNC image, suppressing any traces of the injected Gd imitating a native scan, b) a VNC image with a Gd-image as an overlay, where contrast enhancements in the vascular system are highlighted using colored labels, c) another VNC image with a Xe-image as an overlay, and d) a 3D rendered image of the animal's lung, filled with Xe, indicating local ventilation characteristics. All images are generated from two images based on energy bin information. It is shown that a modified version of a commercially available dual energy software framework is capable of providing images with diagnostic value obtained from the research PCD-CT system.

Shot noise processes are omnipresent in physics and many of their properties have been extensively studied in the past, including the particular problem of level crossing of shot noise. Energy-sensitive, photon-counting detectors using comparators to discriminate pulse-heights are currently heavily investigated for medical applications, e.g. for x-ray computed tomography and x-ray mammography. Surprisingly, no mention of the close relation between the two topics can be found in the literature on photon-counting detectors. In this paper, we point out the close analogy between level crossing of shot noise and the problem of determining count rates of photon- counting detectors subject to pulse pile-up. The latter is very relevant for obtaining precise forward models for photon-counting detectors operated under conditions of very high x-ray flux employed in clinical x-ray computed tomography. Although several attempts have been made to provide reasonably accurate, approximative models for the registered number of counts in x-ray detectors under conditions of high flux and arbitrary x-ray spectra, see, e.g., no exact, analytic solution is given in the literature for general continuous pulse shapes. In this paper we present such a solution for arbitrary response functions, x-ray spectra and continuous pulse shapes based on a result from the theory of level crossing. We briefly outline the theory of level crossing including the famous Rice theorem and translate from the language of level crossing to the language of photon-counting detection.

A high-resolution (HR) data collection mode has been introduced to a whole-body, research photon-counting-detector CT system installed in our laboratory. In this mode, 64 rows of 0.45 mm x 0.45 mm detector pixels were used, which corresponded to a pixel size of 0.25 mm x 0.25 mm at the iso-center. Spatial resolution of this HR mode was quantified by measuring the MTF from a scan of a 50 micron wire phantom. An anthropomorphic lung phantom, cadaveric swine lung, temporal bone and heart specimens were scanned using the HR mode, and image quality was subjectively assessed by two experienced radiologists. High spatial resolution of the HR mode was evidenced by the MTF measurement, with 15 lp/cm and 20 lp/cm at 10% and 2% modulation. Images from anthropomorphic phantom and cadaveric specimens showed clear delineation of small structures, such as lung vessels, lung nodules, temporal bone structures, and coronary arteries. Temporal bone images showed critical anatomy (i.e. stapes superstructure) that was clearly visible in the PCD system. These results demonstrated the potential application of this imaging mode in lung, temporal bone, and vascular imaging. Other clinical applications that require high spatial resolution, such as musculoskeletal imaging, may also benefit from this high resolution mode.

With many attractive attributes, photon counting detectors with many energy bins are being considered for clinical CT systems. In practice, a large amount of projection data acquired for multiple energy bins must be transferred in real time through slip rings and data storage subsystems, causing a bandwidth bottleneck problem. The higher resolution of these detectors and the need for faster acquisition additionally contribute to this issue. In this work, we introduce a new approach to lossless compression, specifically for projection data from photon counting detectors, by utilizing the dependencies in the multi-energy data. The proposed predictor estimates the value of a projection data sample as a weighted average of its neighboring samples and an approximation from other energy bins, and the prediction residuals are then encoded. Context modeling using three or four quantized local gradients is also employed to detect edge characteristics of the data. Using three simulated phantoms including a head phantom, compression of 2.3:1-2.4:1 was achieved. The proposed predictor using zero, three, and four gradient contexts was compared to JPEG-LS and the ideal predictor (noiseless projection data). Among our proposed predictors, three-gradient context is preferred with a compression ratio from Golomb coding 7% higher than JPEG-LS and only 3% lower than the ideal predictor. In encoder efficiency, the Golomb code with the proposed three-gradient contexts has higher compression than block floating point. We also propose a lossy compression scheme, which quantizes the prediction residuals with scalar uniform quantization using quantization boundaries that limit the ratio of quantization error variance to quantum noise variance. Applying our proposed predictor with three-gradient context, the lossy compression achieved a compression ratio of 3.3:1 but inserted a 2.1% standard deviation of error compared to that of quantum noise in reconstructed images. From the initial simulation results, the proposed algorithm shows good control over the bits needed to represent multienergy projection data.

There exists interest in designing a PET system with reduced detectors due to cost concerns, while not significantly compromising the PET utility. Recently developed optimization-based algorithms, which have demonstrated the potential clinical utility in image reconstruction from sparse CT data, may be used for enabling such design of innovative PET systems. In this work, we investigate a PET configuration with reduced number of detectors, and carry out preliminary studies from patient data collected by use of such sparse-PET configuration. We consider an optimization problem combining Kullback-Leibler (KL) data fidelity with an image TV constraint, and solve it by using a primal-dual optimization algorithm developed by Chambolle and Pock. Results show that advanced algorithms may enable the design of innovative PET configurations with reduced number of detectors, while yielding potential practical PET utilities.

The optic nerve (ON) plays a crucial role in human vision transporting all visual information from the retina to the brain for higher order processing. There are many diseases that affect the ON structure such as optic neuritis, anterior ischemic optic neuropathy and multiple sclerosis. Because the ON is the sole pathway for visual information from the retina to areas of higher level processing, measures of ON damage have been shown to correlate well with visual deficits. Increased intracranial pressure has been shown to correlate with the size of the cerebrospinal fluid (CSF) surrounding the ON. These measures are generally taken at an arbitrary point along the nerve and do not account for changes along the length of the ON. We propose a high contrast and high-resolution 3-D acquired isotropic imaging sequence optimized for ON imaging. We have acquired scan-rescan data using the optimized sequence and a current standard of care protocol for 10 subjects. We show that this sequence has superior contrast-to-noise ratio to the current standard of care while achieving a factor of 11 higher resolution. We apply a previously published automatic pipeline to segment the ON and CSF sheath and measure the size of each individually. We show that these measures of ON size have lower short- term reproducibility than the population variance and the variability along the length of the nerve. We find that the proposed imaging protocol is (1) useful in detecting population differences and local changes and (2) a promising tool for investigating biomarkers related to structural changes of the ON.

In time-of-flight (TOF) positron emission tomography (PET), precise calibration of the timing offset of each crystal of a PET scanner is essential. Conventionally this calibration requires a specially designed tool just for this purpose. In this study a method that uses a planar source to measure the crystal timing offsets (CTO) is developed. The method uses list mode acquisitions of a planar source placed at multiple orientations inside the PET scanner field-of-view (FOV). The placement of the planar source in each acquisition is automatically figured out from the measured data, so that a fixture for exactly placing the source is not required. The expected coincidence time difference for each detected list mode event can be found from the planar source placement and the detector geometry. A deviation of the measured time difference from the expected one is due to CTO of the two crystals. The least squared solution of the CTO is found iteratively using the list mode events. The effectiveness of the crystal timing calibration method is evidenced using phantom images generated by placing back each list mode event into the image space with the timing offset applied to each event. The zigzagged outlines of the phantoms in the images become smooth after the crystal timing calibration is applied. In conclusion, a crystal timing calibration method is developed. The method uses multiple list mode acquisitions of a planar source to find the least squared solution of crystal timing offsets.

Unaccounted scatter impact from unknown outside-axial-field-of-view (outside-AFOV) activity in PET is an important degrading factor for image quality and quantitation. Resource consuming and unpopular way to account for the outside- AFOV activity is to perform an additional PET/CT scan of adjacent regions. In this work we investigate a solution to the outside-AFOV scatter problem without performing a PET/CT scan of the adjacent regions. The main motivation for the proposed method is that the measured random corrected prompt (RCP) sinogram in the background region surrounding the measured object contains only scattered events, originating from both inside- and outside-AFOV activity. In this method, the scatter correction simulation searches through many randomly-chosen outside-AFOV activity estimates along with known inside-AFOV activity, generating a plethora of scatter distribution sinograms. This digital experimentation iterates until a decent match is found between a simulated scatter sinogram (that include supposed outside-AFOV activity) and the measured RCP sinogram in the background region. The combined scatter impact from inside- and outside-AFOV activity can then be used for scatter correction during final image reconstruction phase. Preliminary results using measured phantom data indicate successful phantom length estimate with the method, and, therefore, accurate outside-AFOV scatter estimate.

Photon counting detectors in computed tomography facilitate measurements of spectral distributions of detected X-ray quanta in discrete energy bins. Along with the dependency on wavelength and atomic number of the mass attenuation coefficient, this information allows for reconstruction of CT images of different material bases. Decomposition of two materials is considered standard in today’s dual-energy techniques. With photon-counting detectors the decomposition of more than two materials becomes achievable. Efficient detection of CT-typical X-ray spectra is a hard requirement in a clinical environment. This is fulfilled by only a few sensor materials such as CdTe or CdZnTe. In contrast to energy integrating CT-detectors, the pixel dimensions must be reduced to avoid pulse pile-up problems at clinically relevant count rates. However, reducing pixel sizes leads to increased K-escape and charge sharing effects. As a consequence, the correlation between incident and detected X-ray energy is reduced. This degradation is quantified by the detector response function. The goal of this study is to improve the achievable material decomposition by adapting the incident X-ray spectrum with respect to the properties (i.e. the detector response function) of a photon counting detector. A significant improvement of a material decomposition equivalent metric is achievable when using specific materials as X-ray pre-filtration (K-edge filtering) while maintaining the applied patient dose and image quality.

Recent advances in Photon Counting CT (PCCT) have facilitated the simultaneous acquisition of multiple image volumes with differing energy thresholds. This presents the user with several choices for energy threshold combinations. As compared to standard clinical Dual kVp CT , where the user typically has only three choices of kVp pairings (e.g., 80/150Sn, 90/150Sn, 100/150Sn), a “quad” PCCT system with 14 threshold settings has Choose(14,4)= 1001 possible threshold combinations (assuming no restrictions). In this paper we describe a computationally tractable means to order, from best (most accurate) to worst (least accurate), threshold combinations for the task of discriminating pure materials of assumed approximate concentrations using the Bhattacharyya Coefficient. We observe that this ordering is not necessarily identical to the ordering for the task of decomposing material mixtures into their components. We demonstrate our approach on phantom data.

Spectral imaging systems need to be able to produce "conventional" images, and it's been shown that systems with energy discriminating detectors can achieve higher CNR than conventional systems by optimal weighting. Combining measured data in energy bins (EBs) and also combining basis material images have previously been proposed, but there are no studies systematically comparing the two methods. In this paper, we analytically evaluate the two methods for systems with ideal photon counting detectors using CNR and beam hardening (BH) artifact as metrics. For a 120-kVp polychromatic simulations of a water phantom with low contrast inserts, the difference of the optimal CNR between the two methods for the studied phantom is within 2%. For a polychromatic spectrum, beam-hardening artifacts are noticeable in EB weighted images (BH artifact of 3.8% for 8 EB and 6.9% for 2 EB), while weighted basis material images are free of such artifacts.

An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the two pixels. This is called double-counting with charge sharing. The output of individual PCD pixel is Poisson distributed integer counts; however, the outputs of adjacent pixels are correlated due to double-counting. Major problems are the lack of detector noise model for the spatio-energetic crosstalk and the lack of an efficient simulation tool. Monte Carlo simulation can accurately simulate these phenomena and produce noisy data; however, it is not computationally efficient. In this study, we developed a new detector model and implemented into an efficient software simulator which uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account effects: (1) detection efficiency and incomplete charge collection; (2) photoelectric effect with total absorption; (3) photoelectric effect with fluorescence x-ray emission and re-absorption; (4) photoelectric effect with fluorescence x-ray emission which leaves PCD completely; and (5) electric noise. The model produced total detector spectrum similar to previous MC simulation data. The model can be used to predict spectrum and correlation with various different settings. The simulated noisy data demonstrated the expected performance: (a) data were integers; (b) the mean and covariance matrix was close to the target values; (c) noisy data generation was very efficient

Using an energy-resolving photon counting detector, the amount of k-edge material in the x-ray path can be estimated using a process known as material decomposition. However, non-ideal effects within the detector make it difficult to accurately perform this decomposition. This work evaluated the k-edge material decomposition accuracy of two empirical estimators. A neural network estimator and a linearized maximum likelihood estimator with error look-up tables (A-table method) were evaluated through simulations and experiments. Each estimator was trained on system-specific calibration data rather than specific modeling of non-ideal detector effects or the x-ray source spectrum. Projections through a step-wedge calibration phantom consisting of different path lengths through PMMA, aluminum, and a k-edge material was used to train the estimators. The estimators were tested by decomposing data acquired through different path lengths of the basis materials. The estimators had similar performance in the chest phantom simulations with gadolinium. They estimated four of the five densities of gadolinium with less than 2mg/mL bias. The neural networks estimates demonstrated lower bias but higher variance than the A-table estimates in the iodine contrast agent simulations. The neural networks had an experimental variance lower than the CRLB indicating it is a biased estimator. In the experimental study, the k-edge material contribution was estimated with less than 14% bias for the neural network estimator and less than 41% bias for the A-table method.

This study was performed to test the feasibility of multi-gamma-source CT imaging system. Gamma-source CT employs radioisotopes that emit monochromatic energy gamma-rays. The advantages of gamma-source CT include its immunity to beam hardening artifacts, its capacity of quantitative CT imaging, and its higher performance in low contrast imaging compared to the conventional x-ray CT. Radioisotope should be shielded by use of a pin-hole collimator so as to make a fine focal spot. Due to its low gamma-ray flux in general, the reconstructed image from a single gamma-source CT would suffer from high noise in data. To address this problem, we proposed a multi-gamma source CT imaging system and developed an iterative image reconstruction algorithm accordingly in this work. Conventional imaging model assumes a single linear imaging system typically represented by Mf = g. In a multi-gamma-source CT system however, the inversion problem is not any more based on a single linear system since one cannot separate a detector pixel value into multiple ones that are corresponding to each rays from the sources. Instead, the imaging model can be constructed by a set of linear system models each of which assumes an estimated measurement g. Based on this model, the proposed algorithm has a weighting step which distributes each projection data into multiple estimated measurements. We used two gamma sources at various positions and with varying intensities in this numerical study to demonstrate its feasibility. Therefore, the measured projection data(g) is separated into each estimated projection data(g₁, g₂) in this study. The proposed imaging protocol is believed to contribute to both medical and industrial applications.

X-ray fluorescence tomography (XFCT) has potential for high-resolution 3D molecular x-ray bio-imaging. In this technique the fluorescence signal from targeted nanoparticles (NPs) is measured, providing information about the spatial distribution and concentration of the NPs inside the object. However, present laboratory XFCT systems typically have limited spatial resolution (>1 mm) and suffer from long scan times and high radiation dose even at high NP concentrations, mainly due to low efficiency and poor signal-to-noise ratio. We have developed a laboratory XFCT system with high spatial resolution (sub-100 μm), low NP concentration and vastly decreased scan times and dose, opening up the possibilities for in-vivo small-animal imaging research. The system consists of a high-brightness liquid-metal-jet microfocus x-ray source, x-ray focusing optics and an energy-resolving photon-counting detector. By using the source’s characteristic 24 keV line-emission together with carefully matched molybdenum nanoparticles the Compton background is greatly reduced, increasing the SNR. Each measurement provides information about the spatial distribution and concentration of the Mo nanoparticles. A filtered back-projection method is used to produce the final XFCT image.

A small animal micro-CT system was built using an EMCCD detectors having complex pre-digitization amplification technology, high-resolution, high-sensitivity and low-noise. Noise in CBCT reconstructed images when using predigitization amplification behaves differently than commonly used detectors and warrants a detailed investigation. In this study, noise power and contrast sensitivity were estimated for the newly built system. Noise analysis was performed by scanning a water phantom. Tube voltage was lowered to minimum delivered by the tube (20 kVp and 0.5 mA) and detector gain was varied. Contrast sensitivity was analyzed by using a phantom containing different iodine contrast solutions (20% to 70%) filled in six different tubes. First, we scanned the phantom using various x-ray exposures at 40 kVp while changing the gain to maintain the background air value of the projection images constant. Next, the exposure was varied while the detector gain was maintained constant. Radial NPS plots show that noise power level increases as gain increases. Contrast sensitivity was analyzed by calculating ratio of signal-to-noise ratios (SNR) for increased gain with those of low constant gain at each exposure. The SNR value at low constant gain was always lower than SNR of high detector gain at all x-ray settings and iodine contrast. The largest increase of SNR approached 1.3 for low contrast feature for an iodine concentration of 20%. Despite an increase in noise level as gain increases, the SNR improvement shows that signal level also increases because of the unique on-chip gain of the detector.

Proton radiography shows great promise as a tool to guide proton beam therapy (PBT) in real time. Here, we demonstrate two ways in which the technology may progress towards that goal. Firstly, with a proton beam that is 800 MeV in energy, target tissue receives a dose of radiation with very tight lateral constraint. This could present a benefit over the traditional treatment energies of ~200 MeV, where up to 1 cm of lateral tissue receives scattered radiation at the target. At 800 MeV, the beam travels completely through the object with minimal deflection, thus constraining lateral dose to a smaller area. The second novelty of this system is the utilization of magnetic quadrupole refocusing lenses that mitigate the blur caused by multiple Coulomb scattering within an object, enabling high resolution imaging of thick objects, such as the human body. This system is demonstrated on ex vivo salamander and zebrafish specimens, as well as on a realistic hand phantom. The resulting images provide contrast sufficient to visualize thin tissue, as well as fine detail within the target volumes, and the ability to measure small changes in density. Such a system, combined with PBT, would enable the delivery of a highly specific dose of radiation that is monitored and guided in real time.

Scanning-beam digital x-ray (SBDX) is an inverse geometry x-ray fluoroscopy system capable of tomosynthesis-based 3D catheter tracking. This work proposes a method of dose-reduced 3D tracking using dynamic electronic collimation (DEC) of the SBDX scanning x-ray tube. Positions in the 2D focal spot array are selectively activated to create a regionof- interest (ROI) x-ray field around the tracked catheter. The ROI position is updated for each frame based on a motion vector calculated from the two most recent 3D tracking results. The technique was evaluated with SBDX data acquired as a catheter tip inside a chest phantom was pulled along a 3D trajectory. DEC scans were retrospectively generated from the detector images stored for each focal spot position. DEC imaging of a catheter tip in a volume measuring 11.4 cm across at isocenter required 340 active focal spots per frame, versus 4473 spots in full-FOV mode. The dose-area-product (DAP) and peak skin dose (PSD) for DEC versus full field-of-view (FOV) scanning were calculated using an SBDX Monte Carlo simulation code. DAP was reduced to 7.4% to 8.4% of the full-FOV value, consistent with the relative number of active focal spots (7.6%). For image sequences with a moving catheter, PSD was 33.6% to 34.8% of the full-FOV value. The root-mean-squared-deviation between DEC-based 3D tracking coordinates and full-FOV 3D tracking coordinates was less than 0.1 mm. The 3D distance between the tracked tip and the sheath centerline averaged 0.75 mm. Dynamic electronic collimation can reduce dose with minimal change in tracking performance.

Small-angle x-ray scattering (SAXS) imaging is an emerging medical tool that can be used for in vivo detailed tissue characterization and has the potential to provide added contrast to conventional x-ray projection and CT imaging. We used a publicly available MC-GPU code to simulate x-ray trajectories in a SAXS-CT geometry for a target material embedded in a water background material with varying sample sizes (1, 3, 5, and 10 mm). Our target materials were water solution of gold nanoparticle (GNP) spheres with a radius of 6 nm and a water solution with dissolved serum albumin (BSA) proteins due to their well-characterized scatter profiles at small angles and highly scattering properties. The background material was water. Our objective is to study how the reconstructed scatter profile degrades at larger target imaging depths and increasing sample sizes. We have found that scatter profiles of the GNP in water can still be reconstructed at depths up to 5 mm embedded at the center of a 10 mm sample. Scatter profiles of BSA in water were also reconstructed at depths up to 5 mm in a 10 mm sample but with noticeable signal degradation as compared to the GNP sample. This work presents a method to study the sample size limits for future SAXS-CT imaging systems.

It is known that conventional x-ray imaging provides a maximum contrast between cancerous and healthy fibroglandular breast tissues of 3% based on their linear x-ray attenuation coefficients at 17.5 keV, whereas coherent scatter signal provides a maximum contrast of 19% based on their differential coherent scatter cross sections. Therefore in order to exploit this potential contrast, we seek to evaluate the performance of a coded- aperture coherent scatter imaging system for breast cancer detection and investigate its accuracy using Monte Carlo simulations. In the simulations we modeled our experimental system, which consists of a raster-scanned pencil beam of x-rays, a bismuth-tin coded aperture mask comprised of a repeating slit pattern with 2-mm periodicity, and a linear-array of 128 detector pixels with 6.5-keV energy resolution. The breast tissue that was scanned comprised a 3-cm sample taken from a patient-based XCAT breast phantom containing a tomosynthesis- based realistic simulated lesion. The differential coherent scatter cross section was reconstructed at each pixel in the image using an iterative reconstruction algorithm. Each pixel in the reconstructed image was then classified as being either air or the type of breast tissue with which its normalized reconstructed differential coherent scatter cross section had the highest correlation coefficient. Comparison of the final tissue classification results with the ground truth image showed that the coded aperture imaging technique has a cancerous pixel detection sensitivity (correct identification of cancerous pixels), specificity (correctly ruling out healthy pixels as not being cancer) and accuracy of 92.4%, 91.9% and 92.0%, respectively. Our Monte Carlo evaluation of our experimental coded aperture coherent scatter imaging system shows that it is able to exploit the greater contrast available from coherently scattered x-rays to increase the accuracy of detecting cancerous regions within the breast.

Coded aperture X-ray diffraction (coherent scatter spectral) imaging provides fast and dose-efficient measurements of the molecular structure of an object. The information provided is spatially-dependent and material-specific, and can be utilized in medical applications requiring material discrimination, such as tumor imaging. However, current coded aperture coherent scatter spectral imaging system assume a uniformly or weakly attenuating object, and are plagued by image degradation due to non-uniform self-attenuation. We propose accounting for such non-uniformities in the self-attenuation by utilizing an X-ray computed tomography (CT) image (reconstructed attenuation map). In particular, we present an iterative algorithm for coherent scatter spectral image reconstruction, which incorporates the attenuation map, at different stages, resulting in more accurate coherent scatter spectral images in comparison to their uncorrected counterpart. The algorithm is based on a spectrally grouped edge-preserving regularizer, where the neighborhood edge weights are determined by spatial distances and attenuation values.

Purpose: This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison. Methods: We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index (d’). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom. Results: The task-driven design yielded the best performance, improving d’ by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d’ by 16% and 9%, respectively. Conclusions: This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

Reader studies are important in assessing breast imaging systems. The purpose of this work was to assess task-based performance of full field digital mammography (FFDM), digital breast tomosynthesis (DBT), and synthetic mammography (SM) using different phantom types, and to determine an accurate observer model for human readers.

Images were acquired on a Hologic Selenia Dimensions system with a uniform and anthropomorphic phantom. A contrast detail insert of small, low-contrast disks was created using an inkjet printer with iodine-doped ink and inserted in the phantoms. The disks varied in diameter from 210 to 630 μm, and in contrast from 1.1% contrast to 2.2% in regular increments. Human and model observers performed a 4-alternative forced choice experiment. The models were a non-prewhitening matched filter with eye model (NPWE) and a channelized Hotelling observer with either Gabor channels (Gabor-CHO) or Laguerre-Gauss channels (LG-CHO).

With the given phantoms, reader scores were higher in FFDM and DBT than SM. The structure in the phantom background had a bigger impact on outcome for DBT than for FFDM or SM. All three model observers showed good correlation with humans in the uniform background, with ρ between 0.89 and 0.93. However, in the structured background, only the CHOs had high correlation, with ρ=0.92 for Gabor-CHO, 0.90 for LG-CHO, and 0.77 for NPWE.

Because results of any analysis can depend on the phantom structure, conclusions of modality performance may need to be taken in the context of an appropriate model observer and a realistic phantom.

The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images.

Although digital mammography has reduced breast cancer mortality by approximately 30%, sensitivity and specificity are still far from perfect. In particular, the performance of mammography is especially limited for women with dense breast tissue. Two out of every three biopsies performed in the U.S. are unnecessary, thereby resulting in increased patient anxiety, pain, and possible complications. One promising tomographic breast imaging method that has recently been approved by the FDA is dedicated breast computed tomography (BCT). However, visualizing lesions with BCT can still be challenging for women with dense breast tissue due to the minimal contrast for lesions surrounded by fibroglandular tissue. In recent years there has been renewed interest in improving lesion conspicuity in x-ray breast imaging by administration of an iodinated contrast agent. Due to the fully 3-D imaging nature of BCT, as well as sub-optimal contrast enhancement while the breast is under compression with mammography and breast tomosynthesis, dedicated BCT of the uncompressed breast is likely to offer the best solution for injected contrast-enhanced x-ray breast imaging. It is well known that use of statistically-based iterative reconstruction in CT results in improved image quality at lower radiation dose. Here we investigate possible improvements in image reconstruction for BCT, by optimizing free regularization parameter in method of maximum likelihood and comparing its performance with clinical cone-beam filtered backprojection (FBP) algorithm.

In x-ray computed tomography (CT), task-based image quality studies are typically performed using uniform background phantoms with low-contrast signals. Such studies may have limited clinical relevancy for modern non-linear CT systems due to possible influence of background texture on image quality. The purpose of this study was to design and implement anatomically informed textured phantoms for task-based assessment of low-contrast detection. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find the CLB parameters that were most reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, a cylinder phantom (165 mm in diameter and 30 mm height) was designed, containing 20 low-contrast spherical signals (6 mm in diameter at targeted contrast levels of ~3.2, 5.2, 7.2, 10, and 14 HU, 4 repeats per signal). The phantom was voxelized and input into a commercial multi-material 3D printer (Object Connex 350), with custom software for voxel-based printing. Using principles of digital half-toning and dithering, the 3D printer was programmed to distribute two base materials (VeroWhite and TangoPlus, nominal voxel size of 42x84x30 microns) to achieve the targeted spatial distribution of x-ray attenuation properties. The phantom was used for task-based image quality assessment of a clinically available iterative reconstruction algorithm (Sinogram Affirmed Iterative Reconstruction, SAFIRE) using a channelized Hotelling observer paradigm. Images of the textured phantom and a corresponding uniform phantom were acquired at six dose levels and observer model performance was estimated for each condition (5 contrasts x 6 doses x 2 reconstructions x 2 backgrounds = 120 total conditions). Based on the observer model results, the dose reduction potential of SAFIRE was computed and compared between the uniform and textured phantom. The dose reduction potential of SAFIRE was found to be 23% based on the uniform phantom and 17% based on the textured phantom. This discrepancy demonstrates the need to consider background texture when assessing non-linear reconstruction algorithms.

In previous work we developed a method for predicting the minimum detectable change (MDC) in nodule volume based on volumetric CT measurements. MDC was defined as the minimum increase/decrease in a nodule volume distinguishable from the baseline measurement at a specified level of detection performance, assessed using the area under the ROC curve (AUC). In this work we derived volume estimates of a set of synthetic nodules and calculated the detection performance for distinguishing them from baseline measurements. Eight spherical objects of 100HU radio density ranging in diameter from 5.0mm to 5.75mm and 8.0mm to 8.75mm with 0.25mm increments were placed in an anthropomorphic phantom with either no background (high-contrast task) or gelatin background (low-contrast task). The baseline was defined as 5.0mm for the first set of nodules and 8.0mm for the second set. The phantom was scanned using varying exposures, and reconstructed with slice thickness of 0.75, 1.5, and 3.0mm and two reconstruction kernels (standard and smooth). Volume measurements were derived using a previously developed matched- filter approach. Results showed that nodule size, slice thickness, and nodule-to-background contrast affected detectable change in nodule volume when using our volume estimator and the acquisition settings from our study. We also compared our experimental results to the values estimated by our previously-developed MDC prediction method. We found that experimental data for the 8mm baseline nodules matched very well with our predicted values of MDC. These results support considering the use of this metric when standardizing imaging protocols for lung nodule size change assessment.

To facilitate rigorous virtual clinical trials using model observers for breast imaging optimization and evaluation, we demonstrated a method of defining statistical models, based on 177 sets of breast CT patient data, in order to generate tens of thousands of unique digital breast phantoms. In order to separate anatomical texture from variation in breast shape, each training set of breast phantoms were deformed to a consistent atlas compressed geometry. Principal component analysis (PCA) was then performed on the shape-matched breast CT volumes to capture the variation of patient breast textures. PCA decomposes the training set of N breast CT volumes into an N-1-dimensional space of eigenvectors, which we call eigenbreasts. By summing weighted combinations of eigenbreasts, a large ensemble of different breast phantoms can be newly created. Different training sets can be used in eigenbreast analysis for designing basis models to target sub-populations defined by breast characteristics, such as size or density. In this work, we plan to generate ensembles of 30,000 new phantoms based on glandularity for an upcoming virtual trial of lesion detectability in digital breast tomosynthesis. Our method extends our series of digital and physical breast phantoms based on human subject anatomy, providing the capability to generate new, unique ensembles consisting of tens of thousands or more virtual subjects. This work represents an important step towards conducting future virtual trials for tasks-based assessment of breast imaging, where it is vital to have a large ensemble of realistic phantoms for statistical power as well as clinical relevance.

A new 3D breast computed tomography (CT) system is under development enabling imaging of microcalcifications in a fully uncompressed breast including posterior chest wall tissue. The system setup uses a steered electron beam impinging on small tungsten targets surrounding the breast to emit X-rays. A realization of the corresponding detector concept is presented in this work and it is modeled through Monte Carlo simulations in order to quantify first characteristics of transmission and secondary photons. The modeled system comprises a vertical alignment of linear detectors hold by a case that also hosts the breast. Detectors are separated by gaps to allow the passage of X-rays towards the breast volume. The detectors located directly on the opposite side of the gaps detect incident X-rays. Mechanically moving parts in an imaging system increase the duration of image acquisition and thus can cause motion artifacts. So, a major advantage of the presented system design is the combination of the fixed detectors and the fast steering electron beam which enable a greatly reduced scan time. Thereby potential motion artifacts are reduced so that the visualization of small structures such as microcalcifications is improved. The result of the simulation of a single projection shows high attenuation by parts of the detector electronics causing low count levels at the opposing detectors which would require a flat field correction, but it also shows a secondary to transmission ratio of all counted X-rays of less than 1 percent. Additionally, a single slice with details of various sizes was reconstructed using filtered backprojection. The smallest detail which was still visible in the reconstructed image has a size of 0.2mm.

Virtual clinical trials are a promising new approach increasingly used for the evaluation and comparison of breast imaging modalities. A key component in such an assessment paradigm is the use of simulated pathology, in particular, simulation of lesions. Breast mass lesions can be generally classified into two categories based on their appearance; nonspiculated masses and spiculated masses. In our previous work, we have successfully simulated non-spiculated masses using a fractal growth process known as diffusion limited aggregation. In this new work, we have extended the DLA model to simulate spiculated lesions by using features extracted from patient DBT images containing spiculated lesions. The features extracted included spicule length, width, curvature and distribution. This information was used to simulate realistic looking spicules which were attached to the surface of a DLA mass to produce a spiculated mass. A batch of simulated spiculated masses was inserted into normal patient images and presented to an experienced radiologist for review. The study yielded promising results with the radiologist rating 60% of simulated lesions in 2D and 50% of simulated lesions in DBT as realistic.

Energy resolved photon-counting mammography is a new technology, which counts the number of photons that passes through an object, and presents it as a pixel value in an image of the object. Silicon semiconductor detectors are currently used in commercial mammography. However, the disadvantage of silicon is the low absorption efficiency for high X-ray energies. A cadmium telluride (CdTe) series detector has a high absorption efficiency over a wide energy range. In this study, we proposed a method to estimate the composition of the mammary gland using a CdTe series detector as a photon-counting detector. The fact that the detection rate of breast cancer in mammography is affected by mammary gland composition is now widely accepted. Assessment of composition of the mammary gland has important implications. An important advantage of our proposed technique is its ability to discriminate photons using three energy bins. We designed the CdTe series detector system using the MATLAB simulation software. The phantom contains nine regions with the ratio of glandular tissue and adipose varying in increments of 10%. The attenuation coefficient for each bin’s energy was calculated from the number of input and output photons possessed by each. The evaluation results obtained by plotting the attenuation coefficient μ in a three-dimensional (3D) scatter plot show that the plots had a regular composition order congruent with that of the mammary gland. Consequently, we believe that our proposed method can be used to estimate the composition of the mammary gland.

We propose a new mammography system using a cadmium telluride (CdTe) series photon-counting detector, having high absorption efficiency over a wide energy range. In a previous study, we showed that the use of high X-ray energy in digital mammography is useful from the viewpoint of exposure dose and image quality. In addition, the CdTe series detector can acquire X-ray spectrum information following transmission through a subject. This study focused on the tissue composition identified using spectral information obtained by a new photon-counting detector. Normal breast tissue consists entirely of adipose and glandular tissues. However, it is very difficult to find tumor tissue in the region of glandular tissue via a conventional mammogram, especially in dense breast because the attenuation coefficients of glandular tissue and tumor tissue are very close. As a fundamental examination, we considered a simulation phantom and showed the difference between normal breast tissue and tumor tissue of various thicknesses in a three-dimensional (3D) scatter plot. We were able to discriminate between both types of tissues. In addition, there was a tendency for the distribution to depend on the thickness of the tumor tissue. Thinner tumor tissues were shown to be closer in appearance to normal breast tissue. This study also demonstrated that the difference between these tissues could be made obvious by using a CdTe series detector. We believe that this differentiation is important, and therefore, expect this technology to be applied to new tumor detection systems in the future.

In this work we present the MAMMOCARE prototype, a biopsy guided system based on PET. The system is composed by an examination table where the patient is situated in prone position, a PET detector and a biopsy device. The PET detector is composed by two rings. These rings can be separated mechanically in order to allow the needle insertion. The first acquisition is performed with the closed ring configuration in order to obtain a high quality image to locate the lesion. Then, the software calculates the optimum path for the biopsy and moves the biopsy and PET systems to the desired position. At this point, two compression pallets are used to hold the breast. Then, the PET system opens and the biopsy procedure starts. The images are obtained at several steps to ensure the correct location of the needle during the procedure. The performance of the system is evaluated measuring the spatial resolution and sensitivity according the NEMA standard. The uniformity of the reconstructed images is also estimated. The radial resolution is 1.62mm in the center of the FOV and 3.45mm at 50mm off the center in the radial direction using the closed configuration. In the open configuration the resolution reaches 1.85mm at center and 3.65mm at 50mm. The sensitivity using an energy window of 250keV-750keV is 3.6% for the closed configuration and 2.5% for the open configuration. The uniformity measured in the center of the FOV is 14% and 18% for the closed and open configurations respectively.

Breast cancer is one of the most common malignancies in women. For years, mammography has been used as the gold standard for localizing breast cancer, despite its limitation in determining cancer composition. Therefore, the purpose of this simulation study is to confirm the feasibility of obtaining tumor composition using dual energy digital mammography. To generate X-ray sources for dual energy mammography, 26 kVp and 39 kVp voltages were generated for low and high energy beams, respectively. Additionally, the energy subtraction and inverse mapping functions were applied to provide compositional images. The resultant images showed that the breast composition obtained by the inverse mapping function with cubic fitting achieved the highest accuracy and least noise. Furthermore, breast density analysis with cubic fitting showed less than 10% error compare to true values. In conclusion, this study demonstrated the feasibility of creating individual compositional images and capability of analyzing breast density effectively.

The photon counting detector with energy discrimination capabilities provides the spectral information and energy of each photon with single exposure. The energy-resolved photon counting detector makes it possible to improve the visualization of contrast agent by selecting the appropriate energy window. In this study, we simulated the photon counting spectral mammography system using a Monte Carlo method and compared three contrast enhancement methods (K-edge imaging, projection-based energy weighting imaging, and dual energy subtraction imaging). For the quantitative comparison, we used the homogeneous cylindrical breast phantom as a reference and the heterogeneous XCAT breast phantom. To evaluate the K-edge imaging methods, we obtained images by increasing the energy window width based on K-edge absorption energy of iodine. The iodine which has the K-edge discontinuity in the attenuation coefficient curve can be separated from the background. The projection-based energy weighting factor was defined as the difference in the transmissions between the contrast agent and the background. Each weighting factor as a function of photon energy was calculated and applied to the each energy bin. For the dual energy subtraction imaging, we acquired two images with below and above the iodine K-edge energy using single exposure. To suppress the breast tissue in high energy images, the weighting factor was applied as the ratio of the linear attenuation coefficients of the breast tissue at high and low energies. Our results demonstrated the CNR improvement of the K-edge imaging was the highest among the three methods. These imaging techniques based on the energy-resolved photon counting detector improved image quality with the spectral information.

This work investigated the effect of the grid-less acquisition mode with scatter correction software developed by Siemens Healthcare (PRIME mode) on image quality and mean glandular dose (MGD) in a comparative study against a standard mammography system with grid. Image quality was technically quantified with contrast-detail (c-d) analysis and by calculating detectability indices (d’) using a non-prewhitening with eye filter model observer (NPWE). MGD was estimated technically using slabs of PMMA and clinically on a set of 11439 patient images. The c-d analysis gave similar results for all mammographic systems examined, although the d’ values were slightly lower for the system with PRIME mode when compared to the same system in standard mode (-2.8% to -5.7%, depending on the PMMA thickness). The MGD values corresponding to the PMMA measurements with automatic exposure control indicated a dose reduction from 11.0% to 20.8% for the system with PRIME mode compared to the same system without PRIME mode. The largest dose reductions corresponded to the thinnest PMMA thicknesses. The results from the clinical dosimetry study showed an overall population-averaged dose reduction of 11.6% (up to 27.7% for thinner breasts) for PRIME mode compared to standard mode for breast thicknesses from 20 to 69 mm. These technical image quality measures were then supported using a clinically oriented study whereby simulated clusters of microcalcifications and masses were inserted into patient images and read by radiologists in an AFROC study to quantify their detectability. In line with the technical investigation, no significant difference was found between the two imaging modes (p-value 0.95).

This study aims to analyze the agreement between the mean glandular dose estimated by the mammography unit (organ dose) and mean glandular dose calculated using Dance et al published method (calculated dose). Anonymised digital mammograms from 50 BreastScreen NSW centers were downloaded and exposure information required for the calculation of dose was extracted from the DICOM header along with the organ dose estimated by the system. Data from quality assurance annual tests for the included centers were collected and used to calculate the mean glandular dose for each mammogram. Bland-Altman analysis and a two-tailed paired t-test were used to study the agreement between calculated and organ dose and the significance of any differences. A total of 27,869 dose points from 40 centers were included in the study, mean calculated dose and mean organ dose (± standard deviation) were 1.47 (±0.66) and 1.38 (±0.56) mGy respectively. A statistically significant 0.09 mGy bias (t = 69.25; p<0.0001) with 95% limits of agreement between calculated and organ doses ranging from -0.34 and 0.52 were shown by Bland-Altman analysis, which indicates a small yet highly significant difference between the two means. The use of organ dose for dose audits is done at the risk of over or underestimating the calculated dose, hence, further work is needed to identify the causal agents for differences between organ and calculated doses and to generate a correction factor for organ dose.

The scatter effect on detective quantum efficiency (DQE) of digital mammography is investigated using the cascaded-systems model. The cascaded-systems model includes a scatter-reduction device as a binomial selection stage. Quantum-noise-limited operation approximates the system DQE into the multiplication form of the scatter-reduction device DQE and the conventional detector DQE. The developed DQE model is validated in comparisons with the measured results using a CMOS flat-panel detector under scatter environments. For various scatter-reduction devices, the slot-scan method shows the best scatter-cleanup performance in terms of DQE, and the scatter-cleanup performance of the conventional one-dimensional grid is rather worse than the air gap. The developed model can also be applied to general radiography and will be very useful for a better design of imaging chain.

Screening for breast cancer using mammography has been very successful in the effort to reduce breast cancer mortality, and its use has largely resulted in the 30% reduction in breast cancer mortality observed since 1990 [1]. However, diagnostic mammography remains an area of breast imaging that is in great need for improvement. One imaging modality proposed for improving the accuracy of diagnostic workup is iodinated contrast-enhanced breast CT [2]. In this study, a mathematical framework is used to evaluate optimal exposure techniques for contrast-enhanced breast CT. The ideal observer signal-to-noise ratio (i.e., d’) figure-of-merit is used to provide a task performance based assessment of optimal acquisition parameters under the assumptions of a linear, shift-invariant imaging system. A parallel-cascade model was used to estimate signal and noise propagation through the detector, and a realistic lesion model with iodine uptake was embedded into a structured breast background. Ideal observer performance was investigated across kVp settings, filter materials, and filter thickness. Results indicated many kVp spectra/filter combinations can improve performance over currently used x-ray spectra.

Anthropomorphic software breast phantoms have been utilized for preclinical quantitative validation of breast imaging systems. Efficacy of the simulation-based validation depends on the realism of phantom images. Anatomical measurements of the breast tissue, such as the size and distribution of adipose compartments or the thickness of Cooper’s ligaments, are essential for the realistic simulation of breast anatomy. Such measurements are, however, not readily available in the literature. In this study, we assessed the statistics of adipose compartments as visualized in CT images of a total mastectomy specimen. The specimen was preserved in formalin, and imaged using a standard body CT protocol and high X-ray dose. A human operator manually segmented adipose compartments in reconstructed CT images using ITK-SNAP software, and calculated the volume of each compartment. In addition, the time needed for the manual segmentation and the operator’s confidence were recorded. The average volume, standard deviation, and the probability distribution of compartment volumes were estimated from 205 segmented adipose compartments. We also estimated the potential correlation between the segmentation time, operator’s confidence, and compartment volume. The statistical tests indicated that the estimated compartment volumes do not follow the normal distribution. The compartment volumes are found to be correlated with the segmentation time; no significant correlation between the volume and the operator confidence. The performed study is limited by the mastectomy specimen position. The analysis of compartment volumes will better inform development of more realistic breast anatomy simulation.

In order to improve efficacy of screening mammography, in recent years, we have been investigating the feasibility of applying a resonance-frequency based electrical impedance spectroscopy (REIS) technology to noninvasively detect breast abnormalities that may lead to the development of cancer in the near-term. Despite promising study-results, we found that REIS suffered from relatively poor reproducibility due to perturbations in electrode placement, contact pressure variation on the breast, as well as variation of the resonating inductor. To overcome this limitation, in this study, we propose and analyze a new paradigm of Dielectric Relaxation Spectroscopy (DRS) that measures polarization-lag of dielectric signals in breast-capacitance when excited by the pulses or sine waves. Unlike conventional DRS that operates using the signals at very high frequencies (GHz) to examine changes in polarization, our new method detects and characterizes the dielectric properties of tissue at low frequencies (≤10 MHz) due to the advent of inexpensive oscillators that are accurate to 1 pico-second (used in GPS receivers) as well as measurement of amplitudes of 1 ppm or better. From theoretical analysis, we have proved that the sensitivity of new DRS in detecting permittivity of water increased by ≥80 times as compared to conventional DRS, which operates at frequencies around 4GHz. By analyzing and comparing the relationship between the new DRS and REIS, we found that this DRS has potential advantages in enhancing repeatability from various readings, including temperature-insensitive detection, and yielding higher resolution or sensitivity (up to 100 Femtofarads).

Anthropomorphic software breast phantoms have been introduced as a tool for quantitative validation of breast imaging systems. Efficacy of the validation results depends on the realism of phantom images. The recursive partitioning algorithm based upon the octree simulation has been demonstrated as versatile and capable of efficiently generating large number of phantoms to support virtual clinical trials of breast imaging. Previously, we have observed specific artifacts, (here labeled “dents”) on the boundaries of simulated Cooper’s ligaments. In this work, we have demonstrated that these “dents” result from the approximate determination of the closest simulated ligament to an examined subvolume (i.e., octree node) of the phantom. We propose a modification of the algorithm that determines the closest ligament by considering a pre-specified number of neighboring ligaments selected based upon the functions that govern the shape of ligaments simulated in the subvolume. We have qualitatively and quantitatively demonstrated that the modified algorithm can lead to elimination or reduction of dent artifacts in software phantoms. In a proof-of concept example, we simulated a 450 ml phantom with 333 compartments at 100 micrometer resolution. After the proposed modification, we corrected 148,105 dents, with an average size of 5.27 voxels (5.27nl). We have also qualitatively analyzed the corresponding improvement in the appearance of simulated mammographic images. The proposed algorithm leads to reduction of linear and star-like artifacts in simulated phantom projections, which can be attributed to dents. Analysis of a larger number of phantoms is ongoing.

Purpose To evaluate the radiation dose derived from digital mammography (DM) and digital breast tomosynthesis (DBT) at different tube current-exposure time product (mAs) and at 6 phantom thicknesses from 10 to 60 mm. Materials and Methods A total of 240 DM and DBT cranio-caudal (CC) phantom images were acquired at each thickness and at four exposure levels (the baseline mAs, 50%, 25% and 12.5% the baseline mAs). The incident Air Kerma (K) at the surface of the phantoms was measured using a solid state dosimeter. Mean Glandular Doses (MGD) were calculated for both modalities (DM and DBT). Results DBT dose was greater than that of DM for all mAs at each phantom thickness. For a breast thickness of 50 mm (close to average sized breast), the dose for DBT (2.32 mGy) was 13% higher than that for DM (2.05 mGy). The results also show that the difference in MGD between DM and DBT was less for the thicker compared with the thinner phantom, this difference being approximately a factor of 2.58 at 10 mm compared with a factor of 1.08 at 60 mm. While the MGD increased with increasing phantom thickness for both modalities, the dose increase with DBT was less than for DM, with the difference between 10 and 60 mm being a factor of 7 for DM and 3 for DBT. Conclusion The radiation dose from DBT was higher than that of DM and the difference in dose between DM and DBT decreases as phantom thickness increases.

Our earlier work has demonstrated that the data consistency condition can be used as a criterion for scatter kernel optimization in deconvolution methods in a full-fan mode cone-beam CT [1]. However, this scheme cannot be directly applied to CBCT system with an offset detector (half-fan mode) because of transverse data truncation in projections. In this study, we proposed a modified scheme of the scatter kernel optimization method that can be used in a half-fan mode cone-beam CT, and have successfully shown its feasibility. Using the first-reconstructed volume image from half-fan projection data, we acquired full-fan projection data by forward projection synthesis. The synthesized full-fan projections were partly used to fill the truncated regions in the half-fan data. By doing so, we were able to utilize the existing data consistency-driven scatter kernel optimization method. The proposed method was validated by a simulation study using the XCAT numerical phantom and also by an experimental study using the ACS head phantom.

A high spatial resolution iterative reconstruction algorithm is proposed for a half cone beam CT (HCBCT) geometry. The proposed algorithm improves spatial resolution by explicitly accounting for image blurriness caused by different factors, such as extended X-ray source and detector response. The blurring kernel is estimated using the MTF slice of the Catphan phantom. The proposed algorithm is specifically optimized for the new Leksell Gamma Knife Icon (Elekta AB, Stockholm, Sweden) which incorporates the HCBCT geometry to accommodate the existing treatment couch while covering down to the base-of-skull in the reconstructed field-of-view. Image reconstruction involves a Fourier-based scaling simultaneous algebraic reconstruction technique (SART) coupled with total variation (TV) minimization and non-local mean denoising, solved using a split Bregman separation technique that splits the reconstruction problem into a gradient based updating step and a TV-based deconvolution algorithm. This formulation preserves edges and reduces the staircase effect caused by regular TV-penalized iterative algorithms. Our experiments indicate that our proposed method outperforms the conventional filtered back projection and TV penalized SART methods in terms of line pair resolution and retains the favorable properties of the standard TV-penalized reconstruction.

Cone-beam computed tomography (CBCT) has widely been used and studied in both medical imaging and radiation therapy. The aim of this study was to evaluate our newly developed CBCT system by implementing a distance-driven system modeling technique in order to produce excellent and accurate cross-sectional images. For the purpose of comparing the performance of the distance-driven methods, we also performed pixel-driven and ray-driven techniques when conducting forward- and back-projection schemes. We conducted the Feldkamp-Davis-Kress (FDK) algorithm and simultaneous algebraic reconstruction technique (SART) to retrieve a volumetric information of scanned chest phantom. The results indicated that contrast-to-noise (CNR) of the reconstructed images by using FDK and SART showed 8.02 and 15.78 for distance-driven, whereas 4.02 and 5.16 for pixel-driven scheme and 7.81 and 13.01 for ray-driven scheme, respectively. This could demonstrate that distance-driven method described more closely the chest phantom compared to pixel- and ray-driven. However, both elapsed time for modeling a system matrix and reconstruction time took longer time when performing the distance-driven scheme. Therefore, future works will be directed toward reducing computational time to acceptable limits for real applications.