Virtual clinical trials in medical imaging: a review

Abstract. 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.

[1]  Claude Comtat,et al.  ASIM: An Analytic PET Simulator , 2012 .

[2]  G Panayiotakis,et al.  A Monte Carlo simulation model of mammographic imaging with x-ray sources of finite dimensions. , 2002, Physics in medicine and biology.

[3]  Aldo Badano,et al.  Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. , 2009, Medical physics.

[4]  P. J. Hunter,et al.  Generation of an Anatomically Based Three-Dimensional Model of the Conducting Airways , 2000, Annals of Biomedical Engineering.

[5]  Andrew D. A. Maidment,et al.  Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[6]  Dragana Brzakovic,et al.  Mammogram synthesis using a three-dimensional simulation. III. Modeling and evaluation of the breast ductal network. , 2003, Medical physics.

[7]  Xu Hai-Bo,et al.  Monte Carlo simulation for bremsstrahlung and photoneutron yields in high-energy x-ray radiography , 2010 .

[8]  R. Phillips Guidance for Industry and FDA Staff Information for Manufacturers Seeking Marketing Clearance of Diagnostic Ultrasound Systems and Transducers , 2008 .

[9]  Fang-Fang Yin,et al.  Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN). , 2020, Physics in medicine and biology.

[10]  Johannes T Heverhagen,et al.  The presence of iodinated contrast agents amplifies DNA radiation damage in computed tomography. , 2011, Contrast media & molecular imaging.

[11]  Jakob S Jørgensen,et al.  Few-view single photon emission computed tomography (SPECT) reconstruction based on a blurred piecewise constant object model. , 2013, Physics in medicine and biology.

[12]  John C Gore,et al.  Numerical study of water diffusion in biological tissues using an improved finite difference method , 2007, Physics in medicine and biology.

[13]  Ronald J. Jaszczak,et al.  Physical Factors Affecting Quantitative Measurements Using Camera-Based Single Photon Emission Computed Tomography (Spect) , 1981, IEEE Transactions on Nuclear Science.

[14]  Thomas Christen,et al.  A Simulation Tool for Dynamic Contrast Enhanced MRI , 2013, PloS one.

[15]  J. Sempau,et al.  MANTIS: combined x-ray, electron and optical Monte Carlo simulations of indirect radiation imaging systems , 2006, Physics in medicine and biology.

[16]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[17]  M Danielsson,et al.  Detective quantum efficiency dependence on x-ray energy weighting in mammography. , 1999, Medical physics.

[18]  Paul DeLuca,et al.  Realistic reference phantoms: An ICRP/ICRU joint effort , 2009, Annals of the ICRP.

[19]  R. Carmi,et al.  Resolution enhancement of X-ray CT by spatial and temporal MLEM deconvolution correction , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[20]  Andrzej Materka,et al.  Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms , 2016, Comput. Methods Programs Biomed..

[21]  Carole Lartizien,et al.  Simulating whole-body PET scanning with rapid analytical methods , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[22]  W. Paul Segars,et al.  Realistic phantoms to characterize dosimetry in pediatric CT , 2017, Pediatric Radiology.

[23]  Feng Chen,et al.  Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets , 2016, International Journal of Computer Assisted Radiology and Surgery.

[24]  John M. Boone,et al.  Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers , 2011, Medical Image Anal..

[25]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[26]  Xiance Jin,et al.  Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization , 2017, Physics in medicine and biology.

[27]  W. Paul Segars,et al.  Organ doses, effective doses, and risk indices in adult CT: comparison of four types of reference phantoms across different examination protocols. , 2012, Medical physics.

[28]  Fang-Fang Yin,et al.  Four‐dimensional diffusion‐weighted MR imaging (4D‐DWI): a feasibility study , 2017, Medical physics.

[29]  H Zaidi,et al.  Monte carlo simulation of x-ray spectra in diagnostic radiology and mammography using MCNP4C. , 2004, Physics in medicine and biology.

[30]  Andrew D. A. Maidment,et al.  OpenVCT: a GPU-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[31]  Ioannis E. Venetis,et al.  MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations , 2014, IEEE Transactions on Medical Imaging.

[32]  Andrew D. A. Maidment,et al.  Simulation and experimental validation of high-resolution test objects for evaluating a next-generation digital breast tomosynthesis prototype , 2019, Medical Imaging.

[33]  Ellery Storm,et al.  Calculated Bremsstrahlung Spectra from Thick Tungsten Targets , 1972 .

[34]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[35]  Jesse Tanguay,et al.  The role of x-ray Swank factor in energy-resolving photon-counting imaging. , 2010, Medical physics.

[36]  Jie Liu,et al.  Observer efficiency in discrimination tasks Simulating Malignant and benign breast lesions imaged with ultrasound , 2006, IEEE Transactions on Medical Imaging.

[37]  Alena-Kathrin Schnurr,et al.  Synthesis of CT images from digital body phantoms using CycleGAN , 2019, International Journal of Computer Assisted Radiology and Surgery.

[38]  R Birch,et al.  Computation of bremsstrahlung X-ray spectra and comparison with spectra measured with a Ge(Li) detector. , 1979, Physics in medicine and biology.

[39]  Jian Zhou,et al.  Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner , 2017, Physics in medicine and biology.

[40]  Hilde Bosmans,et al.  Performance comparison of breast imaging modalities using a 4AFC human observer study , 2015, Medical Imaging.

[41]  M P Eckstein,et al.  Mass detection on mammograms: influence of signal shape uncertainty on human and model observers. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[42]  Tao Zhang,et al.  Unsupervised clustering method to convert high-resolution magnetic resonance volumes to three-dimensional acoustic models for full-wave ultrasound simulations , 2019, Journal of medical imaging.

[43]  S Suryanarayanan,et al.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography. , 2010, Medical physics.

[44]  John M. Sabol,et al.  A Monte Carlo estimation of effective dose in chest tomosynthesis. , 2009, Medical physics.

[45]  Benjamin M.W. Tsui,et al.  Accuracy analysis of image-based respiratory motion estimation and compensation in respiratory-gated PET reconstruction , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[46]  Jeffrey H Siewerdsen,et al.  Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images. , 2008, Medical physics.

[47]  Paul E Kinahan,et al.  A Virtual Clinical Trial of FDG-PET Imaging of Breast Cancer: Effect of Variability on Response Assessment. , 2014, Translational oncology.

[48]  Richard G. Spencer,et al.  Direct simulation of spin echoes by summation of isochromats , 1996 .

[49]  Ehsan Samei,et al.  Realistic lesion simulation: application of hyperelastic deformation to lesion-local environment in lung CT , 2018, Medical Imaging.

[50]  Stephen J. Glick,et al.  High-resolution, anthropomorphic, computational breast phantom: fusion of rule-based structures with patient-based anatomy , 2017, Medical Imaging.

[51]  Niels Kuster,et al.  Realistic Skeleton Based Deformation of High-Resolution Anatomical Human Models for Electromagnetic Simulations , 2008 .

[52]  Norbert J. Pelc,et al.  Development of a realistic, dynamic digital brain phantom for CT perfusion validation , 2016, SPIE Medical Imaging.

[53]  Stephen L Hillis,et al.  A marginal‐mean ANOVA approach for analyzing multireader multicase radiological imaging data , 2014, Statistics in medicine.

[54]  Hilde Bosmans,et al.  Design of a model observer to evaluate calcification detectability in breast tomosynthesis and application to smoothing prior optimization. , 2016, Medical physics.

[55]  Hoen-oh Shin,et al.  Insertion of virtual pulmonary nodules in CT data of the chest: development of a software tool , 2006, European Radiology.

[56]  N Jon Shah,et al.  High‐performance computing MRI simulations , 2010, Magnetic resonance in medicine.

[57]  J M Boone,et al.  Mammography spectrum measurement using an x-ray diffraction device. , 1998, Physics in medicine and biology.

[58]  W. S. Snyder,et al.  Estimates of absorbed fractions for monoenergetic photon sources uniformly distributed in various organs of a heterogeneous phantom. , 1974, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[59]  M J Yaffe,et al.  Analysis of the spatial-frequency-dependent DQE of optically coupled digital mammography detectors. , 1994, Medical physics.

[60]  Gregory M. Sturgeon,et al.  Modeling “Textured” Bones in Virtual Human Phantoms , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[61]  T. D. Mast,et al.  Simulation of ultrasonic focus aberration and correction through human tissue. , 2002, The Journal of the Acoustical Society of America.

[62]  Jianhua Ma,et al.  A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC) , 2017, Medical physics.

[63]  Hyo-Min Cho,et al.  Characterization of energy response for photon-counting detectors using x-ray fluorescence. , 2014, Medical physics.

[64]  William Paul Segars,et al.  Fetal XCMR: a numerical phantom for fetal cardiovascular magnetic resonance imaging , 2019, Journal of Cardiovascular Magnetic Resonance.

[65]  L Axel,et al.  A computer simulation of nuclear magnetic resonance imaging , 1986, Magnetic resonance in medicine.

[66]  Anando Sen,et al.  Accounting for anatomical noise in search-capable model observers for planar nuclear imaging , 2016, Journal of medical imaging.

[67]  H. C. Torrey Bloch Equations with Diffusion Terms , 1956 .

[68]  Shuai Leng,et al.  Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain. , 2013, Medical physics.

[69]  A. Sisniega,et al.  Technical Note: spektr 3.0—A computational tool for x-ray spectrum modeling and analysis , 2016, Medical physics.

[70]  S Stute,et al.  GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy , 2011, Physics in medicine and biology.

[71]  Ehsan Samei,et al.  DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography , 2019, IEEE Transactions on Medical Imaging.

[72]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[73]  K Bliznakova,et al.  A three-dimensional breast software phantom for mammography simulation. , 2003, Physics in medicine and biology.

[74]  Gregory M. Sturgeon,et al.  Evaluation of statistical breast phantoms with higher resolution , 2018, Medical Imaging.

[75]  Karl G. Baum,et al.  Simulation of High-Resolution Magnetic Resonance Images on the IBM Blue Gene/L Supercomputer Using SIMRI , 2011, Int. J. Biomed. Imaging.

[76]  Demian Wassermann,et al.  Portable simulation framework for diffusion MRI , 2019, Journal of magnetic resonance.

[77]  Karl Stierstorfer,et al.  Modeling the frequency‐dependent detective quantum efficiency of photon‐counting x‐ray detectors , 2018, Medical physics.

[78]  Bruno De Man,et al.  Improved attenuation correction for respiratory gated PET/CT with extended-duration cine CT: a simulation study , 2017, Medical Imaging.

[79]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[80]  Shuai Leng,et al.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.

[81]  Bastien Guerin,et al.  Parallel transmission to reduce absorbed power around deep brain stimulation devices in MRI: Impact of number and arrangement of transmit channels , 2020, Magnetic resonance in medicine.

[82]  Peter J Hunter,et al.  Anatomically based finite element models of the human pulmonary arterial and venous trees including supernumerary vessels. , 2005, Journal of applied physiology.

[83]  L K Wagner,et al.  A Laplace transform pair model for spectral reconstruction. , 1982, Medical physics.

[84]  Stephen J. Glick,et al.  Investigation of statistical iterative reconstruction for dedicated breast CT , 2012, Medical Imaging.

[85]  Paul E Kinahan,et al.  Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method , 2016, Journal of medical imaging.

[86]  E. Halpern,et al.  Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. , 2013, Radiology.

[87]  H Benoit-Cattin,et al.  The SIMRI project: a versatile and interactive MRI simulator. , 2005, Journal of magnetic resonance.

[88]  Boguslaw Tomanek,et al.  Bloch simulations with intra-voxel spin dephasing. , 2010, Journal of magnetic resonance.

[89]  Hilde Bosmans,et al.  Development of breast lesions models database. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[90]  Aldo Badano,et al.  A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms. , 2010, Medical physics.

[91]  Chan Hyeong Kim,et al.  Feasibility of reducing differences in estimated doses in nuclear medicine between a patient-specific and a reference phantom. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[92]  B T Cox,et al.  k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. , 2010, Journal of biomedical optics.

[93]  Shuai Leng,et al.  Validation of a Projection-domain Insertion of Liver Lesions into CT Images. , 2016, Academic radiology.

[94]  Nooshin Kiarashi,et al.  Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data. , 2015, Medical physics.

[95]  Andreas K. Maier,et al.  A hybrid approach for virtual clinical trials for mammographic imaging , 2018, Other Conferences.

[96]  Premkumar Elangovan,et al.  Design and validation of realistic breast models for use in multiple alternative forced choice virtual clinical trials , 2017, Physics in medicine and biology.

[97]  Aldo Badano,et al.  A real-time radiation dose monitoring system for patients and staff during interventional fluoroscopy using a GPU-accelerated Monte Carlo simulator and an automatic 3D localization system based on a depth camera , 2013, Medical Imaging.

[98]  M. Yaffe,et al.  Quantifying masking in clinical mammograms via local detectability of simulated lesions. , 2016, Medical physics.

[99]  Göran Thungström,et al.  Validation of Geant4 Pixel Detector Simulation Framework by Measurements With the Medipix Family Detectors , 2016, IEEE Transactions on Nuclear Science.

[100]  Johanne Bezy-Wendling,et al.  A 3D DYNAMIC MODEL OF VASCULAR TREES , 1999 .

[101]  R. F. Wagner,et al.  Low Contrast Detectability and Contrast/Detail Analysis in Medical Ultrasound , 1983, IEEE Transactions on Sonics and Ultrasonics.

[102]  Shuai Leng,et al.  Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images , 2017, Journal of medical imaging.

[103]  Mark A. Anastasio,et al.  Learning the ideal observer for SKE detection tasks by use of convolutional neural networks (Cum Laude Poster Award) , 2018, Medical Imaging.

[104]  Yacine Noureddine,et al.  In vitro and in silico assessment of RF‐induced heating around intracranial aneurysm clips at 7 Tesla , 2018, Magnetic resonance in medicine.

[105]  G. Bonmassar,et al.  Numerical Simulations of Realistic Lead Trajectories and an Experimental Verification Support the Efficacy of Parallel Radiofrequency Transmission to Reduce Heating of Deep Brain Stimulation Implants during MRI , 2019, Scientific Reports.

[106]  Andrew M Hernandez,et al.  Tungsten anode spectral model using interpolating cubic splines: unfiltered x-ray spectra from 20 kV to 640 kV. , 2014, Medical physics.

[107]  Nooshin Kiarashi,et al.  Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation. , 2016, Medical physics.

[108]  Ehsan Samei,et al.  Simulation of mammographic lesions. , 2006, Academic radiology.

[109]  Benjamin M W Tsui,et al.  Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation , 2018, Medical physics.

[110]  Eric C. Frey,et al.  Parameterization of the scatter response function in SPECT imaging using Monte Carlo simulation , 1990 .

[111]  G Matscheko,et al.  A Compton scattering spectrometer for determining X-ray photon energy spectra. , 1987, Physics in medicine and biology.

[112]  Benjamin M W Tsui,et al.  Task-based evaluation of a 4D MAP-RBI-EM image reconstruction method for gated myocardial perfusion SPECT using a human observer study , 2015, Physics in medicine and biology.

[113]  Chan Hyeong Kim,et al.  Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering—A Topical Review , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[114]  Andrew D. A. Maidment,et al.  Optimized generation of high resolution breast anthropomorphic software phantoms. , 2012, Medical physics.

[115]  D R Dance,et al.  Calculation of the properties of digital mammograms using a computer simulation. , 2005, Radiation protection dosimetry.

[116]  J. Boone,et al.  An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV. , 1997, Medical physics.

[117]  W P Segars,et al.  The development of a population of 4D pediatric XCAT phantoms for imaging research and optimization. , 2015, Medical physics.

[118]  D E Peplow,et al.  Digital mammography image simulation using Monte Carlo. , 2000, Medical physics.

[119]  S Ted Treves,et al.  Internal photon and electron dosimetry of the newborn patient---a hybrid computational phantom study , 2012, Physics in medicine and biology.

[120]  B.M.W. Tsui,et al.  A mathematical observer study for the evaluation and optimization of compensation methods for myocardial SPECT using a phantom population that realistically models patient variability , 2004, IEEE Transactions on Nuclear Science.

[121]  M Yaffe,et al.  Spectroscopy of diagnostic x rays by a Compton-scatter method. , 1976, Medical physics.

[122]  Klaas Paul Pruessmann,et al.  Realistic Analytical Phantoms for Parallel Magnetic Resonance Imaging , 2012, IEEE Transactions on Medical Imaging.

[123]  J-F Aubry,et al.  Ex vivo optimisation of a heterogeneous speed of sound model of the human skull for non-invasive transcranial focused ultrasound at 1 MHz , 2017, International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group.

[124]  Francesc Massanes,et al.  Evaluation of CNN as anthropomorphic model observer , 2017, Medical Imaging.

[125]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[126]  B. Ginneken,et al.  Automated measurement of fetal head circumference using 2D ultrasound images , 2018, PloS one.

[127]  Ehsan Samei,et al.  The Effect of Contrast Material on Radiation Dose at CT: Part II. A Systematic Evaluation across 58 Patient Models. , 2017, Radiology.

[128]  Kyle J Myers,et al.  A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. , 2013, Medical physics.

[129]  W. Segars,et al.  MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance , 2014, Journal of Cardiovascular Magnetic Resonance.

[130]  D. Broga,et al.  Ionizing Radiation Exposure of the Population of the United States , 2009 .

[131]  I Buvat,et al.  Monte Carlo simulations in SPET and PET. , 2002, The quarterly journal of nuclear medicine : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology.

[132]  C. McCollough,et al.  CT scanner x-ray spectrum estimation from transmission measurements. , 2011, Medical physics.

[133]  Christian G. Graff,et al.  A new, open-source, multi-modality digital breast phantom , 2016, SPIE Medical Imaging.

[134]  Howard C Gifford A visual-search model observer for multislice-multiview SPECT images. , 2013, Medical physics.

[135]  J A Seibert,et al.  Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology. , 1988, Medical physics.

[136]  Christopher J. Roy,et al.  Verification and Validation in Scientific Computing , 2010 .

[137]  Alejandro F. Frangi,et al.  A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT , 2013, IEEE Transactions on Medical Imaging.

[138]  Berkman Sahiner,et al.  Seamless Lesion Insertion for Data Augmentation in CAD Training , 2017, IEEE Transactions on Medical Imaging.

[139]  Joseph Y. Lo,et al.  Incorporation of a Laguerre–Gauss Channelized Hotelling Observer for False-Positive Reduction in a Mammographic Mass CAD System , 2007, Journal of Digital Imaging.

[140]  Richard G. S. Spencer,et al.  Time domain simulation of Fourier imaging by summation of isochromats , 1997, Int. J. Imaging Syst. Technol..

[141]  James C. Lin,et al.  SAR and temperature: Simulations and comparison to regulatory limits for MRI , 2007, Journal of magnetic resonance imaging : JMRI.

[142]  W. W. Hansen,et al.  Nuclear Induction , 2011 .

[143]  Ehsan Samei,et al.  Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography , 2016, Journal of medical imaging.

[144]  Deirdre M. McGrath,et al.  Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations , 2017, Magnetic resonance in medicine.

[145]  A. Bozkurt,et al.  VIP-MAN: AN IMAGE-BASED WHOLE-BODY ADULT MALE MODEL CONSTRUCTED FROM COLOR PHOTOGRAPHS OF THE VISIBLE HUMAN PROJECT FOR MULTI-PARTICLE MONTE CARLO CALCULATIONS , 2000, Health physics.

[146]  A Robinson,et al.  Measurement of the focal spot size of diagnostic x-ray tubes--a comparison of pinhole and resolution methods. , 1975, The British journal of radiology.

[147]  Naichang Yu,et al.  Simulating solid lung nodules in MDCT images for CAD evaluation: modeling, validation, and applications , 2007, SPIE Medical Imaging.

[148]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[149]  L. Wald,et al.  Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies , 2018, Physics in medicine and biology.

[150]  H. Torp,et al.  Ultrasound simulation of complex flow velocity fields based on computational fluid dynamics , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[151]  Michael Ljungberg,et al.  Monte Carlo-based quantitative pinhole SPECT reconstruction using a ray-tracing back-projector , 2017, EJNMMI Physics.

[152]  M. Moseley,et al.  Efficient simulation of magnetic resonance imaging with Bloch-Torrey equations using intra-voxel magnetization gradients. , 2006, Journal of magnetic resonance.

[153]  Pierre Croisille,et al.  Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging , 2016, Physics in medicine and biology.

[154]  Vera A. Khokhlova,et al.  Numerical modeling of finite-amplitude sound beams: Shock formation in the near field of a cw plane piston source , 2001 .

[155]  George Sgouros,et al.  Lung dosimetry for radioiodine treatment planning in the case of diffuse lung metastases. , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[156]  Suvranu De,et al.  Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis. , 2010, Medical physics.

[157]  W P Segars,et al.  Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization. , 2013, Medical physics.

[158]  Hilde Bosmans,et al.  Model and human observer reproducibility for detection of microcalcification clusters in digital breast tomosynthesis images of three-dimensionally structured test object , 2019, Journal of medical imaging.

[159]  Ehsan Samei,et al.  Simulation of liver lesions for pediatric CT. , 2006, Radiology.

[160]  Alejandro F. Frangi,et al.  Statistical Personalization of Ventricular Fiber Orientation Using Shape Predictors , 2014, IEEE Transactions on Medical Imaging.

[161]  Ehsan Samei,et al.  Modeling dynamic, nutrient-access-based lesion progression using stochastic processes , 2019, Medical Imaging.

[162]  Cynthia B Paschal,et al.  MRI simulator with object-specific field map calculations. , 2004, Magnetic resonance imaging.

[163]  J. Pantazis,et al.  Characterization of CdTe Detectors for Quantitative X-ray Spectroscopy , 2009, IEEE Transactions on Nuclear Science.

[164]  Alan C. Evans,et al.  MRI Simulation Based Evaluation and Classifications Methods , 1999, IEEE Trans. Medical Imaging.

[165]  Wesley E Bolch,et al.  A paired-image radiation transport model for skeletal dosimetry. , 2005, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[166]  Ehsan Samei,et al.  Patient-specific dose estimation for pediatric chest CT. , 2008, Medical physics.

[167]  Shuai Leng,et al.  Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography , 2016, Journal of medical imaging.

[168]  R Takaki,et al.  A three-dimensional model of the human pulmonary acinus. , 2000, Journal of applied physiology.

[169]  Dean C. Barratt,et al.  Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks , 2018, IEEE Transactions on Medical Imaging.

[170]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[171]  I.E. Magnin,et al.  A new fully-digital anthropomorphic and dynamic thorax/heart model , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[172]  E. Stejskal Use of Spin Echoes in a Pulsed Magnetic‐Field Gradient to Study Anisotropic, Restricted Diffusion and Flow , 1965 .

[173]  C H Kim,et al.  New mesh-type phantoms and their dosimetric applications, including emergencies , 2018, Annals of the ICRP.

[174]  W.F. Walker,et al.  Generalized cystic resolution: a metric for assessing the fundamental limits on beamformer performance , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[175]  T. R. Fewell,et al.  Molybdenum, rhodium, and tungsten anode spectral models using interpolating polynomials with application to mammography. , 1997, Medical physics.

[176]  Dean F. Wong,et al.  Accurate Event-Driven Motion Compensation in High-Resolution PET Incorporating Scattered and Random Events , 2008, IEEE Transactions on Medical Imaging.

[177]  Alejandro F. Frangi,et al.  Quantitative CMR population imaging on 20, 000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation , 2019, Medical Image Anal..

[178]  Alejandro F. Frangi,et al.  Generalised coherent point drift for group‐wise multi‐dimensional analysis of diffusion brain MRI data , 2019, Medical Image Anal..

[179]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[180]  Frank W. Samuelson,et al.  Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial , 2018, JAMA network open.

[181]  Wei Zhao,et al.  Amorphous selenium flat panel detectors for digital mammography: validation of a NPWE model observer with CDMAM observer performance experiments. , 2006, Medical physics.

[182]  Jeffrey A. Fessler,et al.  Non-local means methods using CT side information for I-131 SPECT image reconstruction , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[183]  H. Barrett,et al.  Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[184]  J. Kelly,et al.  A time-space decomposition method for calculating the nearfield pressure generated by a pulsed circular piston , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[185]  T. D. Mast,et al.  Simulation of ultrasonic pulse propagation through the abdominal wall. , 1997, The Journal of the Acoustical Society of America.

[186]  Michael Ljungberg,et al.  Monte Carlo Calculation in Nuclear Medicine: Applications in Diagnostic Imaging , 2012 .

[187]  Louise Poissant Part I , 1996, Leonardo.

[188]  K Rossmann,et al.  New device for accurate measurement of the x-ray intensity distribution of x-ray tube focal spots. , 1975, Medical physics.

[189]  Nooshin Kiarashi,et al.  Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis. , 2014, Medical physics.

[190]  Andrew D. A. Maidment,et al.  Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. , 2011, Medical physics.

[191]  E C Frey,et al.  Receiver operating characteristic evaluation of iterative reconstruction with attenuation correction in 99mTc-sestamibi myocardial SPECT images. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[192]  Jan D'hooge,et al.  The Generalized Contrast-to-Noise Ratio , 2018, 2018 IEEE International Ultrasonics Symposium (IUS).

[193]  John Hunt,et al.  A methodology to develop computational phantoms with adjustable posture for WBC calibration. , 2014, Physics in medicine and biology.

[194]  Kyle J. Myers,et al.  Comparison of Channel Methods and Observer Models for the Task-Based Assessment of Multi-Projection Imaging in the Presence of Structured Anatomical Noise , 2016, IEEE Transactions on Medical Imaging.

[195]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[196]  H Paganetti,et al.  Adaptation of GEANT4 to Monte Carlo dose calculations based on CT data. , 2004, Medical physics.

[197]  John S. Hendricks,et al.  Initial MCNP6 Release Overview , 2012 .

[198]  Ioannis E. Venetis,et al.  High performance MRI simulations of motion on multi-GPU systems , 2014, Journal of Cardiovascular Magnetic Resonance.

[199]  Boguslaw Tomanek,et al.  The integration of real and virtual magnetic resonance imaging experiments in a single instrument. , 2009, The Review of scientific instruments.

[200]  Kay Nehrke,et al.  k‐t PCA: Temporally constrained k‐t BLAST reconstruction using principal component analysis , 2009, Magnetic resonance in medicine.

[201]  Richard Kijowski,et al.  Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model , 2016, IEEE Transactions on Medical Imaging.

[202]  Premkumar Elangovan,et al.  The effect of system geometry and dose on the threshold detectable calcification diameter in 2D-mammography and digital breast tomosynthesis , 2017, Physics in medicine and biology.

[203]  Alejandro F Frangi,et al.  Magnetic resonance elastography of the brain: An in silico study to determine the influence of cranial anatomy , 2015, Magnetic resonance in medicine.

[204]  Elliot K. Fishman,et al.  Effect of heart rate on CT angiography using the enhanced cardiac model of the 4D NCAT , 2006, SPIE Medical Imaging.

[205]  Olivier Basset,et al.  CREANUIS: a non-linear radiofrequency ultrasound image simulator. , 2013, Ultrasound in medicine & biology.

[206]  Craig K. Abbey,et al.  Perfusion signal processing for optimal detection performance , 2014, 2014 IEEE International Ultrasonics Symposium.

[207]  Jing Huang,et al.  Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization , 2016, Comput. Medical Imaging Graph..

[208]  W P Segars,et al.  Fast modelling of the collimator–detector response in Monte Carlo simulation of SPECT imaging using the angular response function , 2005, Physics in medicine and biology.

[209]  Yong Du,et al.  Comparison of Residence Time Estimation Methods for Radioimmunotherapy Dosimetry and Treatment Planning—Monte Carlo Simulation Studies , 2008, IEEE Transactions on Medical Imaging.

[210]  Elfar Adalsteinsson,et al.  Comparison of simulated parallel transmit body arrays at 3 T using excitation uniformity, global SAR, local SAR, and power efficiency metrics , 2015, Magnetic resonance in medicine.

[211]  Alejandro F Frangi,et al.  Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles , 2011, Magnetic resonance in medicine.

[212]  Hao Yan,et al.  A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections. , 2012, Medical physics.

[213]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[214]  German National Cohort Consortium,et al.  The German National Cohort: aims, study design and organization , 2014, European Journal of Epidemiology.

[215]  W.F. Walker,et al.  A spline-based approach for computing spatial impulse responses , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[216]  Ann-Katherine Carton,et al.  Development of a physical 3D anthropomorphic breast phantom. , 2011, Medical physics.

[217]  Daniel Niederlöhner,et al.  Signal transport in Computed Tomography detectors , 2008 .

[218]  Ehsan Samei,et al.  Impact of resolution and noise characteristics of digital radiographic detectors on the detectability of lung nodules , 2003, SPIE Medical Imaging.

[219]  Patrick Hugonnard,et al.  SINDBAD : a realistic multi-purpose and scalable X-ray si mulation tool for NDT applications , 2007 .

[220]  Shobhit Sharma,et al.  Development of a scanner-specific simulation framework for photon-counting computed tomography , 2019, Biomedical physics & engineering express.

[221]  Koji Maeda,et al.  Compton-scattering measurement of diagnostic x-ray spectrum using high-resolution Schottky CdTe detector. , 2005, Medical physics.

[222]  W. Paul Segars,et al.  Generation of a suite of 3D computer-generated breast phantoms from a limited set of human subject data. , 2013, Medical physics.

[223]  D. DeLong,et al.  Effect of dose reduction on the detection of mammographic lesions: a mathematical observer model analysis. , 2007, Medical physics.

[224]  John A. Gunnels,et al.  Massively parallel models of the human circulatory system , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[225]  M. Mazurowski Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. , 2019, Journal of the American College of Radiology : JACR.

[226]  Ehsan Samei,et al.  Population of 224 realistic human subject-based computational breast phantoms. , 2015, Medical physics.

[227]  Paul E. Kinahan,et al.  A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy , 2017, Physics in medicine and biology.

[228]  Katsuyuki Taguchi,et al.  XCAT/DRASIM: a realistic CT/human-model simulation package , 2011, Medical Imaging.

[229]  J. Solomon,et al.  Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms. , 2016, Medical physics.

[230]  Ehsan Samei,et al.  Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. , 2009, Medical physics.

[231]  Min Chen,et al.  Realistic analytical polyhedral MRI phantoms , 2016, Magnetic resonance in medicine.

[232]  Jirí Zára,et al.  Geometric skinning with approximate dual quaternion blending , 2008, TOGS.

[233]  W. K. Sinclair,et al.  Trends in radiation protection--a view from the National Council on Radiation Protection and Measurements (NCRP). , 1988, Health physics.

[234]  Fang-Fang Yin,et al.  Four dimensional magnetic resonance imaging with retrospective k-space reordering: a feasibility study. , 2015, Medical physics.

[235]  Dev P. Chakraborty,et al.  Visual discrimination modeling of lesion detectability , 2002, SPIE Medical Imaging.

[236]  G. Trahey,et al.  Sources of image degradation in fundamental and harmonic ultrasound imaging using nonlinear, full-wave simulations , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[237]  X George Xu,et al.  An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history , 2014, Physics in medicine and biology.

[238]  Alexander I. Veress,et al.  Normal and Pathological NCAT Image and Phantom Data Based on Physiologically Realistic Left Ventricle Finite-Element Models , 2006, IEEE Transactions on Medical Imaging.

[239]  Frank W. Samuelson,et al.  In silico imaging clinical trials for regulatory evaluation: initial considerations for VICTRE, a demonstration study , 2017, Medical Imaging.

[240]  Ehsan Samei,et al.  Patient-specific radiation dose and cancer risk for pediatric chest CT. , 2011, Radiology.

[241]  Michal Byra,et al.  Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions , 2017, Medical physics.

[242]  Premkumar Elangovan,et al.  The threshold detectable mass diameter for 2D-mammography and digital breast tomosynthesis. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[243]  J. Baker,et al.  A mathematical model platform for optimizing a multiprojection breast imaging system. , 2008, Medical physics.

[244]  R. Bouchard,et al.  A finite-element method model of soft tissue response to impulsive acoustic radiation force , 2005, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[245]  Ehsan Samei,et al.  Patient-informed and physiology-based modelling of contrast dynamics in cross-sectional imaging , 2019, Medical Imaging.

[246]  Satoshi Miyajima,et al.  Thin CdTe detector in diagnostic x-ray spectroscopy. , 2003, Medical physics.

[247]  Marek Kretowski,et al.  Multiscale Model of Liver DCE-MRI Towards a Better Understanding of Tumor Complexity , 2010, IEEE Transactions on Medical Imaging.

[248]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[249]  Kyle J Myers,et al.  Performance Evaluation of Computed Tomography Systems: Summary of AAPM Task Group 233. , 2019, Medical physics.

[250]  Jing-Rebecca Li,et al.  SpinDoctor: A MATLAB toolbox for diffusion MRI simulation , 2019, NeuroImage.

[251]  Howard C. Gifford,et al.  Task Equivalence for Model and Human-Observer Comparisons in SPECT Localization Studies , 2016, IEEE Transactions on Nuclear Science.

[252]  G Panayiotakis,et al.  DOSIS: a Monte Carlo simulation program for dose related studies in mammography. , 2005, European journal of radiology.

[253]  James A Bankson,et al.  A novel perfused Bloch-McConnell simulator for analyzing the accuracy of dynamic hyperpolarized MRS. , 2016, Medical physics.

[254]  R J Jaszczak,et al.  Single photon emission computed tomography (SPECT). Principles and instrumentation. , 1985, Investigative radiology.

[255]  Eric C. Frey,et al.  Combination of MCNP and SimSET for Monte Carlo Simulation of SPECT with Medium and High Energy Photons , 2001 .

[256]  James H Thrall,et al.  Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. , 2018, Journal of the American College of Radiology : JACR.

[257]  New S Tudy The German National Cohort: aims, study design and organization , 2014 .

[258]  Paul E. Kinahan,et al.  Effect of 18F-FDG Uptake Time on Lesion Detectability in PET Imaging of Early-Stage Breast Cancer , 2015, Tomography.

[259]  J Michael O'Connor,et al.  Generation of voxelized breast phantoms from surgical mastectomy specimens. , 2013, Medical physics.

[260]  Alejandro F Frangi,et al.  A parametric finite element solution of the generalised Bloch-Torrey equation for arbitrary domains. , 2015, Journal of magnetic resonance.

[261]  A Kosunen,et al.  Monte Carlo simulations of occupational radiation doses in interventional radiology. , 2007, The British journal of radiology.

[262]  Magnus Båth,et al.  Monte Carlo simulations of the dosimetry of chest tomosynthesis. , 2010, Radiation protection dosimetry.

[263]  Alessandro Foi,et al.  Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis. , 2019, Medical physics.

[264]  William Hendee,et al.  The Handbook of Medical Image Perception and Techniques. , 2010, Medical physics.

[265]  Takashi NAKAMURA,et al.  Development of General-Purpose Particle and Heavy Ion Transport Monte Carlo Code , 2002 .

[266]  Ehsan Samei,et al.  A real-time Monte Carlo tool for individualized dose estimations in clinical CT , 2019, Physics in medicine and biology.

[267]  Yi Zhang,et al.  Correlation between human and model observer performance for discrimination task in CT , 2014, Physics in medicine and biology.

[268]  Denis S Grebenkov,et al.  A fast random walk algorithm for computing the pulsed-gradient spin-echo signal in multiscale porous media. , 2011, Journal of magnetic resonance.

[269]  C K Abbey,et al.  Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers. , 2001, Medical physics.

[270]  Geoffrey McLennan,et al.  CT-based geometry analysis and finite element models of the human and ovine bronchial tree. , 2004, Journal of applied physiology.

[271]  Craig K. Abbey,et al.  Model observers for signal-known-statistically tasks (SKS) , 2001, SPIE Medical Imaging.

[272]  Premkumar Elangovan,et al.  Using transfer learning for a deep learning model observer , 2019, Medical Imaging.

[273]  Habib Zaidi,et al.  Current status and new horizons in Monte Carlo simulation of X-ray CT scanners , 2007, Medical & Biological Engineering & Computing.

[274]  M. Hamilton,et al.  Time‐domain modeling of pulsed finite‐amplitude sound beams , 1995 .

[275]  Gregory M. Sturgeon,et al.  Airways, vasculature, and interstitial tissue: anatomically informed computational modeling of human lungs for virtual clinical trials , 2017, Medical Imaging.

[276]  Cezary Szmigielski,et al.  Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images , 2018, International Journal of Computer Assisted Radiology and Surgery.

[277]  David Gavaghan,et al.  Development of a functional magnetic resonance imaging simulator for modeling realistic rigid‐body motion artifacts , 2006, Magnetic resonance in medicine.

[278]  Ehsan Samei,et al.  A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging , 2014, Physics in medicine and biology.

[279]  Xin He,et al.  Model Observers in Medical Imaging Research , 2013, Theranostics.

[280]  Ehsan Samei,et al.  The Effect of Contrast Material on Radiation Dose at CT: Part I. Incorporation of Contrast Material Dynamics in Anthropomorphic Phantoms. , 2017, Radiology.

[281]  J Bittoun,et al.  A computer algorithm for the simulation of any nuclear magnetic resonance (NMR) imaging method. , 1984, Magnetic resonance imaging.

[282]  Min Cheol Han,et al.  Continuously Deforming 4D Voxel Phantom for Realistic Representation of Respiratory Motion in Monte Carlo Dose Calculation , 2016, IEEE Transactions on Nuclear Science.

[283]  Mini Das,et al.  Optimizing breast-tomosynthesis acquisition parameters with scanning model observers , 2008, SPIE Medical Imaging.

[284]  Bruno De Man,et al.  CatSim: a new computer assisted tomography simulation environment , 2007, SPIE Medical Imaging.

[285]  George R Duensing,et al.  SmartPhantom--an fMRI simulator. , 2006, Magnetic resonance imaging.

[286]  T. Douglas Mast Two- and three-dimensional simulations of ultrasonic propagation through human breast tissue , 2002 .

[287]  Ryoichi Kose,et al.  A Fast GPU-optimized 3D MRI Simulator for Arbitrary k-space Sampling , 2018, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[288]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[289]  M. Frijlink,et al.  Abersim: A simulation program for 3D nonlinear acoustic wave propagation for arbitrary pulses and arbitrary transducer geometries , 2008, 2008 IEEE Ultrasonics Symposium.

[290]  Habib Zaidi,et al.  Assessment of CT dose to the fetus and pregnant female patient using patient-specific computational models , 2018, European Radiology.

[291]  Eric A Hoffman,et al.  The lung physiome: merging imaging‐based measures with predictive computational models , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.

[292]  C Lee,et al.  SU-E-I-44: The UF/NCI Family of Hybrid Computational Phantoms Representing the Current US Population of Male and Female Children and Adolescents Applications to CT Organ Dosimetry. , 2012, Medical physics.

[293]  Yiming Gao,et al.  VirtualDose: a software for reporting organ doses from CT for adult and pediatric patients , 2015, Physics in medicine and biology.

[294]  Zhipeng Cao,et al.  Bloch‐based MRI system simulator considering realistic electromagnetic fields for calculation of signal, noise, and specific absorption rate , 2014, Magnetic resonance in medicine.

[295]  Michael Ghaly,et al.  Optimization of energy window and evaluation of scatter compensation methods in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer , 2015, Journal of medical imaging.

[296]  Michael Sandborg,et al.  A Monte Carlo-based model for simulation of digital chest tomosynthesis. , 2010, Radiation protection dosimetry.

[297]  Ehsan Samei,et al.  Patient-specific radiation dose and cancer risk estimation in CT: part I. development and validation of a Monte Carlo program. , 2010, Medical physics.

[298]  B.M.W. Tsui,et al.  Physiologically realistic LV models to produce normal and pathological image and phantom data , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[299]  Matthew A Kupinski,et al.  Correlation between a 2D channelized Hotelling observer and human observers in a low‐contrast detection task with multislice reading in CT , 2017, Medical physics.

[300]  G. Fung,et al.  Development of a model of the coronary arterial tree for the 4D XCAT phantom. , 2011, Physics in medicine and biology.

[301]  Peter J. Hunter,et al.  Modeling RBC and Neutrophil Distribution Through an Anatomically Based Pulmonary Capillary Network , 2004, Annals of Biomedical Engineering.

[302]  Maryellen L. Giger,et al.  Ideal observer approximation using Bayesian classification neural networks , 2001, IEEE Transactions on Medical Imaging.

[303]  Eric C. Frey,et al.  Development of a Customizable Hepatic Arterial Tree and Particle Transport Model for Use in Treatment Planning , 2019, IEEE Transactions on Radiation and Plasma Medical Sciences.

[304]  Eric C. Frey,et al.  Modeling the scatter response function in inhomogeneous scattering media for SPECT , 1994 .

[305]  Michael Lustig,et al.  k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity , 2006 .

[306]  Andrzej Materka,et al.  Computer Simulation of Magnetic Resonance Angiography Imaging: Model Description and Validation , 2014, PloS one.

[307]  Daniel C. Alexander,et al.  Convergence and Parameter Choice for Monte-Carlo Simulations of Diffusion MRI , 2009, IEEE Transactions on Medical Imaging.

[308]  Stéphanie Salmon,et al.  Flow MRI simulation in complex 3D geometries: Application to the cerebral venous network , 2018, Magnetic resonance in medicine.

[309]  Cynthia B Paschal,et al.  Simulation study of susceptibility gradients leading to focal myocardial signal loss , 2008, Journal of magnetic resonance imaging : JMRI.

[310]  Adam S Wang,et al.  Pulse pileup statistics for energy discriminating photon counting x-ray detectors. , 2011, Medical physics.

[311]  Nicholas Ayache,et al.  Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases , 2018, IEEE Transactions on Medical Imaging.

[312]  R. F. Wagner,et al.  Unified SNR analysis of medical imaging systems , 1985, Physics in medicine and biology.

[313]  W. Paul Segars,et al.  Virtual clinical trial in action: textured XCAT phantoms and scanner-specific CT simulator to characterize noise across CT reconstruction algorithms , 2018, Medical Imaging.

[314]  Ehsan Samei,et al.  Dose coefficients in pediatric and adult abdominopelvic CT based on 100 patient models , 2013, Physics in medicine and biology.

[315]  Lars Hoff Simulations of Nonlinear Bubble Response , 2001 .

[316]  Giovanni Mettivier,et al.  Method for measuring the focal spot size of an x-ray tube using a coded aperture mask and a digital detector. , 2011, Medical physics.

[317]  Lawrence Wc Chan,et al.  Simulation, visualization and dosimetric validation of scatter radiation distribution under fluoroscopy settings , 2015 .

[318]  Andrew D. A. Maidment,et al.  Multiple-reader, multiple-case ROC analysis for determining the limit of calcification detection in tomosynthesis , 2019, Medical Imaging.

[319]  Yeong Shiong Chiew,et al.  Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them , 2018, BioMedical Engineering OnLine.

[320]  Andrew D. A. Maidment,et al.  Realistic Simulation of Breast Tissue Microstructure in Software Anthropomorphic Phantoms , 2014, Digital Mammography / IWDM.

[321]  Mini Das,et al.  Visual-search observers for assessing tomographic x-ray image quality. , 2016, Medical physics.

[322]  Ehsan Samei,et al.  Automated characterization of perceptual quality of clinical chest radiographs: validation and calibration to observer preference. , 2014, Medical physics.

[323]  Gianmarco Pinton,et al.  Nonlinear Ultrasound Propagation in Homogeneous and Heterogeneous Media: Factors Affecting the in situ Mechanical Index (MI) , 2017, 2017 IEEE International Ultrasonics Symposium (IUS).

[324]  Ehsan Samei,et al.  Patient-based estimation of organ dose for a population of 58 adult patients across 13 protocol categories. , 2014, Medical physics.

[325]  D. C. Barber,et al.  Medical Imaging-The Assessment of Image Quality , 1996 .

[326]  J. Jensen,et al.  Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[327]  Craig K. Abbey,et al.  Objective Assessment of Sonographic Quality I: Task Information , 2013, IEEE Transactions on Medical Imaging.

[328]  Gregory M. Sturgeon,et al.  Eigenbreasts for statistical breast phantoms , 2016, SPIE Medical Imaging.

[329]  Bin He,et al.  Comparison of RF body coils for MRI at 3  T: a simulation study using parallel transmission on various anatomical targets , 2015, NMR in biomedicine.

[330]  W P Segars,et al.  Realistic CT simulation using the 4D XCAT phantom. , 2008, Medical physics.

[331]  Ehsan Samei,et al.  Estimability index for volume quantification of homogeneous spherical lesions in computed tomography , 2018, Journal of medical imaging.

[332]  M. Burger,et al.  Total Variation Regularisation in Measurement and Image space for PET reconstruction , 2014, 1403.1272.

[333]  Peter Herscovitch,et al.  Single-Photon Emission Computed Tomography (SPECT) , 2014 .

[334]  Mark L. Palmeri,et al.  Guidelines for Finite-Element Modeling of Acoustic Radiation Force-Induced Shear Wave Propagation in Tissue-Mimicking Media , 2017, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[335]  Grace J Gang,et al.  Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance. , 2011, Medical physics.

[336]  Satoshi Miyajima,et al.  CdZnTe detector in diagnostic x-ray spectroscopy. , 2002, Medical physics.

[337]  Ehsan Samei,et al.  An angle-dependent estimation of CT x-ray spectrum from rotational transmission measurements. , 2014, Medical physics.

[338]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

[339]  T R Fewell,et al.  Photon energy distribution of some typical diagnostic x-ray beams. , 1977, Medical physics.

[340]  J. Helpern,et al.  Monte Carlo study of a two‐compartment exchange model of diffusion , 2010, NMR in biomedicine.

[341]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[342]  Premkumar Elangovan,et al.  A deep learning model observer for use in alterative forced choice virtual clinical trials , 2018, Medical Imaging.

[343]  Katsuyuki Taguchi,et al.  Modeling the performance of a photon counting x-ray detector for CT: energy response and pulse pileup effects. , 2011, Medical physics.

[344]  Mini Das,et al.  Towards visual-search model observers for mass detection in breast tomosynthesis , 2013, Medical Imaging.

[345]  W E Bolch,et al.  Voxel-based models representing the male and female ICRP reference adult--the skeleton. , 2007, Radiation protection dosimetry.

[346]  Katsuyuki Taguchi,et al.  An analytical model of the effects of pulse pileup on the energy spectrum recorded by energy resolved photon counting x-ray detectors , 2010, Medical Imaging.

[347]  Luis de Sisternes,et al.  A computational model to generate simulated three-dimensional breast masses. , 2015, Medical physics.

[348]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[349]  C A Giannone Monte Carlo Calculations in Nuclear Medicine: Applications in Diagnostic Imaging , 1999 .

[350]  Christos G Xanthis,et al.  coreMRI: A high-performance, publicly available MR simulation platform on the cloud , 2019, PloS one.

[351]  S. Incerti,et al.  Geant4 developments and applications , 2006, IEEE Transactions on Nuclear Science.

[352]  Fang-Fang Yin,et al.  Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study. , 2011, Medical physics.

[353]  Gregory M. Sturgeon,et al.  Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins , 2018, IEEE Transactions on Medical Imaging.

[354]  Nooshin Kiarashi,et al.  Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study , 2016, Journal of medical imaging.

[355]  Raúl San José Estépar,et al.  Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms , 2015, bioRxiv.

[356]  R. Harrington Part II , 2004 .

[357]  Adam Wunderlich,et al.  Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies , 2015, IEEE Transactions on Medical Imaging.

[358]  B.M.W. Tsui,et al.  Modeling respiratory motion variations in the 4D NCAT phantom , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[359]  Peter Gatehouse,et al.  Variability of myocardial perfusion dark rim Gibbs artifacts due to sub-pixel shifts , 2009, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[360]  Avan Suinesiaputra,et al.  Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank , 2019, Journal of Cardiovascular Magnetic Resonance.

[361]  K Bliznakova,et al.  Dual-energy mammography: simulation studies. , 2006, Physics in medicine and biology.

[362]  Ehsan Samei,et al.  An Improved Index of Image Quality for Task-based Performance of CT Iterative Reconstruction across Three Commercial Implementations. , 2015, Radiology.

[363]  Andrew D. A. Maidment,et al.  Developing populations of software breast phantoms for virtual clinical trials , 2018, Other Conferences.

[364]  Jay Bartroff,et al.  Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks. , 2003, Optics express.

[365]  C. D'Orsi,et al.  Computation of the glandular radiation dose in digital tomosynthesis of the breast. , 2006, Medical physics.

[366]  Ryoichi Kose,et al.  BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences. , 2017, Journal of magnetic resonance.

[367]  Joshua E. Soneson,et al.  A User‐Friendly Software Package for HIFU Simulation , 2009 .

[368]  J Law Measurement of focal spot size in mammography X-ray tubes. , 1993, The British journal of radiology.