This PDF file contains the editorial “JMI 2016 List of Reviewers” for JMI Vol. 4 Issue 01

This PDF file contains the editorial “Special Section Guest Editorial: Positron Emission Tomography: History, Current Status, and Future Prospects” for JMI Vol. 4 Issue 01

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was ≥0.5 (p<0.05), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast ≥0.5 and ≥0.25, respectively. For all other cases, there was no statistically significant difference between PL and OSEM (p>0.05). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Using analytical and Monte Carlo modeling, we explored performance of a lightweight wearable helmet-shaped brain positron emission tomography (PET), or BET camera, based on thin-film digital Geiger avalanche photodiode arrays with Lutetium-yttrium oxyorthosilicate (LYSO) or LaBr₃ scintillators for imaging in vivo human brain function of freely moving and acting subjects. We investigated a spherical cap BET and cylindrical brain PET (CYL) geometries with 250-mm diameter. We also considered a clinical whole-body (WB) LYSO PET/CT scanner. The simulated energy resolutions were 10.8% (LYSO) and 3.3% (LaBr₃), and the coincidence window was set at 2 ns. The brain was simulated as a water sphere of uniform F-18 activity with a radius of 100 mm. We found that BET achieved >40% better noise equivalent count (NEC) performance relative to the CYL and >800% than WB. For 10-mm-thick LaBr₃ equivalent mass systems, LYSO (7-mm thick) had ∼40% higher NEC than LaBr₃. We found that 1×1×3  mm scintillator crystals achieved ∼1.1  mm full-width-half-maximum spatial resolution without parallax errors. Additionally, our simulations showed that LYSO generally outperformed LaBr₃ for NEC unless the timing resolution for LaBr₃ was considerably smaller than that presently used for LYSO, i.e., well below 300 ps.

Positron emission tomography (PET) images are potential quantitative biomarkers. Understanding long-term (months/years) biomarker variability is important for establishing confidence intervals on studies using such biomarkers over these time frames. PET biomarkers are derived from activity concentration (ρ) extracted from PET images. Over 30 months, we measured the stability of decay-normalized counts (N_n) and ρ by scanning the same 4.5–cm-diameter Ge-68 cylinder weekly, the same Na-22 point source daily, and a refilled 20-cm F-18 cylinder phantom monthly on a clinical TOF-PET/CT scanner. Longitudinal and adjacent-measurement variability was characterized. We found no drift in ρ or N_n for properly calibrated images over 24 months. During this time, ρ_mean ranged ±5% to 6% for count-matched Ge-68 and F-18 images, with coefficient of variation (COV) across time of 2.3% (Ge-68, 81 scans) and 3.2% (F-18, 24 scans). At typical patient image count levels the Ge-68 ρ_mean (ρ_max) COV across time was 6.9% (9.6%). Changes in ρ_mean between adjacent F-18 scans (Δdays=34) ranged between ±10%, with corresponding date-matched changes in Ge-68 ρ_mean ranging ±2%. We recommend (1) tracking trends in N_n with image ρ as a check of quantitative data corrections/calibrations and (2) tracking both mean and COV of ρ (single time point measures) to hundredths precision using standardized uptake values.

The quality assurance of particle therapy treatment is a fundamental issue that can be addressed by developing reliable monitoring techniques and indicators of the treatment plan correctness. Among the available imaging techniques, positron emission tomography (PET) has long been investigated and then clinically applied to proton and carbon beams. In 2013, the Innovative Solutions for Dosimetry in Hadrontherapy (INSIDE) collaboration proposed an innovative bimodal imaging concept that combines an in-beam PET scanner with a tracking system for charged particle imaging. This paper presents the general architecture of the INSIDE project but focuses on the in-beam PET scanner that has been designed to reconstruct the particles range with millimetric resolution within a fraction of the dose delivered in a treatment of head and neck tumors. The in-beam PET scanner has been recently installed at the Italian National Center of Oncologic Hadrontherapy (CNAO) in Pavia, Italy, and the commissioning phase has just started. The results of the first beam test with clinical proton beams on phantoms clearly show the capability of the in-beam PET to operate during the irradiation delivery and to reconstruct on-line the beam-induced activity map. The accuracy in the activity distal fall-off determination is millimetric for therapeutic doses.

We report the development of a modularized compact positron emission tomography (PET) detector that outputs serial streams of digital samples of PET event pulses via an Ethernet interface using the UDP/IP protocol to enable rapid configuration of a PET system by connecting multiple such detectors via a network switch to a computer. Presently, the detector is 76  mm×50  mm×55  mm in extent (excluding I/O connectors) and contains an 18×12 array of 4.2×4.2×20  mm3 one-to-one coupled lutetium-yttrium oxyorthosilicate/silicon photomultiplier pixels. It employs cross-wire and stripline readouts to merge the outputs of the 216 detector pixels to 24 channels. Signals at these channels are sampled using a built-in 24-ch, 4-level field programmable gate arrays-only multivoltage threshold digitizer. In the computer, software programs are implemented to analyze the digital samples to extract event information and to perform energy qualification and coincidence filtering. We have developed two such detectors. We show that all their pixels can be accurately discriminated and measure a crystal-level energy resolution of 14.4% to 19.4% and a detector-level coincidence time resolution of 1.67 ns FWHM. Preliminary imaging results suggests that a PET system based on the detectors can achieve an image resolution of ∼1.6  mm.

Positron emission tomography (PET) scanners designed for imaging of small animals have transformed translational research by reducing the necessity to invasively monitor physiology and disease progression. Virtually all of these scanners are based on the use of pixelated detector modules arranged in rings. This design, while generally successful, has some limitations. Specifically, use of discrete detector modules to construct PET scanners reduces detection sensitivity and can introduce artifacts in reconstructed images, requiring the use of correction methods. To address these challenges, and facilitate measurement of photon depth-of-interaction in the detector, we investigated a small animal PET scanner (called AnnPET) based on a monolithic annulus of scintillator. The scanner was created by placing 12 flat facets around the outer surface of the scintillator to accommodate placement of silicon photomultiplier arrays. Its performance characteristics were explored using Monte Carlo simulations and sections of the NEMA NU4-2008 protocol. Results from this study revealed that AnnPET’s reconstructed spatial resolution is predicted to be ∼1  mm full width at half maximum in the radial, tangential, and axial directions. Peak detection sensitivity is predicted to be 10.1%. Images of simulated phantoms (mini-hot rod and mouse whole body) yielded promising results, indicating the potential of this system for enhancing PET imaging of small animals.

The next generation of discoveries in molecular imaging requires positron emission tomography (PET) systems with high spatial resolution and high sensitivity to visualize and quantify low concentrations of molecular probes. The goal of this work is to assemble and explore such a system. We use cadmium zinc telluride (CZT) to achieve high spatial resolution, three-dimensional interaction positioning, and excellent energy resolution. The CZT crystals are arranged in an edge-on configuration with a minimum gap of 50  μm in a four-sided panel geometry to achieve superior photon sensitivity. The developed CZT detectors and readout electronics were scaled up to complete significant portions of the final PET system. The steering electrode bias and the amplitude of the analog signals for time measurement were optimized to improve performance. The energy resolution (at 511 keV) over 468 channels is 7.43±1.02% full-width-at-half-maximum (FWHM). The spatial resolution is 0.76±0.1  mm FWHM. The time resolution of six CZT crystals in coincidence with six other CZT crystals is 37 ns. With high energy and spatial resolution and the relatively low random rate for small animal imaging, this system shows promise to be very useful for molecular imaging studies.

This paper presents an improved GrowCut (IGC), a positron emission tomography-based segmentation algorithm, and tests its clinical applicability. Contrary to the traditional method that requires the user to provide the initial seeds, the IGC algorithm starts with a threshold-based estimate of the tumor and a three-dimensional morphologically grown shell around the tumor as the foreground and background seeds, respectively. The repeatability of IGC from the same observer at multiple time points was compared with the traditional GrowCut algorithm. The algorithm was tested in 11 nonsmall cell lung cancer lesions and validated against the clinician-defined manual contour and compared against the clinically used 25% of the maximum standardized uptake value [SUV-(max)], 40% SUVmax, and adaptive threshold methods. The time to edit IGC-defined functional volume to arrive at the gross tumor volume (GTV) was compared with that of manual contouring. The repeatability of the IGC algorithm was very high compared with the traditional GrowCut (p=0.003) and demonstrated higher agreement with the manual contour with respect to threshold-based methods. Compared with manual contouring, editing the IGC achieved the GTV in significantly less time (p=0.11). The IGC algorithm offers a highly repeatable functional volume and serves as an effective initial guess that can well minimize the time spent on labor-intensive manual contouring.

We compare the performance of two detector materials, cadmium telluride (CdTe) and bismuth silicon oxide (BSO), for optical property modulation-based radiation detection method for positron emission tomography (PET), which is a potential new direction to dramatically improve the annihilation photon pair coincidence time resolution. We have shown that the induced current flow in the detector crystal resulting from ionizing radiation determines the strength of optical modulation signal. A larger resistivity is favorable for reducing the dark current (noise) in the detector crystal, and thus the higher resistivity BSO crystal has a lower (50% lower on average) noise level than CdTe. The CdTe and BSO crystals can achieve the same sensitivity under laser diode illumination at the same crystal bias voltage condition while the BSO crystal is not as sensitive to 511-keV photons as the CdTe crystal under the same crystal bias voltage. The amplitude of the modulation signal induced by 511-keV photons in BSO crystal is around 30% of that induced in CdTe crystal under the same bias condition. In addition, we have found that the optical modulation strength increases linearly with crystal bias voltage before saturation. The modulation signal with CdTe tends to saturate at bias voltages higher than 1500 V due to its lower resistivity (thus larger dark current) while the modulation signal strength with BSO still increases after 3500 V. Further increasing the bias voltage for BSO could potentially further enhance the modulation strength and thus, the sensitivity.

Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.

Maintaining excellent timing resolution in the generation of silicon photomultiplier (SiPM)-based time-of-flight positron emission tomography (TOF-PET) systems requires a large number of high-speed, high-bandwidth electronic channels and components. To minimize the cost and complexity of a system’s back-end architecture and data acquisition, many analog signals are often multiplexed to fewer channels using techniques that encode timing, energy, and position information. With progress in the development SiPMs having lower dark noise, after pulsing, and cross talk along with higher photodetection efficiency, a coincidence timing resolution (CTR) well below 200 ps FWHM is now easily achievable in single pixel, bench-top setups using 20-mm length, lutetium-based inorganic scintillators. However, multiplexing the output of many SiPMs to a single channel will significantly degrade CTR without appropriate signal processing. We test the performance of a PET detector readout concept that multiplexes 16 SiPMs to two channels. One channel provides timing information with fast comparators, and the second channel encodes both position and energy information in a time-over-threshold-based pulse sequence. This multiplexing readout concept was constructed with discrete components to process signals from a 4×4 array of SensL MicroFC-30035 SiPMs coupled to 2.9×2.9×20  mm3 Lu_1.8Gd_0.2SiO₅ (LGSO):Ce (0.025 mol. %) scintillators. This readout method yielded a calibrated, global energy resolution of 15.3% FWHM at 511 keV with a CTR of 198±2  ps FWHM between the 16-pixel multiplexed detector array and a 2.9×2.9×20  mm3 LGSO-SiPM reference detector. In summary, results indicate this multiplexing scheme is a scalable readout technique that provides excellent coincidence timing performance.

Instrumentation for positron emission tomography (PET) imaging has experienced tremendous improvements in performance over the past 60 years since it was first conceived as a medical imaging modality. Spatial resolution has improved by a factor of 10 and sensitivity by a factor of 40 from the early designs in the 1970s to the high-performance scanners of today. Multimodality configurations have emerged that combine PET with computed tomography (CT) and, more recently, with MR. Whole-body scans for clinical purposes can now be acquired in under 10 min on a state-of-the-art PET/CT. This paper will review the history of these technical developments over 40 years and summarize the important clinical research and healthcare applications that have been made possible by these technical advances. Some perspectives for the future of this technology will also be presented that promise to bring about new applications of this imaging modality in clinical research and healthcare.

A conventional three-dimensional/four-dimensional (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 filter designed to preserve bony edges while suppressing noise. Two-dimensional (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 0.856 for the 3D-DSA and 0.849 for the 4D-DSA, indicating that 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.

Our objective was to analyze the agreement between organ dose estimated by different digital mammography units and calculated dose for clinical data. Digital Imaging and Communication in Medicine header information was extracted from 52,405 anonymized mammograms. Data were filtered to include images with no breast implants, breast thicknesses 20 to 110 mm, and complete exposure and quality assurance data. Mean glandular dose was calculated using methods by Dance et al., Wu et al., and Boone et al. Bland–Altman analysis and regression were used to study the agreement and correlation between organ and calculated doses. Bland–Altman showed statistically significant bias between organ and calculated doses. The bias differed for different unit makes and models; Philips had the lowest bias, overestimating Dance method by 0.03 mGy, while general electric had the highest bias, overestimating Wu method by 0.20 mGy, the Hologic organ dose underestimated Boone method by 0.07 mGy, and the Fujifilm organ dose underestimated Dance method by 0.09 mGy. Organ dose was found to disagree with our calculated dose, yet organ dose is potentially beneficial for rapid dose audits. Conclusions drawn based on the organ dose alone come with a risk of over or underestimating the calculated dose to the patient and this error should be considered in any reported results.

Currently, computed tomography (CT) dosimetry relies on surrogates for dose, such as CT dose index and size-specific dose estimates, rather than dose per se. Organ dose is considered as the gold standard for radiation dosimetry. However, organ dose estimation requires precise knowledge of organ locations. Regional imparted energy and dose can also be used to quantify radiation burden and are beneficial because they do not require knowledge of organ size or location. This work investigated an automated technique to retrospectively estimate the imparted energy from tube current-modulated (TCM) CT exams across 13 protocols. Monte Carlo simulations of various head and body TCM CT examinations across various tube potentials and TCM strengths were performed on 58 adult computational extended cardiac-torso phantoms to develop relationships between scanned mass and imparted energy normalized by dose length product. Results from the Monte Carlo simulations indicate that normalized imparted energy increases with increasing both scanned mass and tube potential, but it is relatively unaffected by the strength of the TCM. The automated algorithm was tested on 40 clinical datasets with a 98% success rate.

Screening for breast cancer with mammography has been very successful, resulting in part to a reduction of breast cancer mortality by approximately 39% since 1990. However, mammography still has limitations in performance, especially for women with dense breast tissue. Iodinated contrast-enhanced, dedicated breast CT (BCT) has been proposed to improve lesion analysis and the accuracy of diagnostic workup for patients suspected of having breast cancer. A mathematical analysis to explore the use of various x-ray filters for iodinated contrast-enhanced BCT is presented. To assess task-based performance, the ideal linear observer signal-to-noise ratio (SNR) is used as a figure-of-merit under the assumptions of a linear, shift-invariant imaging system. To estimate signal and noise propagation through the BCT detector, a parallel-cascade model was used. The lesion model was embedded into a structured background and included a realistic level of iodine uptake. SNR was computed for 84,000 different exposure settings by varying the kV setting, x-ray filter materials and thickness, breast size, and composition and radiation dose. It is shown that some x-ray filter material/thickness combinations can provide up to 75% improvement in the linear ideal observer SNR over a conventionally used x-ray filter for BCT. This improvement in SNR can be traded off for substantial reductions in mean glandular dose.

Although transmission-based x-ray imaging is the most commonly used imaging approach for breast cancer detection, it exhibits false negative rates higher than 15%. To improve cancer detection accuracy, x-ray coherent scatter computed tomography (CSCT) has been explored to potentially detect cancer with greater consistency. However, the 10-min scan duration of CSCT limits its possible clinical applications. The coded aperture coherent scatter spectral imaging (CACSSI) technique has been shown to reduce scan time through enabling single-angle imaging while providing high detection accuracy. Here, we use Monte Carlo simulations to test analytical optimization studies of the CACSSI technique, specifically for detecting cancer in ex vivo breast samples. An anthropomorphic breast tissue phantom was modeled, a CACSSI imaging system was virtually simulated to image the phantom, a diagnostic voxel classification algorithm was applied to all reconstructed voxels in the phantom, and receiver-operator characteristics analysis of the voxel classification was used to evaluate and characterize the imaging system for a range of parameters that have been optimized in a prior analytical study. The results indicate that CACSSI is able to identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) in tissue samples with a cancerous voxel identification area-under-the-curve of 0.94 through a scan lasting less than 10 s per slice. These results show that coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue within ex vivo samples. Furthermore, the results indicate potential CACSSI imaging system configurations for implementation in subsequent imaging development studies.

Accurate and artifact-free reconstruction of tomographic images requires precise knowledge of the imaging system geometry. A projection matrix-based calibration method to enable C-arm inverse geometry CT (IGCT) is proposed. The method is evaluated for scanning-beam digital x-ray (SBDX), a C-arm mounted inverse geometry fluoroscopic technology. A helical configuration of fiducials is imaged at each gantry angle in a rotational acquisition. For each gantry angle, digital tomosynthesis is performed at multiple planes and a composite image analogous to a cone-beam projection is generated from the plane stack. The geometry of the C-arm, source array, and detector array is determined at each angle by constructing a parameterized three-dimensional-to-two-dimensional projection matrix that minimizes the sum-of-squared deviations between measured and projected fiducial coordinates. Simulations were used to evaluate calibration performance with translations and rotations of the source and detector. The relative root-mean-square error in a reconstruction of a numerical thorax phantom was 0.4% using the calibration method versus 7.7% without calibration. In phantom studies, reconstruction of SBDX projections using the proposed method eliminated artifacts present in noncalibrated reconstructions. The proposed IGCT calibration method reduces image artifacts when uncertainties exist in system geometry.

A lens-coupled x-ray camera with a tilted phosphor collects light emission from the x-ray illuminated (front) side of phosphor. Experimentally, it has been shown to double x-ray photon capture efficiency and triple the spatial resolution along the phosphor tilt direction relative to the same detector at normal phosphor incidence. These characteristics benefit grating-based phase-contrast methods, where linear interference fringes need to be clearly resolved. However, both the shallow incident angle on the phosphor and lens aberrations of the camera cause geometric distortions. When tiling multiple images of limited vertical view into a full-field image, geometric distortion causes blurring due to image misregistration. Here, we report a procedure of geometric correction based on global polynomial transformation of image coordinates. The corrected image is equivalent to one obtained with a single full-field flat panel detector placed at the sample plane. In a separate evaluation scan, the position deviations in the horizontal and vertical directions were reduced from 0.76 and 0.028 mm, respectively, to 0.006 and 0.009 mm, respectively, by the correction procedure, which were below the 0.028-mm pixel size of the imaging system. In a demonstration of a phase-contrast imaging experiment, the correction reduced blurring of small structures.

Scatter radiation severely degrades the image quality. Measurement-based scatter correction methods sample the scatter signal at sparsely distributed points, from which the scatter profile is estimated and deterministically removed from the projection image. The estimation of the scatter profile is generally done through a spline interpolation and the resulting scatter profile is quite smooth. Consequently, the noise is intact and the signal-to-noise ratio is reduced in the projection image after scatter correction, leading to image artifacts and increased noise in the reconstruction images. We propose a simple and effective method, referred to as filtered scatter-to-primary ratio (f-SPR) estimation, to estimate the scatter profile using the sparsely sampled scatter signal. Using the primary sampling device and the stationary digital tomosynthesis systems previously developed in our lab, we evaluated and compared the f-SPR method in comparison with existing methods in terms of contrast ratio, signal difference-to-noise ratio, and modulation transfer function. A significant improvement in image quality is observed in both the projection and the reconstruction images using the proposed method.

Using numerical simulations, the influence of various imaging parameters on the resulting image can be determined for various imaging technologies. To achieve this, visualization of fine tissue structures needed to evaluate the image quality with different radiation quality and dose is essential. The present work examines a method that employs simulations of the imaging process using Monte Carlo methods and a combination of a standard and higher resolution voxel models. A hybrid model, based on nonlinear uniform rational B-spline and polygon mesh surfaces, was constructed from an existing voxel model of a female patient of a resolution in the range of millimeters. The resolution of the hybrid model was 500  μm, i.e., substantially finer than that of the original model. Furthermore, a high resolution lung voxel model [(0.11  mm)3 voxel volume, slice thickness: 114  μm] was developed from the specimen of a left lung lobe. This has been inserted into the hybrid model, substituting its left lung lobe and resulting in a dual-lattice geometry model. “Dual lattice” means, in this context, the combination of voxel models with different resolutions. Monte Carlo simulations of radiographic imaging were performed and the fine structure of the lung was easily recognizable.

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Prospective case acquisition can be time-consuming. Inserting lesions into existing cases to simulate positive cases is a promising alternative. The aim was to evaluate a recently developed projection-based lesion insertion technique in thoracic CT. In total, 32 lung nodules of various attenuations were segmented from 21 patient cases, forward projected, inserted into projections, and reconstructed. Two experienced radiologists and two residents independently evaluated these nodules in two substudies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a score from 1 to 10 (1 = absolutely artificial to 10 = absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader. For the randomized evaluation, discrimination of real versus inserted nodules was poor with areas under the receiver operative characteristic curves being 0.57 [95% confidence interval (CI): 0.46 to 0.68], 0.69 (95% CI: 0.58 to 0.78), and 0.62 (95% CI: 0.54 to 0.69) for the two residents, two radiologists, and all four readers, respectively. Our projection-based lung nodule insertion technique provides a robust method to artificially generate positive cases that prove to be difficult to differentiate from real cases.

Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients’ movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of 1.87±0.87  mm and an average centerline distance error of 1.28±0.78  mm. The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.

Image denoising is a crucial step before performing segmentation or feature extraction on an image, which affects the final result in image processing. In recent years, utilizing the self-similarity characteristics of the images, many patch-based image denoising methods have been proposed, but most of them, named the internal denoising methods, utilized the noisy image only where the performances are constrained by the limited information they used. We proposed a patch-based method, which uses a low-rank technique and targeted database, to denoise the optical coherence tomography (OCT) image. When selecting the similar patches for the noisy patch, our method combined internal and external denoising, utilizing the other images relevant to the noisy image, in which our targeted database is made up of these two kinds of images and is an improvement compared with the previous methods. Next, we leverage the low-rank technique to denoise the group matrix consisting of the noisy patch and the corresponding similar patches, for the fact that a clean image can be seen as a low-rank matrix and rank of the noisy image is much larger than the clean image. After the first-step denoising is accomplished, we take advantage of Gabor transform, which considered the layer characteristic of the OCT retinal images, to construct a noisy image before the second step. Experimental results demonstrate that our method compares favorably with the existing state-of-the-art methods.

Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.

Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy (∼1% to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy (∼76% average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).

A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% (±0.67%), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% (±0.66%).

The automatic extraction of the vertebra’s shape from dynamic magnetic resonance imaging (MRI) could improve understanding of clinical conditions and their diagnosis. It is hypothesized that the shape of the sacral curve is related to the development of some gynecological conditions such as pelvic organ prolapse (POP). POP is a critical health condition for women and consists of pelvic organs dropping from their normal position. Dynamic MRI is used for assessing POP and to complement clinical examination. Studies have shown some evidence on the association between the shape of the sacral curve and the development of POP. However, the sacral curve is currently extracted manually limiting studies to small datasets and inconclusive evidence. A method composed of an adaptive shortest path algorithm that enhances edge detection and linking, and an improved curve fitting procedure is proposed to automate the identification and segmentation of the sacral curve on MRI. The proposed method uses predetermined pixels surrounding the sacral curve that are found through edge detection to decrease computation time compared to other model-based segmentation algorithms. Moreover, the proposed method is fully automatic and does not require user input or training. Experimental results show that the proposed method can accurately identify sacral curves for nearly 91% of dynamic MRI cases tested in this study. The proposed model is robust and can be used to effectively identify bone structures on MRI.

In minimally invasive surgery the haptic feedback, which represents an important tool for the localization of abnormalities, is no longer available. Elastography is an imaging technique that results in quantitative elastic parameters. It can hence be used to replace the lost sense of touch, as to enable tissue localization and discrimination. Digital image correlation is the chosen elastographic imaging technique. The implementation discussed here is clinically sound, based on a spectrally engineered illumination source that enables imaging of biological surface markers (blood vessels) with high contrast. Mechanical loading and deformation of the sample is performed using a rolling indenter, which enables the investigation of large organs (size of kidney) with reduced measurement time compared to a scanning approach. Furthermore, the rolling indentation results in strain contrast improvement and an increase in detection accuracy. The successful application of digital image correlation is first demonstrated on a silicone phantom and later on biological samples. Elasticity parameters and their corresponding four-dimensional distribution are generated via solving the inverse problem (only two-dimensional displacement field and strain map experimentally available) using a well-matched hyperelastic finite element model.

Intraoperative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study through statistical analysis, we show that (1) multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Ultrasound (US)-guided interventions are often enhanced via integration with an augmented reality environment, a necessary component of which is US calibration. Calibration requires the segmentation of fiducials, i.e., a phantom, in US images. Fiducial localization error (FLE) can decrease US calibration accuracy, which fundamentally affects the total accuracy of the interventional guidance system. Here, we investigate the effects of US image reconstruction techniques as well as phantom material and geometry on US calibration. It was shown that the FLE was reduced by 29% with synthetic transmit aperture imaging compared with conventional B-mode imaging in a Z-bar calibration, resulting in a 10% reduction of calibration error. In addition, an evaluation of a variety of calibration phantoms with different geometrical and material properties was performed. The phantoms included braided wire, plastic straws, and polyvinyl alcohol cryogel tubes with different diameters. It was shown that these properties have a significant effect on calibration error, which is a variable based on US beamforming techniques. These results would have important implications for calibration procedures and their feasibility in the context of image-guided procedures.

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient’s preoperative image volume to more closely match the intraoperative state of the patient’s brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient’s neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

Image guidance plays a critical role in radiotherapy. Currently, cone-beam computed tomography (CBCT) is routinely used in clinics for this purpose. While this modality can provide an attenuation image for therapeutic planning, low soft-tissue contrast affects the delineation of anatomical and pathological features. Efforts have recently been devoted to several MRI linear accelerator (LINAC) projects that lead to the successful combination of a full diagnostic MRI scanner with a radiotherapy machine. We present a new concept for the development of the MRI-LINAC system. Instead of combining a full MRI scanner with the LINAC platform, we propose using an interior MRI (iMRI) approach to image a specific region of interest (RoI) containing the radiation treatment target. While the conventional CBCT component still delivers a global image of the patient’s anatomy, the iMRI offers local imaging of high soft-tissue contrast for tumor delineation. We describe a top-level system design for the integration of an iMRI component into an existing LINAC platform. We performed numerical analyses of the magnetic field for the iMRI to show potentially acceptable field properties in a spherical RoI with a diameter of 15 cm. This field could be shielded to a sufficiently low level around the LINAC region to avoid electromagnetic interference. Furthermore, we investigate the dosimetric impacts of this integration on the radiotherapy beam.

We specify a notion of perceived background tissue complexity (BTC) that varies with lesion shape, lesion size, and lesion location in the image. We propose four unsupervised BTC estimators based on: perceived pre and postlesion similarity of images, lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), tissue anomaly detection, and local energy. The latter two are existing methods adapted for location- and lesion-dependent BTC estimation. For evaluation, we ask human observers to measure BTC (threshold visibility amplitude of a given lesion inserted) at specified locations in a mammogram. As expected, both human measured and computationally estimated BTC vary with lesion shape, size, and location. BTCs measured by different human observers are correlated (ρ=0.67). BTC estimators are correlated to each other (0.84<ρ<0.95) and less so to human observers (ρ≤0.81). With change in lesion shape or size, LBA estimated BTC changes in the same direction as human measured BTC. Proposed estimators can be generalized to other modalities (e.g., breast tomosynthesis) and used as-is or customized to a specific human observer, to construct BTC-aware model observers with applications, such as optimization of contrast-enhanced medical imaging systems and creation of a diversified image dataset with characteristics of a desired population.

Assessment of mechanical properties of soft matter is a challenging task in a purely noninvasive and noncontact environment. As tissue mechanical properties play a vital role in determining tissue health status, such noninvasive methods offer great potential in framing large-scale medical screening strategies. The digital speckle pattern interferometry (DSPI)–based image capture and analysis system described here is capable of extracting the deformation information from a single acquired fringe pattern. Such a method of analysis would be required in the case of the highly dynamic nature of speckle patterns derived from soft tissues while applying mechanical compression. Soft phantoms mimicking breast tissue optical and mechanical properties were fabricated and tested in the DSPI out of plane configuration set up. Hilbert transform (HT)-based image analysis algorithm was developed to extract the phase and corresponding deformation of the sample from a single acquired fringe pattern. The experimental fringe contours were found to correlate with numerically simulated deformation patterns of the sample using Abaqus finite element analysis software. The extracted deformation from the experimental fringe pattern using the HT-based algorithm is compared with the deformation value obtained using numerical simulation under similar conditions of loading and the results are found to correlate with an average %error of 10. The proposed method is applied on breast phantoms fabricated with included subsurface anomaly mimicking cancerous tissue and the results are analyzed.