Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering - A Topical Review

Dose, dose, dose, but where is the patient dose?

The article reviews the historical developments in radiation dose metrices in medical imaging. It identifies the good, the bad, and the ugly aspects of current-day metrices. The actions on shifting focus from International Commission on Radiological Protection (ICRP) Reference-Man-based population-average phantoms to patient-specific computational phantoms have been proposed and discussed. Technological developments in recent years involving AI-based automatic organ segmentation and 'near real-time' Monte Carlo dose calculations suggest the feasibility and advantage of obtaining patient-specific organ doses. It appears that the time for ICRP and other international organizations to embrace 'patient-specific' dose quantity representing risk may have finally come. While the existing dose metrices meet specific demands, emphasis needs to be also placed on making radiation units understandable to the medical community.

Automatic vessel attenuation measurement for quality control of contrast-enhanced CT: Validation on the portal vein

BACKGROUND

Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.

PURPOSE

The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CT examinations.

METHODS

Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast-enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations.

RESULTS

The best classifier was found to be a random forest, with a precision of 0.892 in the held-out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU.

CONCLUSIONS

The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time-consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast-enhanced CT examinations.

Investigation of alternative RF power limit control methods for 0.5T, 1.5T, and 3T parallel transmission cardiac imaging: A simulation study

PURPOSE

To investigate safety and performance aspects of parallel-transmit (pTx) RF control-modes for a body coil atB 0 ≤ 3 T $$ {B}_0\le 3\mathrm{T} $$ .

METHODS

Electromagnetic simulations of 11 human voxel models in cardiac imaging position were conducted forB 0 = 0.5 T $$ {B}_0=0.5\mathrm{T} $$ ,1.5 T $$ 1.5\mathrm{T} $$ and3 T $$ 3\mathrm{T} $$ and a body coil with a configurable number of transmit channels (1, 2, 4, 8, 16). Three safety modes were considered: the 'SAR-controlled mode' (SCM), where specific absorption rate (SAR) is limited directly, a 'phase agnostic SAR-controlled mode' (PASCM), where phase information is neglected, and a 'power-controlled mode' (PCM), where the voltage amplitude for each channel is limited. For either mode, safety limits were established based on a set of 'anchor' simulations and then evaluated in 'target' simulations on previously unseen models. The comparison allowed to derive safety factors accounting for varying patient anatomies. All control modes were compared in terms of theB 1 + $$ {B}_1^{+} $$ amplitude and homogeneity they permit under their respective safety requirements.

RESULTS

Large safety factors (approximately five) are needed if only one or two anchor models are investigated but they shrink with increasing number of anchors. The achievableB 1 + $$ {B}_1^{+} $$ is highest for SCM but this advantage is reduced when the safety factor is included. PCM appears to be more robust against variations of subjects. PASCM performance is mostly in between SCM and PCM. Compared to standard circularly polarized (CP) excitation, pTx offers minorB 1 + $$ {B}_1^{+} $$ improvements if local SAR limits are always enforced.

CONCLUSION

PTx body coils can safely be used atB 0 ≤ 3 T $$ {B}_0\le 3\mathrm{T} $$ . Uncertainties in patient anatomy must be accounted for, however, by simulating many models.

A simulation-based phantom model for generating synthetic mitral valve image data-application to MRI acquisition planning

PURPOSE

Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve.

METHODS

We present a pipeline for synthetic image generation based on the combination of individual anatomical 3D models with a position-based dynamics simulation of the mitral valve closure. The corresponding images are generated using modality-specific intensity models and spatiotemporal sampling concepts. We test the applicability in the context of MRI imaging strategies for the assessment of the mitral valve. Synthetic images are generated with different strategies regarding image orientation (SAX and rLAX) and spatial sampling density.

RESULTS

The suitability of the imaging strategy is evaluated by comparing MV segmentations against ground truth annotations. The generated synthetic images were compared to ones acquired with similar parameters, and the result is promising. The quantitative analysis of annotation results suggests that the rLAX sampling strategy is preferable for MV assessment, reaching accuracy values that are comparable to or even outperform literature values.

CONCLUSION

The proposed approach provides a valuable tool for the evaluation and optimization of cardiac valve image acquisition. Its application to the use case identifies the radial image sampling strategy as the most suitable for MV assessment through MRI.

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms

BACKGROUND

Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion.

PURPOSE

We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle.

METHODS

Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations.

RESULTS

Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms.

CONCLUSIONS

A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion.

Development of Respiratory Tract Organs for ICRP Pediatric Mesh-type Reference Computational Phantoms

ABSTRACT

As part of the activities of the International Commission on Radiological Protection (ICRP) Task Group 103, the present study developed a new set of respiratory tract organs consisting of the extrathoracic, bronchial, bronchiolar, and alveolar-interstitial regions for newborn, 1-, 5-, 10-, and 15-y-old males and females for use in pediatric mesh-type reference computational phantoms. The developed respiratory tract organs, while preserving the original topologies of those of the pediatric voxel-type reference computational phantoms of ICRP Publication 143, have improved anatomy and detailed structure and also include μm-thick target and source regions prescribed in ICRP Publication 66. The dosimetric impact of the developed respiratory tract organs was investigated by calculating the specific absorbed fraction for internal electron exposures, which were then compared with the ICRP Task Group 96 values. The results showed that except for the alveolar-interstitial region as a source region, the pediatric mesh phantoms showed larger specific absorbed fractions than the Task Group 96 values. The maximum difference was a factor of ~3.5 for the extrathoracic-2 basal cell and surface as target and source regions, respectively. These results reflect the differences in the target masses and geometry caused by the anatomical enhancement of the pediatric mesh phantoms. For the alveolar-interstitial region as a source region, the pediatric mesh phantoms showed larger values for low energy ranges and lower values with increasing energies, owing to the differences in the size and shape of the alveolar-interstitial region.

In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation

BACKGROUND

In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.

PURPOSE

The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.

METHODS

The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.

RESULTS

The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law= 1.2 ± 1 . 2 o $ = \;1.2 \pm {1.2^o}$ ), radii (median Murray deviation= 0.08 $ = \;0.08$ ), and smoothly curved, non-intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01).

CONCLUSIONS

This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

Synthetic biomedical data generation in support of In Silico Clinical Trials

Living in the era of Big Data, one may advocate that the additional synthetic generation of data is redundant. However, to be able to truly say whether it is valid or not, one needs to focus more on the meaning and quality of data than on the quantity. In some domains, such as biomedical and translational sciences, data privacy still holds a higher importance than data sharing. This by default limits access to valuable research data. Intensive discussion, agreements, and conventions among different medical research players, as well as effective techniques and regulations for data anonymization, already made a big step toward simplification of data sharing. However, the situation with the availability of data about rare diseases or outcomes of novel treatments still requires costly and risky clinical trials and, thus, would greatly benefit from smart data generation. Clinical trials and tests on animals initiate a cyclic procedure that may involve multiple redesigns and retesting, which typically takes two or three years for medical devices and up to eight years for novel medicines, and costs between 10 and 20 million euros. The US Food and Drug Administration (FDA) acknowledges that for many novel devices, practical limitations require alternative approaches, such as computer modeling and engineering tests, to conduct large, randomized studies. In this article, we give an overview of global initiatives advocating for computer simulations in support of the 3R principles (Replacement, Reduction, and Refinement) in humane experimentation. We also present several research works that have developed methodologies of smart and comprehensive generation of synthetic biomedical data, such as virtual cohorts of patients, in support of In Silico Clinical Trials (ISCT) and discuss their common ground.

Validation of a Monte Carlo simulated cardiac phantom for planar and SPECT studies

PURPOSE

This work aimed to validate Monte Carlo (MC) simulated cardiac phantoms for the evaluation of planar- and SPECT-gated-blood-pool (GBP-P and GBP-S) studies.

METHODS

A comparison of gamma camera system performance criteria measurements (energy resolution, spatial resolution, sensitivity) with MC simulations was conducted. Furthermore, the accuracy of measured and simulated volumes of two stereolithography-printed cardiac phantoms (based on 4D-XCAT phantoms) was assessed. Finally, the simulated GBP-P and GBP-S XCAT studies were validated by comparing calculated left ventricular ejection fraction (LVEF) and ventricle volume values with known parameters.

RESULTS

The simulated performance criteria compared well with measured values (energy resolution difference: 0.1 ± 0.10%; spatial resolution (full width at half maximum) difference ≤ 0.5 ± 0.8 mm and system sensitivity difference ≤ 6.2 ± 0.62cps/MBq). The measured and simulated cardiac phantoms were in good agreement; the left anterior oblique views compared well. This is supported by line profiles through these phantoms and on average, simulated counts were 5.8% lower than measured counts. The LVEF values calculated from the GBP-P and GBP-S simulated data differ from known values (2.8 ± 0.64% and 0.8 ± 0.52%). The differences between the known XCAT LV volumes and simulated GBP-S calculated volumes were -1.2 ± 1.91 ml and -1.5 ± 0.96 ml for the end-diastolic and end-systolic volumes.

CONCLUSION

The MC-simulated cardiac phantom has been validated successfully. Stereolithography-printing allows researchers to create clinically realistic organ phantoms and is a valuable tool for validating MC simulations and clinical software. By conducting GBP simulation studies with various XCAT models, the user will be able to generate GBP-P and GBP-S databases for future software evaluation.

An Investigation of Body Size Impact on Organ Doses for Neutron External Exposures Using the MRCP-based Phantom Library

ABSTRACT

In a recent study, a comprehensive library composed of 212 phantoms with different body sizes was established by deforming the adult male and female mesh-type reference computational phantoms (MRCPs) of ICRP Publication 145 and the next-generation ICRP reference phantoms over the current voxel-type reference phantoms of ICRP Publication 110. In this study, as an application of the MRCP-based phantom library, we investigated dosimetric impacts due to the different body sizes for neutron external exposures. A comprehensive dataset of organ/tissue dose coefficients (DCs) for idealized external neutron beams with four phantoms for each sex representatively selected from the phantom library were produced by performing Monte Carlo simulations using the Geant4 code. The body size-dependent DCs produced in this study were systematically analyzed, observing that the variation of the body weights overall played a more important role in organ/tissue dose calculations than the variation of the body heights. We also observed that the reference body-size DCs based on the MRCPs indeed significantly under- or overestimated the DCs produced using the phantoms, especially for those much heavier (male: 175 cm and 140 kg; female: 165 cm and 140 kg) than the reference body sizes (male: 176 cm and 73 kg; female: 163 cm and 60 kg) by up to 1.6 or 3.3 times, respectively. We believe that the use of the body size-dependent DCs, together with the reference body-size DCs, should be beneficial for more reliable organ/tissue dose estimates of individuals considering their body sizes rather than the most common conventional approach, i.e., the sole use of the reference body size DCs.

PET scatter estimation using deep learning U-Net architecture

Objective.Positron emission tomography (PET) image reconstruction needs to be corrected for scatter in order to produce quantitatively accurate images. Scatter correction is traditionally achieved by incorporating an estimated scatter sinogram into the forward model during image reconstruction. Existing scatter estimated methods compromise between accuracy and computing time. Nowadays scatter estimation is routinely performed using single scatter simulation (SSS), which does not accurately model multiple scatter and scatter from outside the field-of-view, leading to reduced qualitative and quantitative PET reconstructed image accuracy. On the other side, Monte-Carlo (MC) methods provide a high precision, but are computationally expensive and time-consuming, even with recent progress in MC acceleration.Approach.In this work we explore the potential of deep learning (DL) for accurate scatter correction in PET imaging, accounting for all scatter coincidences. We propose a network based on a U-Net convolutional neural network architecture with 5 convolutional layers. The network takes as input the emission and computed tomography (CT)-derived attenuation factor (AF) sinograms and returns the estimated scatter sinogram. The network training was performed using MC simulated PET datasets. Multiple anthropomorphic extended cardiac-torso phantoms of two different regions (lung and pelvis) were created, considering three different body sizes and different levels of statistics. In addition, two patient datasets were used to assess the performance of the method in clinical practice.Main results.Our experiments showed that the accuracy of our method, namely DL-based scatter estimation (DLSE), was independent of the anatomical region (lungs or pelvis). They also showed that the DLSE-corrected images were similar to that reconstructed from scatter-free data and more accurate than SSS-corrected images.Significance.The proposed method is able to estimate scatter sinograms from emission and attenuation data. It has shown a better accuracy than the SSS, while being faster than MC scatter estimation methods.

Absorbed dose calculation for a realistic CT-derived mouse phantom irradiated with a standard Cs-137 cell irradiator using a Monte Carlo method

Computed tomography (CT) derived Monte Carlo (MC) phantoms allow dose determination within small animal models that is not feasible with in-vivo dosimetry. The aim of this study was to develop a CT-derived MC phantom generated from a mouse with a xenograft tumour that could then be used to calculate both the dose heterogeneity in the tumour volume and out of field scattered dose for pre-clinical small animal irradiation experiments. A BEAMnrc Monte-Carlo model has been built of our irradiation system that comprises a lead collimator with a 1 cm diameter aperture fitted to a Cs-137 gamma irradiator. The MC model of the irradiation system was validated by comparing the calculated dose results with dosimetric film measurement in a polymethyl methacrylate (PMMA) phantom using a 1D gamma-index analysis. Dose distributions in the MC mouse phantom were calculated and visualized on the CT-image data. Dose volume histograms (DVHs) were generated for the tumour and organs at risk (OARs). The effect of the xenographic tumour volume on the scattered out of field dose was also investigated. The defined gamma index analysis criteria were met, indicating that our MC simulation is a valid model for MC mouse phantom dose calculations. MC dose calculations showed a maximum out of field dose to the mouse of 7% of Dmax. Absorbed dose to the tumour varies in the range 60%-100% of Dmax. DVH analysis demonstrated that tumour received an inhomogeneous dose of 12 Gy-20 Gy (for 20 Gy prescribed dose) while out of field doses to all OARs were minimized (1.29 Gy-1.38 Gy). Variation of the xenographic tumour volume exhibited no significant effect on the out of field scattered dose to OARs. The CT derived MC mouse model presented here is a useful tool for tumour dose verifications as well as investigating the doses to normal tissue (in out of field) for preclinical radiobiological research.

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)

An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.

Modeling a 3-D multiscale blood-flow and heat-transfer framework for realistic vascular systems

Modeling of biological domains and simulation of biophysical processes occurring in them can help inform medical procedures. However, when considering complex domains such as large regions of the human body, the complexities of blood vessel branching and variation of blood vessel dimensions present a major modeling challenge. Here, we present a Voxelized Multi-Physics Simulation (VoM-PhyS) framework to simulate coupled heat transfer and fluid flow using a multi-scale voxel mesh on a biological domain obtained. In this framework, flow in larger blood vessels is modeled using the Hagen–Poiseuille equation for a one-dimensional flow coupled with a three-dimensional two-compartment porous media model for capillary circulation in tissue. The Dirac distribution function is used as Sphere of Influence (SoI) parameter to couple the one-dimensional and three-dimensional flow. This blood flow system is coupled with a heat transfer solver to provide a complete thermo-physiological simulation. The framework is demonstrated on a frog tongue and further analysis is conducted to study the effect of convective heat exchange between blood vessels and tissue, and the effect of SoI on simulation results.

Development of alimentary tract organs for ICRP pediatric mesh-type reference computational phantoms

In line with the activities of Task Group 103 under the International Commission on Radiological Protection (ICRP), the present study was conducted to develop a new set of alimentary tract organs consisting of the oral cavity, oesophagus, stomach, small intestine, and colon for the newborn, 1 year-old, 5 year-old, 10 year-old, and 15 year-old males and females for use in the pediatric mesh-type reference computational phantoms (MRCPs). The developed alimentary tract organs of the pediatric MRCPs, while nearly preserving the original topology and shape of those of the pediatric voxel-type reference computational phantoms (VRCPs) of ICRPPublication 143, present considerable anatomical improvement and include all micrometre-scale target and source regions as prescribed in ICRPPublication 100. To investigate the dosimetric impact of the developed alimentary tract organs, organ doses and specific absorbed fractions were computed for certain external exposures to photons and electrons and internal exposures to electrons, respectively, which were then compared with the values computed using the current ICRP models (i.e. pediatric VRCPs and ICRP-100 stylised models). The results showed that for external exposures to penetrating radiations (i.e. photons >0.04 MeV), there was generally good agreement between the compared values, within a 10% difference, except for the oral mucosa. For external exposures to weakly penetrating radiations (i.e. low-energy photons and electrons), there were significant differences, up to a factor of ∼8300, owing to the geometric difference caused by the anatomical enhancement in the MRCPs. For internal exposures of electrons, there were significant differences, the maximum of which reached a factor of ∼73 000. This was attributed not only to the geometric difference but also to the target mass difference caused by the different luminal content mass and organ shape.

Construction of A Digital Fetus Library for Radiation Dosimetry

PURPOSE

Accurate estimation of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radio-sensitivity of the fetus. Computational anthropomorphic models have been widely used in patient-specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies.

ACQUISITION AND VALIDATION METHODS

Computed tomography (CT) images of abdominal and pelvic regions of 46 pregnant females were segmented by experienced medical physicists. The segmented tissues/organs include the body contour, skeleton, uterus, liver, kidney, intestine, stomach, lung, bladder, gall bladder, spleen and pancreas for maternal body, and placenta, amniotic fluid, fetal body, fetal brain and fetal skeleton. Non-Uniform Rational B-Spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit (HU) values of each identified organs were gathered from CT images of pregnant patients and converted to tissue density. Organ volumes were further adjusted according to reference measurements for the developing fetus recommended by the World Health Organization (WHO) and International Commission on Radiological Protection (ICRP). A series of anatomical parameters, including femur length (FL), humerus length (HL), biparietal diameter (BPD), abdominal circumference (FAC) and head circumference (HC) were measured and compared with WHO recommendations.

DATA FORMAT AND USAGE NOTES

The first fetal patient-specific model library was developed with the anatomical characteristics of each model derived from the corresponding patient whose gestational age varies between 8-weeks and 35-weeks. Voxelized models are represented in the form of MCNP matrix input files representing the three-dimensional model of the fetus. The size distributions of each model are also provided in text files. All data are stored on Zenodo and are publicly accessible on the following link: https://zenodo.org/record/6471884.

POTENTIAL APPLICATIONS

The constructed fetal models and maternal anatomical characteristics are consistent with the corresponding patients. The resulting computational fetus could be used in radiation dosimetry studies to improve the reliability of fetal dosimetry and radiation risks assessment. The advantages of NURBS surfaces in terms of adapting fetal postures and positions enable us to adequately assess their impact on radiation dosimetry calculations. This article is protected by copyright. All rights reserved.

Criteria for the design of tissue-mimicking phantoms for the standardization of biophotonic instrumentation

A lack of accepted standards and standardized phantoms suitable for the technical validation of biophotonic instrumentation hinders the reliability and reproducibility of its experimental outputs. In this Perspective, we discuss general criteria for the design of tissue-mimicking biophotonic phantoms, and use these criteria and state-of-the-art developments to critically review the literature on phantom materials and on the fabrication of phantoms. By focusing on representative examples of standardization in diffuse optical imaging and spectroscopy, fluorescence-guided surgery and photoacoustic imaging, we identify unmet needs in the development of phantoms and a set of criteria (leveraging characterization, collaboration, communication and commitment) for the standardization of biophotonic instrumentation.

Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations

Purpose

Respiratory motion is one of the major challenges in radiotherapy. In this work, a comprehensive and clinically plausible set of 4D numerical phantoms, together with their corresponding “ground truths,” have been developed and validated for 4D radiotherapy applications.

Methods

The phantoms are based on CTs providing density information and motion from multi‐breathing‐cycle 4D Magnetic Resonance imagings (MRIs). Deformable image registration (DIR) has been utilized to extract motion fields from 4DMRIs and to establish inter‐subject correspondence by registering binary lung masks between Computer Tomography (CT) and MRI. The established correspondence is then used to warp the CT according to the 4DMRI motion. The resulting synthetic 4DCTs are called 4DCT(MRI)s. Validation of the 4DCT(MRI) workflow was conducted by directly comparing conventional 4DCTs to derived synthetic 4D images using the motion of the 4DCTs themselves (referred to as 4DCT(CT)s). Digitally reconstructed radiographs (DRRs) as well as 4D pencil beam scanned (PBS) proton dose calculations were used for validation.

Results

Based on the CT image appearance of 13 lung cancer patients and deformable motion of five volunteer 4DMRIs, synthetic 4DCT(MRI)s with a total of 871 different breathing cycles have been generated. The 4DCT(MRI)s exhibit an average superior–inferior tumor motion amplitude of 7 ± 5 mm (min: 0.5 mm, max: 22.7 mm). The relative change of the DRR image intensities of the conventional 4DCTs and the corresponding synthetic 4DCT(CT)s inside the body is smaller than 5% for at least 81% of the pixels for all studied cases. Comparison of 4D dose distributions calculated on 4DCTs and the synthetic 4DCT(CT)s using the same motion achieved similar dose distributions with an average 2%/2 mm gamma pass rate of 90.8% (min: 77.8%, max: 97.2%).

Conclusion

We developed a series of numerical 4D lung phantoms based on real imaging and motion data, which give realistic representations of both anatomy and motion scenarios and the accessible “ground truth” deformation vector fields of each 4DCT(MRI). The open‐source code and motion data allow foreseen users to generate further 4D data by themselves. These numeric 4D phantoms can be used for the development of new 4D treatment strategies, 4D dose calculations, DIR algorithm validations, as well as simulations of motion mitigation and different online image guidance techniques for both proton and photon radiation therapy.

An update on computational anthropomorphic anatomical models

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.

Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation

Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic 2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. A convolutional neural network is trained to segment the left ventricle and left atrium using only synthetic images. Networks trained with synthetic images were extensively tested on four different unseen datasets of real images with median Dice scores of 91, 90, 88, and 87 for left ventricle segmentation. These results match or are better than inter-observer results measured on real ultrasound datasets and are comparable to a network trained on a separate set of real images. Results demonstrate the images produced can effectively be used in place of real data for training. The proposed pipeline opens the door for automatic generation of training data for many tasks in medical imaging as the same process can be applied to other segmentation or landmark detection tasks in any modality. The source code and anatomical models are available to other researchers.1 1https://adgilbert.github.io/data-generation/.

ESHO benchmarks for computational modeling and optimization in hyperthermia therapy

BACKGROUND

The success of cancer hyperthermia (HT) treatments is strongly dependent on the temperatures achieved in the tumor and healthy tissues as it correlates with treatment efficacy and safety, respectively. Hyperthermia treatment planning (HTP) simulations have become pivotal for treatment optimization due to the possibility for pretreatment planning, optimization and decision making, as well as real-time treatment guidance.

MATERIALS AND METHODS

The same computational methods deployed in HTP are also used for in silico studies. These are of great relevance for the development of new HT devices and treatment approaches. To aid this work, 3 D patient models have been recently developed and made available for the HT community. Unfortunately, there is no consensus regarding tissue properties, simulation settings, and benchmark applicators, which significantly influence the clinical relevance of computational outcomes.

RESULTS AND DISCUSSION

Herein, we propose a comprehensive set of applicator benchmarks, efficacy and safety optimization algorithms, simulation settings and clinical parameters, to establish benchmarks for method comparison and code verification, to provide guidance, and in view of the 2021 ESHO Grand Challenge (Details on the ESHO grand challenge on HTP will be provided at https://www.esho.info/).

CONCLUSION

We aim to establish guidelines to promote standardization within the hyperthermia community such that novel approaches can quickly prove their benefit as quickly as possible in clinically relevant simulation scenarios. This paper is primarily focused on radiofrequency and microwave hyperthermia but, since 3 D simulation studies on heating with ultrasound are now a reality, guidance as well as a benchmark for ultrasound-based hyperthermia are also included.

Experimental validation of Fastcat kV and MV cone beam CT (CBCT) simulator

PURPOSE

To experimentally validate the Fastcat cone beam computed tomography (CBCT) simulator against kV and MV CBCT images acquired with a Varian Truebeam linac.

METHODS

kV and MV CBCT images of a Catphan 504 phantom were acquired using a 100 kVp beam with the on-board imager (OBI) and a 6 MV treatment beam with the electronic portal imaging device (EPID), respectively. The kV Fastcat simulation was performed using detailed models of the x-ray source, bowtie filter, a high resolution voxelized virtual Catphan phantom, anti-scatter grid, and the CsI scintillating detector. Likewise, an MV Fastcat CBCT was simulated with detailed models for the beam energy spectrum, flattening filter, a high-resolution voxelized virtual Catphan phantom, and the gadolinium oxysulfide (GOS) scintillating detector. Experimental and simulated CBCT images of the phantom were compared with respect to HU values, contrast to noise ratio (CNR), and dose linearity. Detector modulation transfer function (MTF) for the two detectors were also experimentally validated. Fastcat's dose calculations were compared to Monte Carlo (MC) dose calculations performed with Topas.

RESULTS

For the kV and MV simulations, respectively: Contrast agreed within 14 and 9 HUs and detector MTF agreed within 4.2% and 2.5%. Likewise, CNR had a root mean squared error (RMSE) of 2.6% and 1.4%. Dose agreed within 2.4% and 1.6% of MC values. The kV and MV CBCT images took 71 and 72 s to simulate in Fastcat with 887 and 493 projections, respectively.

CONCLUSIONS

We present a multienergy experimental validation of a fast and accurate CBCT simulator against a commercial linac. The simulator is open source and all models found in this work can be downloaded from https://github.com/jerichooconnell/fastcat.git.

Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician

Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-based evaluation of artificial intelligence methods. We provide a list of available tools to conduct this evaluation. We outline the important role of physicians in conducting these evaluation studies. The examples in this article are proposed in the context of PET scans with a focus on evaluating neural network-based methods. However, the framework is also applicable to evaluate other medical imaging modalities and other types of artificial intelligence methods.

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

OBJECTIVE

This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients.

METHOD

Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams.

RESULT

iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors).

CONCLUSION

iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry.

SIGNIFICANCE

The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

Development of skeletal systems for ICRP pediatric mesh-type reference computational phantoms

In 2016, the International Commission on Radiological Protection (ICRP) launched Task Group 103 (TG 103) for the explicit purpose of developing a new generation of adult and pediatric reference computational phantoms, named 'mesh-type reference computational phantoms (MRCPs)', that can overcome the limitations of voxel-type reference computational phantoms (VRCPs) of ICRPPublications 110and143due to their finite voxel resolutions and the nature of voxel geometry. After completing the development of the adult MRCPs, TG 103 has started the development of pediatric MRCPs comprising 10 phantoms (male and female versions of the reference newborn, 1-year-old, 5-year-old, 10-year-old, and 15-year-old). As part of the TG 103 project, within the present study, the skeletal systems, one of the most important and complex organ systems of the body, were developed for each phantom age and sex. The developed skeletal systems, while closely preserving the original bone topology of the pediatric VRCPs, present substantial improvements in the anatomy of complex and/or small bones. In order to investigate the dosimetric impact of the developed skeletons, the average absorbed doses and the specific absorbed fractions for radiosensitive skeletal tissues (i.e. active marrow and bone endosteum) were computed for some selected external and internal exposure cases, which were then compared with those calculated with the skeletons of pediatric VRCPs. The comparison result showed that the dose values of the pediatric MRCPs were generally similar to those of the pediatric VRCPs for highly penetrating radiations (e.g. photons >200 keV); however, for weakly penetrating radiations (e.g. photons ⩽200 keV and electrons), significant differences up to a factor of 140 were observed.

Virtual Imaging Trials for Coronavirus Disease (COVID-19)

OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. CONCLUSION. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.

USE OF ANTHROPOMORPHIC HETEROGENEOUS PHYSICAL PHANTOMS FOR VALIDATION OF COMPUTATIONAL DOSIMETRY OF MEDICAL PERSONNEL AND PATIENTS

In the dosimetry of ionizing radiation, the phantoms of the human body, which are used as a replacement for thehuman body in physical measurements and calculations, play an important, but sometimes underestimated, role.There are physical phantoms used directly for measurements, and mathematical phantoms for computationaldosimetry. Their complexity varies from simple geometry applied for calibration purposes up to very complex, whichsimulates in detail the shapes of organs and tissues of the human body. The use of physical anthropomorphic phantoms makes it possible to effectively optimize radiation doses by adjusting the parameters of CT-scanning (computed tomography) in accordance with the characteristics of the patient without compromising image quality. The useof phantoms is an indispensable approach to estimate the actual doses to the organs or to determine the effectivedose of workers - values that are regulated, but cannot be directly measured.The article contains an overview of types, designs and the fields of application of anthropomorphic heterogeneousphysical phantoms of a human with special emphasis on their use for validation of models and methods of computational dosimetry.

Integration of spatial distortion effects in a 4D computational phantom for simulation studies in extra-cranial MRI-guided radiation therapy: Initial results

PURPOSE

Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue-specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image-guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four-dimensional (4D) computational phantom for simulation studies in MRI-guided radiation therapy at extra-cranial sites.

METHODS

A geometrical spatial distortion phantom was designed in four modules embedding laser-cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm³ . The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra-cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI-guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated.

RESULTS

The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans-axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient-specific measurements was also verified, aiming at increased realism in the simulation.

CONCLUSIONS

The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time-dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra-cranial MRI-guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment.

POLY2TET: a computer program for conversion of computational human phantoms from polygonal mesh to tetrahedral mesh

As a geometrical format for computational human phantoms, tetrahedral mesh (TM) is known to have significant advantages over polygonal mesh (PM), including higher compatibility with Monte Carlo radiation transport codes, higher computation speed, and the capability of modeling heterogeneous density variation in an organ of the phantom. In the present study, a computer program named POLY2TET was developed to convert the format of computational human phantoms from PM to TM and generate a sample source code or input file, as applicable, for the converted phantom to be used in some general-purpose Monte Carlo radiation transport codes (i.e. Geant4, PHITS, and MCNP6). The developed program was then tested using four existing high-fidelity PM phantoms. The computation speed, memory requirement, and initialisation time of the generated TM phantoms were also measured and compared with those of the original PM phantoms in Geant4. From the results of our test, it was concluded that the developed program successfully converts PM phantoms into the TM format. The organ doses calculated using the generated TM phantom for the three Monte Carlo codes all produced essentially identical dose values to those for the original PM phantoms in Geant4. The comparison of computation speed showed that compared to the original PM phantoms in Geant4, the TM phantoms in the three Monte Carlo codes were much faster in transporting the particles considered in the present study, i.e. by up to ∼2600 times for electron beams simulated in PHITS. The comparison of the memory requirement showed that the TM phantoms required more memory than the original PM phantoms, but, except for MCNP6, the memory required for the TM phantoms was still less than 12 GB, which typically is available in personal computers these days. For MCNP6, the required memory was much higher, i.e. 60-70 GB.

Virtual clinical trials in medical imaging: a review

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

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches.

Body-size-dependent phantom library constructed from ICRP mesh-type reference computational phantoms

Recently, ICRP Task Group 103 developed new mesh-type reference computational phantoms (MRCPs) for the adult male and female by converting the current voxel-type reference computational phantoms (VRCPs) of ICRP Publication 110 into a high-quality/fidelity mesh format. Utilizing the great deformability/flexibility of the MRCPs compared with the VRCPs, in the present study, we established a body-size-dependent phantom library by modifying the MRCPs. The established library includes 108 adult male and 104 adult female phantoms in different standing heights and body weights, covering most body sizes representative of Caucasian and Asian populations. Ten secondary anthropometric parameters with respect to standing height and body weight were derived from various anthropometric databases and applied in the construction of the phantom library. An in-house program for automatic phantom adjustment was developed and applied for practical construction of such a large number of phantoms in the library with minimized human intervention. Organ/tissue doses calculated with three male phantoms in different standing heights (165, 175, and 190 cm) with a fixed body weight of 80 kg for external exposures to broad parallel photon beams from 0.01 to 10⁴ MeV were compared, observing there are significant dose differences particularly for the photon energies <0.1 MeV in which the organ/tissue doses tended to increase with increasing standing height. In addition, the organ/tissue doses of three female phantoms in different body weights (45, 65, and 140 kg) with a fixed standing height of 165 cm were compared, showing a significant decreasing tendency with increasing body weight for the photon energies <10 MeV. For the higher energies, the opposite trend, interestingly, was observed; that is, the organ/tissue doses tended to increase with increasing body weight. The results, despite the limited number of exposure cases, suggest that the use of the body-size-dependent phantom library can improve the accuracy of individual dose estimates for many retrospective dosimetry studies by taking the body size of individuals into account.

Total Body PET: Why, How, What for?

PET instruments are now available with a long axial field-of-view (LAFOV) to enable imaging the total-body, or at least head and torso, simultaneously and without bed translation. This has two major benefits, a dramatic increase in system sensitivity and the ability to measure kinetics with wider axial coverage so as to include multiple organs. This manuscript presents a review of the technology leading up to the introduction of these new instruments, and explains the benefits of a LAFOV PET-CT instrument. To date there are two platforms developed for TB-PET, an outcome of the EXPLORER Consortium of the University of California at Davis (UC Davis) and the University of Pennsylvania (Penn). The uEXPLORER at UC Davis has an AFOV of 194 cm and was developed by United Imaging Healthcare. The PennPET EXPLORER was developed at Penn and is based on the digital detector from Philips Healthcare. This multi-ring system is scalable and has been tested with 3 rings but is now being expanded to 6 rings for 140 cm. Initial human studies with both EXPLORER systems have demonstrated the successful implementation and benefits of LAFOV scanners for both clinical and research applications. Examples of such studies are described in this manuscript.

Comparison of Correction Techniques for the Spill in Effect in Emission Tomography

In positron emission tomography (PET) imaging, accurate clinical assessment is often affected by the partial volume effect (PVE) leading to overestimation (spill-in) or underestimation (spill-out) of activity in various small regions. The spill-in correction, in particular, can be very challenging when the target region is close to a hot background region. Therefore, this study evaluates and compares the performance of various recently developed spill-in correction techniques, namely: background correction (BC), local projection (LP), and hybrid kernelized (HKEM) methods. We used a simulated digital phantom and [¹⁸F]-NaF PET data of three patients with abdominal aortic aneurysms (AAA) acquired with Siemens Biograph mMR™ and mCT™ scanners respectively. Region of Interest (ROI) analysis was performed and the extracted SUV _mean , SUV _max and target-to-background ratio (TBR) scores were compared. Results showed substantial spill-in effects from hot regions to targeted regions, which are more prominent in small structures. The phantom experiment demonstrated the feasibility of spill-in correction with all methods. For the patient data, large differences in SUV _mean , SUV _max and TBR _max scores were observed between the ROIs drawn over the entire aneurysm and ROIs excluding some regions close to the bone. Overall, BC yielded the best performance in spill-in correction in both phantom and patient studies.

Dose coefficients of percentile-specific computational phantoms for photon external exposures

The use of dose coefficients (DCs) based on the reference phantoms recommended by the International Commission on Radiological Protection (ICRP) with a fixed body size may produce errors to the estimated organ/tissue doses to be used, for example, for epidemiologic studies depending on the body size of cohort members. A set of percentile-specific computational phantoms that represent 10th, 50th, and 90th percentile standing heights and body masses in adult male and female Caucasian populations were recently developed by modifying the mesh-type ICRP reference computational phantoms (MRCPs). In the present study, these percentile-specific phantoms were used to calculate a comprehensive dataset of body-size-dependent DCs for photon external exposures by performing Monte Carlo dose calculations with the Geant4 code. The dataset includes the DCs of absorbed doses for 29 individual organs/tissues from 0.01 to 104 MeV photon energy, in the antero-posterior, postero-anterior, right lateral, left lateral, rotational, and isotropic geometries. The body-size-dependent DCs were compared with the DCs of the MRCPs in the reference body size, showing that the DCs of the MRCPs are generally similar to those of the 50th percentile standing height and body mass phantoms over the entire photon energy region except for low energies (≤ 0.03 MeV); the differences are mostly less than 10%. In contrast, there are significant differences in the DCs between the MRCPs and the 10th and 90th percentile standing height and body mass phantoms (i.e., H10M10 and H90M90). At energies of less than about 10 MeV, the MRCPs tended to under- and over-estimate the organ/tissue doses of the H10M10 and H90M90 phantoms, respectively. This tendency was revised at higher energies. The DCs of the percentile-specific phantoms were also compared with the previously published values of another phantom sets with similar body sizes, showing significant differences particularly at energies below about 0.1 MeV, which is mainly due to the different locations and depths of organs/tissues between the different phantom libraries. The DCs established in the present study should be useful to improve the dosimetric accuracy in the reconstructions of organ/tissue doses for individuals in risk assessment for epidemiologic investigations taking body sizes into account.

A Structured Cleaving Mesh for Bioheat Transfer Application

Goal: The thermoregulation mechanism is a complex system that executes vital processes in the human body. Various models have been proposed to simulate the thermoregulatory response of an adult human to environmental stimuli. However, these models generally rely on stylized phantoms that lack the anatomical details of voxel phantoms used in radiation dosimetry and shielding research. The goal of this work is to introduce voxel phantoms to thermoregulation research by modeling the physical energy exchange between tissue and its surroundings, discuss a specific challenge associated with voxel phantoms, propose a method to address this challenge, and demonstrate its application. Method: One of the major challenges in using voxel phantoms is the stair-step effect on the surface of the voxelized domain. This effect causes over-estimation of surface area, accurate knowledge of which is critical for modeling heat exchanging systems. A methodology to generate a voxel domain from medical imaging data and reduce error in the surface area caused by the stair-step effect is presented. The methodology, based on a structured mesh and finite-volume method, is demonstrated with tumors generated from magnetic resonance imaging (MRI) scans of mice. Results: The methodology discussed in the paper shows a decrease in surface area over-estimation from 50% to 15% for a sphere and 47% to 17% for tumor models generated directly from MRI scans. Conclusion: This work provides a direct method to generate a smoother domain from medical imaging data and reducing surface area error in a voxelized domain. The technique presented is independent of domain material, including tissue type, and can be extended to any homogeneous or inhomogeneous domain. The increase in surface area accuracy obtained by smoothing the voxel domain results in more accurate temperature estimates in heat transfer simulation.

Radiation Dosimetry of Inhaled Radioactive Aerosols: CFPD and MCNP Transport Simulations of Radionuclides in the Lung

Despite extensive efforts in studying radioactive aerosols, including the transmission of radionuclides in different chemical matrices throughout the body, the internal organ-specific radiation dose due to inhaled radioactive aerosols has largely relied on experimental deposition data and simplified human phantoms. Computational fluid-particle dynamics (CFPD) has proven to be a reliable tool in characterizing aerosol transport in the upper airways, while Monte Carlo based radiation codes allow accurate simulation of radiation transport. The objective of this study is to numerically assess the radiation dosimetry due to particles decaying in the respiratory tract from environmental radioactive exposures by coupling CFPD with Monte Carlo N-Particle code, version 6 (MCNP6). A physiologically realistic mouth-lung model extending to the bifurcation generation G9 was used to simulate airflow and particle transport within the respiratory tract. Polydisperse aerosols with different distributions were considered, and deposition distribution of the inhaled aerosols on the internal airway walls was quantified. The deposition mapping of radioactive aerosols was then registered to the respiratory tract of an image-based whole-body adult male model (VIP-Man) to simulate radiation transport and energy deposition. Computer codes were developed for geometry visualization, spatial normalization, and source card definition in MCNP6. Spatial distributions of internal radiation dosimetry were compared for different radionuclides (¹³¹I, ^134,137Cs, ⁹⁰Sr-⁹⁰Y, ¹⁰³Ru and ^239,240Pu) in terms of the radiation fluence, energy deposition density, and dose per decay.

Dosimetric impact of voxel resolutions of computational human phantoms for external photon exposure

Several research teams have developed computational phantoms in polygonal-mesh (PM) and/or Non-Uniform Rational B-Spline format, but it has not been systematically evaluated if the existing voxel phantoms are still dosimetrically valid. We created three voxel phantoms with the resolutions of 1,000, 125, and 1 mm3 and simulated the irradiation in antero-posterior geometry with photons of 0.1, 1, and 10 MeV using voxel Monte Carlo codes, and compared the energy deposition to their organs/tissues with the values from the original PM phantom using mesh Monte Carlo codes. The coefficient of variation in energy deposition overall showed about five-fold decrease as the voxel resolution increased but differences were mostly less than 5% for any voxel resolution. We conclude that PM phantoms and mesh Monte Carlo techniques may not be necessary for external photon exposure (0.1 - 10 MeV) and the existing voxel phantoms can provide enough dosimetric accuracy in those exposure conditions.

Time-resolved volumetric MRI in MRI-guided radiotherapy: an in silico comparative analysis

MRI-treatment units enable 2D cine-MRI centred in the tumour for motion detection in radiotherapy, but they lack 3D information due to spatio-temporal limits. To derive time-resolved 3D information, different approaches have been proposed in the literature, but a rigorous comparison among these strategies has not yet been performed. The goal of this study is to quantitatively investigate five published strategies that derive time-resolved volumetric MRI in MRI-guided radiotherapy: Propagation, out-of-plane motion compensation, Fayad model, ROI-based model and Stemkens model. Comparisons were performed using an MRI digital phantom generated with six different patient-derived motion signals and tumour-shapes. An average 4D cycle was generated as well as 2D cine-MRI data with corresponding 3D in-room ground truth. Quantitative analysis was performed by comparing the estimated 3D volume to the ground truth available for each 2D cine-MRI sample. A grouped patient statistical analysis was performed to evaluate the performance of the selected methods, in case of tumour tracking or motion estimation of the whole anatomy. Analyses were also performed based on patient characteristics. Quantitative ranking of the investigated methods highlighted that Propagation and ROI-based model strategies achieved an overall median tumour centre of mass 3D distance from the ground truth of 1.1 mm and 1.3 mm, respectively, and a diaphragm distance below 1.6 mm. Higher errors and variabilities were instead obtained for other methods, which lack the ability to compensate for in-room variations and to account for regional changes. These results were especially evident when further analysing patient characteristics, where errors above 2 mm/5 mm in tumour/diaphragm were found for more irregular breathing patterns in case of out-of-plane motion compensation, Fayad and Stemkens models. These findings suggest the potential of the proposed in silico framework to develop and compare strategies to estimate time-resolved 3DMRI in MRI-guided radiotherapy.