Development of physiologically-informed computational coronary artery plaques for use in virtual imaging trials.

BACKGROUND: As a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices. PURPOSE: In this work, we develop realistic, physiologically-informed models for coronary plaques for application in cardiac imaging VITs. METHODS: Histology images of plaques at micron-level resolution were used to train a deep convolutional generative adversarial network (DC-GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole-body XCAT computational phantom to perform initial simulations comparing standard energy-integrating detector (EID) CT with photon-counting detector (PCD) CT. RESULTS: Our results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices. CONCLUSIONS: Vast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.

Modeling the effect of patient size on cerebral perfusion during veno-arterial extracorporeal membrane oxygenation.

INTRODUCTION: A well-known complication of veno-arterial extracorporeal membrane oxygenation (VA ECMO) is differential hypoxia, in which poorly-oxygenated blood ejected from the left ventricle mixes with and displaces well-oxygenated blood from the circuit, thereby causing cerebral hypoxia and ischemia. We sought to characterize the impact of patient size and anatomy on cerebral perfusion under a range of different VA ECMO flow conditions. METHODS: We use one-dimensional (1D) flow simulations to investigate mixing zone location and cerebral perfusion across 10 different levels of VA ECMO support in eight semi-idealized patient geometries, for a total of 80 scenarios. Measured outcomes included mixing zone location and cerebral blood flow (CBF). RESULTS: Depending on patient anatomy, we found that a VA ECMO support ranging between 67-97% of a patient's ideal cardiac output was needed to perfuse the brain. In some cases, VA ECMO flows exceeding 90% of the patient's ideal cardiac output are needed for adequate cerebral perfusion. CONCLUSIONS: Individual patient anatomy markedly affects mixing zone location and cerebral perfusion in VA ECMO. Future fluid simulations of VA ECMO physiology should incorporate varied patient sizes and geometries in order to best provide insights toward reducing neurologic injury and improved outcomes in this patient population.

TransMorph: Transformer for unsupervised medical image registration.

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations.

BACKGROUND: Digital anthropomorphic phantoms, such as the 4D extended cardiac-torso (XCAT) phantom, are actively used to develop, optimize, and evaluate a variety of imaging applications, allowing for realistic patient modeling and knowledge of ground truth. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, and soft tissue, while also accounting for cardiac and respiratory motion. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating medical imaging tasks such as sentinel node detection, node density measurement, and radiation dosimetry. PURPOSE: In this study, we aimed to develop a scalable lymphatic system in the XCAT phantom, to facilitate improved research of the lymphatic system in medical imaging. Using this scalable lymphatic system, we modeled the lymph node conglomerate pathology that is characteristically observed in primary mediastinal B-cell lymphoma (PMBCL). As an extended application, we evaluated positron emission tomography (PET) image quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of these simulated lymphomas, though the phantoms may be applied to other imaging modalities and study design paradigms (e.g., image quality, detection). METHODS: A template model for the lymphatic system was developed based on anatomical data from the Visible Human Project of the National Library of Medicine. The segmented nodes and vessels were fit with non-uniform rational basis spline surfaces, and multichannel large deformation diffeomorphic metric mapping was used to propagate the template to different XCAT anatomies. To model conglomerates observed in PMBCL, lymph nodes were enlarged, converged within the mediastinum, and tracer concentration was increased. We used the phantoms as inputs to a PET simulation tool, which generated images using ordered subsets expectation maximization reconstruction with 2-8 mm Gaussian filters. Fixed thresholding (FT) and gradient segmentation were used to determine MTV and TLG. Percent bias (%Bias) and coefficient of variation (COV) were computed as measures of accuracy and precision, respectively, for each MTV and TLG measurement. RESULTS: Using the methodology described above, we introduced a scalable lymphatic system in the XCAT phantom, which allows for the radioactivity and attenuation ground truth to be generated in 116 ± 2.5 s using a 2.3 GHz processor. Within the Rhinoceros interface, lymph node anatomy and function were modified to create a cohort of 10 phantoms with lymph node conglomerates. Using the lymphoma phantoms to evaluate PET quantification of MTV, mean %Bias values were -9.3%, -41.3%, and 20.9%, while COV values were 4.08%, 7.6%, and 3.4% using 25% FT, 40% FT, and gradient segmentations, respectively. Comparatively for TLG, mean %Bias values were -27.4%, -45.8%, and -16.0%, while COV values were 1.9%, 5.7%, and 1.4%, for the 25% FT, 40% FT, and gradient segmentations, respectively. CONCLUSIONS: In this work, we upgraded the XCAT phantom to include a lymphatic system, comprised of a network of 276 scalable lymph nodes and corresponding vessels. As an application, we created a cohort of phantoms with lymph node conglomerates to evaluate lymphoma quantification in PET imaging, which highlights an important application of this work.

Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry".

In [1], the dose estimation accuracy using the alternative baseline method under modulated tube current was not correctly calculated due to an unintentional simulation error.

Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training.

Purpose: Accurate classification of COVID-19 in chest radiographs is invaluable to hard-hit pandemic hot spots. Transfer learning techniques for images using well-known convolutional neural networks show promise in addressing this problem. These methods can significantly benefit from supplemental training on similar conditions, considering that there currently exists no widely available chest x-ray dataset on COVID-19. We evaluate whether targeted pretraining for similar tasks in radiography labeling improves classification performance in a sample radiograph dataset containing COVID-19 cases. Approach: We train a DenseNet121 to classify chest radiographs through six training schemes. Each training scheme is designed to incorporate cases from established datasets for general findings in chest radiography (CXR) and pneumonia, with a control scheme with no pretraining. The resulting six permutations are then trained and evaluated on a dataset of 1060 radiographs collected from 475 patients after March 2020, containing 801 images of laboratory-confirmed COVID-19 cases. Results: Sequential training phases yielded substantial improvement in classification accuracy compared to a baseline of standard transfer learning with ImageNet parameters. The test set area under the receiver operating characteristic curve for COVID-19 classification improved from 0.757 in the control to 0.857 for the optimal training scheme in the available images. Conclusions: We achieve COVID-19 classification accuracies comparable to previous benchmarks of pneumonia classification. Deliberate sequential training, rather than pooling datasets, is critical in training effective COVID-19 classifiers within the limitations of early datasets. These findings bring clinical-grade classification through CXR within reach for more regions impacted by COVID-19.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration.

PURPOSE: Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. METHODS: We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. RESULTS: Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. CONCLUSION: Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.

A dynamic simulation framework for CT perfusion in stroke assessment built from first principles.

PURPOSE: Physicians utilize cerebral perfusion maps (e.g., cerebral blood flow, cerebral blood volume, transit time) to prescribe the plan of care for stroke patients. Variability in scanning techniques and post-processing software can result in differences between these perfusion maps. To determine which techniques are acceptable for clinical care, it is important to validate the accuracy and reproducibility of the perfusion maps. Validation using clinical data is challenging due to the lack of a gold standard to assess cerebral perfusion and the impracticality of scanning patients multiple times with different scanning techniques. In contrast, simulated data from a realistic digital phantom of the cerebral perfusion in acute stroke patients would enable studies to optimize and validate the scanning and post-processing techniques. METHODS: We describe a complete framework to simulate CT perfusion studies for stroke assessment. We begin by expanding the XCAT brain phantom to enable spatially varying contrast agent dynamics and incorporate a realistic model of the dynamics in the cerebral vasculature derived from first principles. A dynamic CT simulator utilizes the time-concentration curves to define the contrast agent concentration in the object at each time point and generates CT perfusion images compatible with commercially available post-processing software. We also generate ground truth perfusion maps to which the maps generated by post-processing software can be compared. RESULTS: We demonstrate a dynamic CT perfusion study of a simulated patient with an ischemic stroke and the resulting perfusion maps generated by post-processing software. We include a visual comparison between the computer-generated perfusion maps and the ground truth perfusion maps. The framework is highly tunable; users can modify the perfusion properties (e.g., occlusion location, CBF, CBV, and MTT), scanner specifications (e.g., focal spot size and detector configuration), scanning protocol (e.g., kVp and mAs), and reconstruction parameters (e.g., slice thickness and reconstruction filter). CONCLUSIONS: This framework provides realistic test data with the underlying ground truth that enables a robust assessment of CT perfusion techniques and post-processing methods for stroke assessment.

iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry.

OBJECTIVE: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. METHOD: Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. RESULT: iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). CONCLUSION: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. SIGNIFICANCE: The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

Task-dependent estimability index to assess the quality of cardiac computed tomography angiography for quantifying coronary stenosis.

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA. Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index ( e ' ) as a task-based measure of image quality in cardiac CTA. The e ' index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA). Results: The e ' index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e ' threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training-test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective. Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.

Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography.

Purpose: To utilize a virtual clinical trial (VCT) construct to investigate the effects of beam collimation and pitch on image quality (IQ) in computed tomography (CT) under different respiratory and cardiac motion rates. Approach: A computational human model [extended cardiac-torso (XCAT) phantom] with added lung lesions was used to simulate seven different rates of cardiac and respiratory motions. A validated CT simulator (DukeSim) was used in this study. A supplemental validation was done to ensure the accuracy of DukeSim across different pitches and beam collimations. Each XCAT phantom was imaged using the CT simulator at multiple pitches (0.5 to 1.5) and beam collimations (19.2 to 57.6 mm) at a constant dose level. The images were compared against the ground truth using three task-generic IQ metrics in the lungs. Additionally, the bias and variability in radiomics (morphological) feature measurements were quantified for task-specific lung lesion quantification across the studied imaging conditions. Results: All task-generic metrics degraded by 1.6% to 13.3% with increasing pitch. When imaged with motion, increasing pitch reduced motion artifacts. The IQ slightly degraded (1.3%) with changes in the studied beam collimations. Patient motion exhibited negative effects (within 7%) on the IQ. Among all features across all imaging conditions studies, compactness2 and elongation showed the largest ( - 26.5 % , 7.8%) and smallest ( - 0.8 % , 2.7%) relative bias and variability. The radiomics results were robust across the motion profiles studied. Conclusions: While high pitch and large beam collimations can negatively affect the quality of CT images, they are desirable for fast imaging. Further, our results showed no major adverse effects in morphology quantification of lung lesions with the increase in pitch or beam collimation. VCTs, such as the one demonstrated in this study, represent a viable methodology for experiments in CT.

Virtual clinical trials in medical imaging: a review.

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Modeling "Textured" Bones in Virtual Human Phantoms.

The purpose of this study was to develop detailed and realistic models of the cortical and trabecular bones in the spine, ribs, and sternum and incorporate them into the library of virtual human phantoms (XCAT). Cortical bone was modeled by 3D morphological erosion of XCAT homogenously defined bones with an average thickness measured from the CT dataset upon which each individual XCAT phantom was based. The trabecular texture was modeled using a power law synthesis algorithm where the parameters were tuned using high-resolution anatomical images of the Human Visible Female. The synthesized bone textures were added into the XCAT phantoms. To qualitatively evaluate the improved realism of the bone modeling, CT simulations of the XCAT phantoms were acquired with and without the textured bone modeling. The 3D power spectrum of the anatomical images exhibited a power law behavior (R2 = 0.84), as expected in fractal and porous textures. The proposed texture synthesis algorithm was able to synthesize textures emulating real anatomical images, with the simulated CT images with the prototyped bones were more realistic than those simulated with the original XCAT models. Incorporating intra-organ structures, the "textured" phantoms are envisioned to be used to conduct virtual clinical trials in the context of medical imaging in cases where the actual trials are infeasible due to the lack of ground truth, cost, or potential risks to the patients.

DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography.

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT scanner (Somatom Definition Flash; Siemens Healthcare). The real and simulated images were compared in terms of image contrast, noise magnitude, noise texture, and spatial resolution. The relative error between the clinical and simulated images was less than 1.4%, 0.5%, 2.6%, and 3%, for image contrast, noise magnitude, noise texture, and spatial resolution, respectively, demonstrating the high realism of DukeSim. The runtime, dependent on the imaging task and the hardware, was approximately 2-3 minutes per rotation in our study using a computer with 4 GPUs. DukeSim, when combined with realistic human phantoms, provides the necessary toolset with which to perform large-scale and realistic virtual clinical trials in a patient and scanner-specific manner.

Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins.

The purpose of this paper was to extend the extended cardiac-torso (XCAT) series of computational phantoms to include a detailed lung architecture including airways and pulmonary vasculature. Eleven XCAT phantoms of varying anatomy were used in this paper. The lung lobes and initial branches of the airways, pulmonary arteries, and veins were previously defined in each XCAT model. These models were extended from the initial branches of the airways and vessels to the level of terminal branches using an anatomically-based volume-filling branching algorithm. This algorithm grew the airway and vasculature branches separately and iteratively without intersecting each other using cylindrical models with diameters estimated by order-based anatomical measurements. Geometrical features of the extended branches were compared with the literature anatomy values to quantitatively evaluate the models. These features include branching angle, length to diameter ratio, daughter to parent diameter ratio, asymmetrical branching pattern, diameter, and length ratios. The XCAT phantoms were then used to simulate CT images to qualitatively compare them with the original phantom images. The proposed growth model produced 46369 ± 12521 airways, 44737 ± 11773 arteries, and 39819 ± 9988 veins to the XCAT phantoms. Furthermore, the growth model was shown to produce asymmetrical airway, artery, and vein networks with geometrical attributes close to morphometry and model based studies. The simulated CT images of the phantoms were judged to be more realistic, including more airways and pulmonary vessels compared with the original phantoms. Future work will seek to add a heterogeneous parenchymal background into the XCAT lungs to make the phantoms even more representative of human anatomy, paving the way towards the use of XCAT models as a tool to virtually evaluate the current and emerging medical imaging technologies.

Correction of hysteretic respiratory motion in SPECT myocardial perfusion imaging: Simulation and patient studies.

PURPOSE: Amplitude-based respiratory gating is known to capture the extent of respiratory motion (RM) accurately but results in residual motion in the presence of respiratory hysteresis. In our previous study, we proposed and developed a novel approach to account for respiratory hysteresis by applying the Bouc-Wen (BW) model of hysteresis to external surrogate signals of anterior/posterior motion of the abdomen and chest with respiration. In this work, using simulated and clinical SPECT myocardial perfusion imaging (MPI) studies, we investigate the effects of respiratory hysteresis and evaluate the benefit of correcting it using the proposed BW model in comparison with the abdomen signal typically employed clinically. METHODS: The MRI navigator data acquired in free-breathing human volunteers were used in the specially modified 4D NCAT phantoms to allow simulating three types of respiratory patterns: monotonic, mild hysteresis, and strong hysteresis with normal myocardial uptake, and perfusion defects in the anterior, lateral, inferior, and septal locations of the mid-ventricular wall. Clinical scans were performed using a Tc-99m sestamibi MPI protocol while recording respiratory signals from thoracic and abdomen regions using a visual tracking system (VTS). The performance of the correction using the respiratory signals was assessed through polar map analysis in phantom and 10 clinical studies selected on the basis of having substantial RM. RESULTS: In phantom studies, simulations illustrating normal myocardial uptake showed significant differences (P < 0.001) in the uniformity of the polar maps between the RM uncorrected and corrected. No significant differences were seen in the polar map uniformity across the RM corrections. Studies simulating perfusion defects showed significantly decreased errors (P < 0.001) in defect severity and extent for the RM corrected compared to the uncorrected. Only for the strong hysteretic pattern, there was a significant difference (P < 0.001) among the RM corrections. The errors in defect severity and extent for the RM correction using abdomen signal were significantly higher compared to that of the BW (severity = -4.0%, P < 0.001; extent = -65.4%, P < 0.01) and chest (severity = -4.1%, P < 0.001; extent = -52.5%, P < 0.01) signals. In clinical studies, the quantitative analysis of the polar maps demonstrated qualitative and quantitative but not statistically significant differences (P = 0.73) between the correction methods that used the BW signal and the abdominal signal. CONCLUSIONS: This study shows that hysteresis in respiration affects the extent of residual motion left in the RM-binned data, which can impact wall uniformity and the visualization of defects. Thus, there appears to be the potential for improved accuracy in reconstruction in the presence of hysteretic RM with the BW model method providing a possible step in the direction of improvement.

Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data.

PURPOSE: Physical phantoms are essential for the development, optimization, and evaluation of x-ray breast imaging systems. Recognizing the major effect of anatomy on image quality and clinical performance, such phantoms should ideally reflect the three-dimensional structure of the human breast. Currently, there is no commercially available three-dimensional physical breast phantom that is anthropomorphic. The authors present the development of a new suite of physical breast phantoms based on human data. METHODS: The phantoms were designed to match the extended cardiac-torso virtual breast phantoms that were based on dedicated breast computed tomography images of human subjects. The phantoms were fabricated by high-resolution multimaterial additive manufacturing (3D printing) technology. The glandular equivalency of the photopolymer materials was measured relative to breast tissue-equivalent plastic materials. Based on the current state-of-the-art in the technology and available materials, two variations were fabricated. The first was a dual-material phantom, the Doublet. Fibroglandular tissue and skin were represented by the most radiographically dense material available; adipose tissue was represented by the least radiographically dense material. The second variation, the Singlet, was fabricated with a single material to represent fibroglandular tissue and skin. It was subsequently filled with adipose-equivalent materials including oil, beeswax, and permanent urethane-based polymer. Simulated microcalcification clusters were further included in the phantoms via crushed eggshells. The phantoms were imaged and characterized visually and quantitatively. RESULTS: The mammographic projections and tomosynthesis reconstructed images of the fabricated phantoms yielded realistic breast background. The mammograms of the phantoms demonstrated close correlation with simulated mammographic projection images of the corresponding virtual phantoms. Furthermore, power-law descriptions of the phantom images were in general agreement with real human images. The Singlet approach offered more realistic contrast as compared to the Doublet approach, but at the expense of air bubbles and air pockets that formed during the filling process. CONCLUSIONS: The presented physical breast phantoms and their matching virtual breast phantoms offer realistic breast anatomy, patient variability, and ease of use, making them a potential candidate for performing both system quality control testing and virtual clinical trials.

Two Realistic Beagle Models for Dose Assessment.

Previously, the authors developed a series of eight realistic digital mouse and rat whole body phantoms based on NURBS technology to facilitate internal and external dose calculations in various species of rodents. In this paper, two body phantoms of adult beagles are described based on voxel images converted to NURBS models. Specific absorbed fractions for activity in 24 organs are presented in these models. CT images were acquired of an adult male and female beagle. The images were segmented, and the organs and structures were modeled using NURBS surfaces and polygon meshes. Each model was voxelized at a resolution of 0.75 × 0.75 × 2 mm. The voxel versions were implemented in GEANT4 radiation transport codes to calculate specific absorbed fractions (SAFs) using internal photon and electron sources. Photon and electron SAFs were then calculated for relevant organs in both models. The SAFs for photons and electrons were compatible with results observed by others. Absorbed fractions for electrons for organ self-irradiation were significantly less than 1.0 at energies above 0.5 MeV, as expected for many of these small-sized organs, and measurable cross irradiation was observed for many organ pairs for high-energy electrons (as would be emitted by nuclides like 32P, 90Y, or 188Re). The SAFs were used with standardized decay data to develop dose factors (DFs) for radiation dose calculations using the RADAR Method. These two new realistic models of male and female beagle dogs will be useful in radiation dosimetry calculations for external or internal simulated sources.

MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance.

BACKGROUND: Computer simulations are important for validating novel image acquisition and reconstruction strategies. In cardiovascular magnetic resonance (CMR), numerical simulations need to combine anatomical information and the effects of cardiac and/or respiratory motion. To this end, a framework for realistic CMR simulations is proposed and its use for image reconstruction from undersampled data is demonstrated. METHODS: The extended Cardiac-Torso (XCAT) anatomical phantom framework with various motion options was used as a basis for the numerical phantoms. Different tissue, dynamic contrast and signal models, multiple receiver coils and noise are simulated. Arbitrary trajectories and undersampled acquisition can be selected. The utility of the framework is demonstrated for accelerated cine and first-pass myocardial perfusion imaging using k-t PCA and k-t SPARSE. RESULTS: MRXCAT phantoms allow for realistic simulation of CMR including optional cardiac and respiratory motion. Example reconstructions from simulated undersampled k-t parallel imaging demonstrate the feasibility of simulated acquisition and reconstruction using the presented framework. Myocardial blood flow assessment from simulated myocardial perfusion images highlights the suitability of MRXCAT for quantitative post-processing simulation. CONCLUSION: The proposed MRXCAT phantom framework enables versatile and realistic simulations of CMR including breathhold and free-breathing acquisitions.

Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems.

Mammography is currently the most widely utilized tool for detection and diagnosis of breast cancer. However, in women with dense breast tissue, tissue overlap may obscure lesions. Digital breast tomosynthesis can reduce tissue overlap. Furthermore, imaging with contrast enhancement can provide additional functional information about lesions, such as morphology and kinetics, which in turn may improve lesion identification and characterization. The performance of these imaging techniques is strongly dependent on the structural composition of the breast, which varies significantly among patients. Therefore, imaging system and imaging technique optimization should take patient variability into consideration. Furthermore, optimization of imaging techniques that employ contrast agents should include the temporally varying breast composition with respect to the contrast agent uptake kinetics. To these ends, we have developed a suite of 4-D virtual breast phantoms, which are incorporated with the kinetics of contrast agent propagation in different tissues and can realistically model normal breast parenchyma as well as benign and malignant lesions. This development presents a new approach in performing simulation studies using truly anthropomorphic models. To demonstrate the utility of the proposed 4-D phantoms, we present a simplified example study to compare the performance of 14 imaging paradigms qualitatively and quantitatively.