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Cardiac fluid dynamics fundamentally involves interactions between complex blood flows and the structural deformations of the muscular heart walls and the thin valve leaflets. There has been longstanding scientific, engineering, and medical interest in creating mathematical models of the heart that capture, explain, and predict these fluid-structure interactions (FSIs). However, existing computational models that account for interactions among the blood, the actively contracting myocardium, and the valves are limited in their abilities to predict valve performance, capture fine-scale flow features, or use realistic descriptions of tissue biomechanics. Here we introduce and benchmark a comprehensive mathematical model of cardiac FSI in the human heart. A unique feature of our model is that it incorporates biomechanically detailed descriptions of all major cardiac structures that are calibrated using tensile tests of human tissue specimens to reflect the heart's microstructure. Further, it is the first FSI model of the heart that provides anatomically and physiologically detailed representations of all four cardiac valves. We demonstrate that this integrative model generates physiologic dynamics, including realistic pressure-volume loops that automatically capture isovolumetric contraction and relaxation, and that its responses to changes in loading conditions are consistent with the Frank-Starling mechanism. These complex relationships emerge intrinsically from interactions within our comprehensive description of cardiac physiology. Such models can serve as tools for predicting the impacts of medical interventions. They also can provide platforms for mechanistic studies of cardiac pathophysiology and dysfunction, including congenital defects, cardiomyopathies, and heart failure, that are difficult or impossible to perform in patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background. The first proposed solution here is to manually segment the regions, which is a time-consuming and error-prone procedure. In this context, many semi- or fully automatic solutions have been proposed recently, among which deep learning-based methods have revealed high performance in segmenting regions in CMRI data. In this study, a self-adaptive multi attention (SMA) module is introduced to adaptively leverage multiple attention mechanisms for better segmentation. The convolutional-based position and channel attention mechanisms with a patch tokenization-based vision transformer (ViT)-based attention mechanism in a hybrid and end-to-end manner are integrated into the SMA. The CNN- and ViT-based attentions mine the short- and long-range dependencies for more precise segmentation. The SMA module is applied in an encoder-decoder structure with a ResNet50 backbone named CardSegNet. Furthermore, a deep supervision method with multi-loss functions is introduced to the CardSegNet optimizer to reduce overfitting and enhance the model's performance. The proposed model is validated on the ACDC2017 (n=100), M&Ms (n=321), and a local dataset (n=22) using the 10-fold cross-validation method with promising segmentation results, demonstrating its outperformance versus its counterparts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The accuracy and variability of quantification in computed tomography angiography (CTA) are affected by the interplay of imaging parameters and patient attributes. The assessment of these combined effects has been an open engineering challenge., Purpose: In this study, we developed a framework that optimizes imaging parameters for accurate and consistent coronary stenosis quantification in cardiac CTA while accounting for patient-specific variables., Methods: The framework utilizes a task-specific image quality index, the estimability index ( e ' ), approximated by a surrogate estimability polynomial function (EPF) capable of finding the optimal protocol that (1) maximizes image quality with an upper bound for desired radiation dose or (2) minimizes the dose level with a lower bound of acceptable image quality. The optimization process was formulated with the decision variables being subject to a set of constraints. The methodology was verified using CTA data from a prior clinical trial (prospective multi-center imaging study for evaluation of chest pain) by assessing the concordance of its prediction with the trial results. Further, the framework was used to derive an optimum protocol for each case based on the patient attributes, gauging how much improvement would have been possible if the derived optimized protocol would have been deployed., Results: The framework produced results consistent with imaging physics principles with approximated EPFs of 97% accuracy. The feature importance evaluation demonstrated a close match with earlier studies. The verification study found e ' scores closely predicting the cardiologist scores to within 95% in terms of the area under the receiver operating characteristic curve and predicting potential for either an average of fourfold increase in e ' within a targeted dose or a reduction in radiation dose by an average of 57% without reducing the image quality., Conclusions: The protocol optimization framework provides means to assess and optimize CTA in terms of either image quality or radiation dose objectives with its results predicting prior clinical trial findings., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

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., (© 2021 The Authors.)

Our approach differs from the usual global measure of cardiac efficiency by using PET/MRI to measure efficiency of small pieces of cardiac tissue whose limiting size is equal to the spatial resolution of the PET scanner. We initiated a dynamic cardiac PET study immediately prior to the injection of 15.1 mCi of 11 C-acetate acquiring data for 25 minutes while simultaneously acquiring MRI cine data. 1) A 3D finite element (FE) biomechanical model of the imaged heart was constructed by utilizing nonrigid deformable image registration to alter the Dassault Systèmes FE Living Heart Model (LHM) to fit the geometry in the cardiac MRI cine data. The patient specific FE cardiac model with estimates of stress, strain, and work was transformed into PET/MRI format. 2) A 1-tissue compartment model was used to calculate wash-in (K 1 ) and the linear portion of the decay in the PET 11 C-acetate time activity curve (TAC) was used to calculate the wash-out k 2 (mono) rate constant. K 1 was used to calculate blood flow and k 2 (mono) was used to calculate myocardial volume oxygen consumption ( MVO 2 ). 3) Estimates of stress and strain were used to calculate Myocardial Equivalent Minute Work ( MEMW ) and Cardiac Efficiency = MEMW/MVO 2 was then calculated for 17 tissue segments of the left ventricle. The global MBF was 0.96 ± 0.15 ml/min/gm and MVO 2 ranged from 8 to 17 ml/100gm/min. Six central slices of the MRI cine data provided a range of MEMW of 0.1 to 0.4 joules/gm/min and a range of Cardiac Efficiency of 6 to 18%.

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., (© 2021 The Authors.)

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., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

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., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

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.

Background: Validating new techniques for fetal cardiovascular magnetic resonance (CMR) is challenging due to random fetal movement that precludes repeat measurements. Consequently, fetal CMR development has been largely performed using physical phantoms or postnatal volunteers. In this work, we present an open-source simulation designed to aid in the development and validation of new approaches for fetal CMR. Our approach, fetal extended Cardiac-Torso cardiovascular magnetic resonance imaging (Fetal XCMR), builds on established methods for simulating CMR acquisitions but is tailored toward the dynamic physiology of the fetal heart and body. We present comparisons between the Fetal XCMR phantom and data acquired in utero, resulting in image quality, anatomy, tissue signals and contrast., Methods: Existing extended Cardiac-Torso models are modified to create maternal and fetal anatomy, combined according to simulated motion, mapped to CMR contrast, and converted to CMR data. To provide a comparison between the proposed simulation and experimental fetal CMR images acquired in utero, images from a typical scan of a pregnant woman are included and simulated acquisitions were generated using matching CMR parameters, motion and noise levels. Three reconstruction (static, real-time, and CINE), and two motion estimation methods (translational motion, fetal heart rate) from data acquired in transverse, sagittal, coronal, and short-axis planes of the fetal heart were performed to compare to in utero acquisitions and demonstrate feasibility of the proposed simulation framework., Results: Overall, CMR contrast, morphologies, and relative proportions of the maternal and fetal anatomy are well represented by the Fetal XCMR images when comparing the simulation to static images acquired in utero. Additionally, visualization of maternal respiratory and fetal cardiac motion is comparable between Fetal XCMR and in utero real-time images. Finally, high quality CINE image reconstructions provide excellent delineation of fetal cardiac anatomy and temporal dynamics for both data types., Conclusion: The fetal CMR phantom provides a new method for evaluating fetal CMR acquisition and reconstruction methods by simulating the underlying anatomy and physiology. As the field of fetal CMR continues to grow, new methods will become available and require careful validation. The fetal CMR phantom is therefore a powerful and convenient tool in the continued development of fetal cardiac imaging.

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.

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Purpose: To develop a 4-dimensional computerized magnetic resonance imaging phantom with image textures extracted from real patient scans for liver motion studies., Methods: The proposed phantom was developed based on the current version of 4-dimensional extended cardiac-torso computerized phantom and a clinical magnetic resonance scan. Initially, the extended cardiac-torso phantom was voxelized in abdominal-chest region at the end of exhalation phase. Structures/tissues were classified into 4 categories: (1) Seven key textured organs, including liver, gallbladder, spleen, stomach, heart, kidneys, and pancreas, were mapped from a clinical T1-weighted liver magnetic resonance scan using deformable registration. (2) Large textured soft tissue volumes were simulated via an iterative pattern generation method using the same magnetic resonance scan. (3) Lung and intestine structures were generated by assigning uniform intensity with proper noise modeling. (4) Bony structures were generated by assigning the magnetic resonance values. A spherical hypointensity tumor was inserted into the liver. Other respiratory phases of the 4-dimensional phantom were generated using the backward deformation vector fields exported by the extended cardiac-torso program, except that bony structures were generated separately for each phase. A weighted image filtering process was utilized to improve the overall tissue smoothness at each phase., Results: Three 4-dimensional series with different motion amplitudes were generated. The developed motion phantom produced good illustrations of abdominal-chest region with anatomical structures in key organs and texture patterns in large soft tissue volumes. In a standard series, the tumor volume was measured as 13.90 ± 0.11 cm 3 in a respiratory cycle and the tumor's maximum center-of-mass shift was 2.95 cm/1.84 cm on superior-inferior/anterior-posterior directions. The organ motion during the respiratory cycle was well rendered. The developed motion phantom has the flexibility of motion pattern variation, organ geometry variation, and tumor modeling variation., Conclusions: A 4-D computerized phantom was developed and could be used to produce image series with synthetic magnetic resonance textures for magnetic resonance imaging research of liver motion.

This study aimed to estimate the organ dose reduction potential for organ-dose-based tube current modulated (ODM) thoracic computed tomography (CT) with a wide dose reduction arc. Twenty-one computational anthropomorphic phantoms (XCAT) were used to create a virtual patient population with clinical anatomic variations. The phantoms were created based on patient images with normal anatomy (age range: 27 to 66 years, weight range: 52.0 to 105.8 kg). For each phantom, two breast tissue compositions were simulated: [Formula: see text] and [Formula: see text] (glandular-to-adipose ratio). A validated Monte Carlo program (PENELOPE, Universitat de Barcelona, Spain) was used to estimate the organ dose for standard tube current modulation (TCM) (SmartmA, GE Healthcare) and ODM (GE Healthcare) for a commercial CT scanner (Revolution, GE Healthcare) using a typical clinical thoracic CT protocol. Both organ dose and [Formula: see text]-to-organ dose conversion coefficients ([Formula: see text] factors) were compared between TCM and ODM. ODM significantly reduced all radiosensitive organ doses ([Formula: see text]). The breast dose was reduced by [Formula: see text]. For [Formula: see text] factors, organs in the anterior region (e.g., thyroid and stomach) exhibited substantial decreases, and the medial, distributed, and posterior region saw either an increase of less than 5% or no significant change. ODM significantly reduced organ doses especially for radiosensitive superficial anterior organs such as the breasts.

The purpose of this study was to investigate relationships between patient attributes and organ dose for a population of computational phantoms for 20 tomosynthesis and radiography protocols. Organ dose was estimated from 54 adult computational phantoms (age: 18 to 78 years, weight 52 to 117 kg) using a validated Monte-Carlo simulation (PENELOPE) of a system capable of performing tomosynthesis and radiography. The geometry and field of view for each exam were modeled to match clinical protocols. For each protocol, the energy deposited in each organ was estimated by the simulations, converted to dose units, and then normalized by exposure in air. Dose to radiosensitive organs was studied as a function of average patient thickness in the region of interest and as a function of body mass index. For tomosynthesis, organ doses were also studied as a function of x-ray tube position. This work developed comprehensive information for organ dose dependencies across a range of tomosynthesis and radiography protocols. The results showed a protocol-dependent exponential decrease with an increasing patient size. There was a variability in organ dose across the patient population, which should be incorporated in the metrology of organ dose. The results can be used to prospectively and retrospectively estimate organ dose for tomosynthesis and radiography.

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.

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Cubic Hermite hexahedral finite element meshes have some well-known advantages over linear tetrahedral finite element meshes in biomechanical and anatomic modeling using isogeometric analysis. These include faster convergence rates as well as the ability to easily model rule-based anatomic features such as cardiac fiber directions. However, it is not possible to create closed complex objects with only regular nodes; these objects require the presence of extraordinary nodes (nodes with 3 or >= 5 adjacent elements in 2D) in the mesh. The presence of extraordinary nodes requires new constraints on the derivatives of adjacent elements to maintain continuity. We have developed a new method that uses an ensemble coordinate frame at the nodes and a local-to-global mapping to maintain continuity. In this paper, we make use of this mapping to create cubic Hermite models of the human ventricles and a four-chamber heart. We also extend the methods to the finite element equations to perform biomechanics simulations using these meshes. The new methods are validated using simple test models and applied to anatomically accurate ventricular meshes with valve annuli to simulate complete cardiac cycle simulations.

X-ray phase-contrast imaging (XPCI) can dramatically improve soft tissue contrast in X-ray medical imaging. Despite worldwide efforts to develop novel XPCI systems, a numerical framework to rigorously predict the performance of a clinical XPCI system at a human scale is not yet available. We have developed such a tool by combining a numerical anthropomorphic phantom defined with non-uniform rational B-splines (NURBS) and a wave optics-based simulator that can accurately capture the phase-contrast signal from a human-scaled numerical phantom. Using a synchrotron-based, high-performance XPCI system, we provide qualitative comparison between simulated and experimental images. Our tool can be used to simulate the performance of XPCI on various disease entities and compare proposed XPCI systems in an unbiased manner.

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.

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.

Unlabelled: A new generation of reference computational phantoms, based on image-based models tied to the reference masses defined by the International Commission on Radiological Protection (ICRP) for dose calculations, is presented., Methods: Anatomic models based on nonuniform rational b-spline modeling techniques were used to define reference male and female adults, 15-y-olds, 10-y-olds, 5-y-olds, 1-y-olds, newborns, and pregnant women at 3 stages of gestation, using the defined reference organ masses in ICRP publication 89. Absorbed fractions and specific absorbed fractions for internal emitters were derived using standard Monte Carlo radiation transport simulation codes., Results: Differences were notable between many pairs of organs in specific absorbed fractions because of the improved realism of the models, with adjacent organs usually closer and sometimes touching. Final estimates of absorbed dose for radiopharmaceuticals, for example, were only slightly different overall, as many of the differences were small and most pronounced at low radiation energies. Some new important organs were defined (salivary glands, prostate, eyes, and esophagus), and the identity of a few gastrointestinal tract organs changed., Conclusion: A new generation of reference models for standardized internal and external dose calculations has been defined. The models will be implemented in standardized software for internal dose calculations and be used to produce new standardized dose estimates for radiopharmaceuticals and other applications.

The 4D extended cardiac-torso (XCAT) phantom was developed to provide a realistic and flexible model of the human anatomy and cardiac and respiratory motions for use in medical imaging research. A prior limitation to the phantom was that it did not accurately simulate altered functions of the heart that result from cardiac pathologies such as coronary artery disease (CAD). We overcame this limitation in a previous study by combining the phantom with a finite-element (FE) mechanical model of the left ventricle (LV) capable of more realistically simulating regional defects caused by ischemia. In the present work, we extend this model giving it the ability to accurately simulate motion abnormalities caused by myocardial infarction (MI), a far more complex situation in terms of altered mechanics compared with the modeling of acute ischemia. The FE model geometry is based on high resolution CT images of a normal male subject. An anterior region was defined as infarcted and the material properties and fiber distribution were altered, according to the bio-physiological properties of two types of infarction, i.e., fibrous and remodeled infarction (30% thinner wall than fibrous case). Compared with the original, surface-based 4D beating heart model of the XCAT, where regional abnormalities are modeled by simply scaling down the motion in those regions, the FE model was found to provide a more accurate representation of the abnormal motion of the LV due to the effects of fibrous infarction as well as depicting the motion of remodeled infarction. In particular, the FE models allow for the accurate depiction of dyskinetic motion. The average circumferential strain results were found to be consistent with measured dyskinetic experimental results. Combined with the 4D XCAT phantom, the FE model can be used to produce realistic multimodality sets of imaging data from a variety of patients in which the normal or abnormal cardiac function is accurately represented.

This paper describes an automated procedure for creating detailed patient-specific pediatric dosimetry phantoms from a small set of segmented organs in a child's CT scan. The algorithm involves full body mappings from adult template to pediatric images using multichannel large deformation diffeomorphic metric mapping (MC-LDDMM). The parallel implementation and performance of MC-LDDMM for this application is studied here for a sample of 4 pediatric patients, and from 1 to 24 processors. 93.84% of computation time is parallelized, and the efficiency of parallelization remains high until more than 8 processors are used. The performance of the algorithm was validated on a set of 24 male and 18 female pediatric patients. It was found to be accurate typically to within 1-2 voxels (2-4 mm) and robust across this large and variable data set.

Unlabelled: Rodent species are widely used in the testing and approval of new radiopharmaceuticals, necessitating murine phantom models. As more therapy applications are being tested in animal models, calculating accurate dose estimates for the animals themselves becomes important to explain and control potential radiation toxicity or treatment efficacy. Historically, stylized and mathematically based models have been used for establishing doses to small animals. Recently, a series of anatomically realistic human phantoms was developed using body models based on nonuniform rational B-spline. Realistic digital mouse whole-body (MOBY) and rat whole-body (ROBY) phantoms were developed on the basis of the same NURBS technology and were used in this study to facilitate dose calculations in various species of rodents., Methods: Voxel-based versions of scaled MOBY and ROBY models were used with the Vanderbilt multinode computing network (Advanced Computing Center for Research and Education), using geometry and tracking radiation transport codes to calculate specific absorbed fractions (SAFs) with internal photon and electron sources. Photon and electron SAFs were then calculated for relevant organs in all models., Results: The SAF results were compared with values from similar studies found in reference literature. Also, the SAFs were used with standardized decay data to develop dose factors to be used in radiation dose calculations. Representative plots were made of photon electron SAFs, evaluating the traditional assumption that all electron energy is absorbed in the source organs., Conclusion: The organ masses in the MOBY and ROBY models are in reasonable agreement with models presented by other investigators noting that considerable variation can occur between reported masses. Results consistent with those found by other investigators show that 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 such as (32)P, (90)Y, or (188)Re).

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the field-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

The four-dimensional (4-D) NURBS-based cardiac-torso (NCAT) phantom, which provides a realistic model of the normal human anatomy and cardiac and respiratory motions, is used in medical imaging research to evaluate and improve imaging devices and techniques, especially dynamic cardiac applications. One limitation of the phantom is that it lacks the ability to accurately simulate altered functions of the heart that result from cardiac pathologies such as coronary artery disease (CAD). The goal of this work was to enhance the 4-D NCAT phantom by incorporating a physiologically based, finite-element (FE) mechanical model of the left ventricle (LV) to simulate both normal and abnormal cardiac motions. The geometry of the FE mechanical model was based on gated high-resolution X-ray multislice computed tomography (MSCT) data of a healthy male subject. The myocardial wall was represented as a transversely isotropic hyperelastic material, with the fiber angle varying from -90 degrees at the epicardial surface, through 0 degrees at the midwall, to 90 degrees at the endocardial surface. A time-varying elastance model was used to simulate fiber contraction, and physiological intraventricular systolic pressure-time curves were applied to simulate the cardiac motion over the entire cardiac cycle. To demonstrate the ability of the FE mechanical model to accurately simulate the normal cardiac motion as well as the abnormal motions indicative of CAD, a normal case and two pathologic cases were simulated and analyzed. In the first pathologic model, a subendocardial anterior ischemic region was defined. A second model was created with a transmural ischemic region defined in the same location. The FE-based deformations were incorporated into the 4-D NCAT cardiac model through the control points that define the cardiac structures in the phantom which were set to move according to the predictions of the mechanical model. A simulation study was performed using the FE-NCAT combination to investigate how the differences in contractile function between the subendocardial and transmural infarcts manifest themselves in myocardial Single photon emission computed tomography (SPECT) images. The normal FE model produced strain distributions that were consistent with those reported in the literature and a motion consistent with that defined in the normal 4-D NCAT beating heart model based on tagged magnetic resonance imaging (MRI) data. The addition of a subendocardial ischemic region changed the average transmural circumferential strain from a contractile value of -0.09 to a tensile value of 0.02. The addition of a transmural ischemic region changed average circumferential strain to a value of 0.13, which is consistent with data reported in the literature. Model results demonstrated differences in contractile function between subendocardial and transmural infarcts and how these differences in function are documented in simulated myocardial SPECT images produced using the 4-D NCAT phantom. Compared with the original NCAT beating heart model, the FE mechanical model produced a more accurate simulation for the cardiac motion abnormalities. Such a model, when incorporated into the 4-D NCAT phantom, has great potential for use in cardiac imaging research. With its enhanced physiologically based cardiac model, the 4-D NCAT phantom can be used to simulate realistic, predictive imaging data of a patient population with varying whole-body anatomy and with varying healthy and diseased states of the heart that will provide a known truth from which to evaluate and improve existing and emerging 4-D imaging techniques used in the diagnosis of cardiac disease.

To address the task of detecting nonischemic motion abnormalities from animated displays of gated myocardial perfusion single photon emission computed tomography data, we performed an observer study to evaluate the difference in detection performance between gating to 8 and 16 frames. Images were created from the NCAT mathematical phantom with a realistic heart simulating hypokinetic motion in the left lateral wall. Realistic noise-free projection data were simulated for both normal and defective hearts to obtain 16 frames for the cardiac cycle. Poisson noise was then simulated for each frame to create 50 realizations of each heart, All datasets were processed in two ways: reconstructed as a 16-frame set, and collapsed to 8 frames and reconstructed. Ten observers viewed the cardiac images animated with a realistic real-time frame rate. Observers trained on 100 images and tested on 100 images, rating their confidence on the presence of a motion defect on a continuous scale. None of the observers showed a significant difference in performance between the two gating methods. The 95% confidence interval on the difference in areas under the ROC curve (Az8 - Az16) was -0.029-0.085. Our test did not find a significant difference in detection performance between 8-frame gating and 16-frame gating. We conclude that, for the task of detecting abnormal motion, increasing the number of gated frames from 8 to 16 offers no apparent advantage.