Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations.

Objective.To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed to18F-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children.Approach.Data of 29 Chinese pediatric patients undergoing whole-body18F-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities. Absorbed radiation dose for both CT and PET component were calculated using Monte Carlo simulations. Regression models were fitted to time integrated activity coefficient (TIAC) and organ absorbed dose for each patient.Main results.TIACs of all the organs in our compartment model and the organ dose for 12 organs were correlated with patients' weight. Young children have significantly large uptake in brain compared to adults. The distinctions of anatomical and biological characteristics between Chinese and Caucasian children contribute to variations in the absorbed dose of18F-FDG PET scans. PET contributed more in organ dose than CT did in most organs, especially in brain and bladder. The average effective dose (± SD) was 4.5 mSv (± 1.12 mSv), 7.8 mSv (± 3.2 mSv) and 12.3 mSv (± 3.5 mSv) from CT, PET and their sum respectively. PET contributed 1.7 times higher than CT.Significance.To the best of our knowledge, this work represents the first attempt to estimate patient-specific radiation doses from PET/CT for Chinese pediatric patients. TIACs derived from our methodology in both age groups exhibited significant differences from the that reported in ICRP 128. Substantial differences in absorbed and effective doses were observed between Chinese and Caucasian children across all age groups. These disparities are attributed to markedly distinct anatomical and pharmacokinetic characteristics among adults and pediatric patients, and different racial groups. The application of data derived from adults to pediatric patients introduces considerable uncertainty. Our methodology offers a valuable approach not only for estimating pharmacokinetic characteristics and patient-specific radiation doses in pediatric patients undergoing18F-FDG studies but also for other cohorts with similar characteristics.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

PURPOSE: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS: For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION: The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

Ultrasound image segmentation of renal tumors based on UNet++ with fusion of multiscale residuals and dual attention.

Objective. Laparoscopic renal unit-preserving resection is a routine and effective means of treating renal tumors. Image segmentation is an essential part before tumor resection. The current segmentation method mainly relies on doctors manual delineation, which is time-consuming, labor-intensive, and influenced by their personal experience and ability. And the image quality of segmentation is low, with problems such as blurred edges, unclear size and shape, which are not conducive to clinical diagnosis.Approach. To address these problems, we propose an automated segmentation method, i.e. the UNet++ algorithm fusing multiscale residuals and dual attention (MRDA_UNet++). It replaces two consecutive 3 × 3 convolutions in UNet++ with the 'MultiRes block' module, which incorporates coordinate attention to fuse features from different scales and suppress the impact of background noise. Furthermore, an attention gate is also added at the short connections to enhance the ability of the network to extract features from the target area.Main results. The experimental results show that MRDA_UNet++ achieves 93.18%, 92.87%, 93.66%, and 92.09% on the real-world dataset for MIoU, Dice, Precision, and Recall, respectively. Compared to the baseline model UNet++ on three public datasets, the MIoU, Dice, and Recall metrics improved by 6.00%, 7.90% and 18.09% respectively for BUSI, 0.39%, 0.27% and 1.03% for Dataset C, and 1.37%, 1.75% and 1.30% for DDTI.Significance. The proposed MRDA_UNet++ exhibits obvious advantages in feature extraction, which can not only significantly reduce the workload of doctors, but also further decrease the risk of misdiagnosis. It is of great value to assist doctors diagnosis in the clinic.

Guided block matching and 4-D transform domain filter projection denoising method for dynamic PET image reconstruction.

PURPOSE: Dynamic PET is an essential tool in oncology due to its ability to visualize and quantify radiotracer uptake, which has the potential to improve imaging quality. However, image noise caused by a low photon count in dynamic PET is more significant than in static PET. This study aims to develop a novel denoising method, namely the Guided Block Matching and 4-D Transform Domain Filter (GBM4D) projection, to enhance dynamic PET image reconstruction. METHODS: The sinogram was first transformed using the Anscombe method, then denoised using a combination of hard thresholding and Wiener filtering. Each denoising step involved guided block matching and grouping, collaborative filtering, and weighted averaging. The guided block matching was performed on accumulated PET sinograms to prevent mismatching due to low photon counts. The performance of the proposed denoising method (GBM4D) was compared to other methods such as wavelet, total variation, non-local means, and BM3D using computer simulations on the Shepp-Logan and digital brain phantoms. The denoising methods were also applied to real patient data for evaluation. RESULTS: In all phantom studies, GBM4D outperformed other denoising methods in all time frames based on the structural similarity and peak signal-to-noise ratio. Moreover, GBM4D yielded the lowest root mean square error in the time-activity curve of all tissues and produced the highest image quality when applied to real patient data. CONCLUSION: GBM4D demonstrates excellent denoising and edge-preserving capabilities, as validated through qualitative and quantitative assessments of both temporal and spatial denoising performance.

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors.

Objective: To investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis. Materials and methods: From 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC). Results: All seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively. Conclusion: This retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary.

Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging.

BACKGROUND: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired. PURPOSE: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients' anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development. METHODS: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks. The simulation of abdominal and pelvic helical CT examinations was performed on three validated commercial scanner systems to calculate organ-level fetal radiation dose. RESULTS: The absorbed radiation dose to the fetus ranged between 0.97 and 2.24 mGy, with an average of 1.63 ± 0.33 mGy. The CTDIvol -normalized fetal dose ranged between 0.56 and 1.30, with an average of 0.94 ± 0.25. The normalized fetal organ dose showed significant correlations with gestational age, maternal abdominal circumference (MAC), and fetal depth. The use of ATCM technique increased the fetal radiation dose in some patients. CONCLUSION: A technique enabling the calculation of organ-level radiation dose to the fetus was developed from models of actual anatomy representing a range of gestational age, maternal size, and fetal position. The developed maternal and fetal models provide a basis for reliable and accurate radiation dose estimation to fetal organs.

Development of a voxel S-value database for patient internal radiation dosimetry.

PURPOSE: Personalized dosimetry with high accuracy drew great attention in clinical practices. Voxel S-value (VSV) convolution has been proposed to speed up absorbed dose calculations. However, the VSV method is efficient for personalized internal radiation dosimetry only when there are pre-calculated VSVs of the radioisotope. In this work, we propose a new method for VSV calculation based on the developed mono-energetic particle VSV database of γ, ß, α, and X-ray for any radioisotopes. METHODS: Mono-energetic VSV database for γ, ß, α, and X-ray was calculated using Monte Carlo methods. Radiation dose was first calculated based on mono-energetic VSVs for [F-18]-FDG in 10 patients. The estimated doses were compared with the values obtained from direct Monte Carlo simulation for validation of the proposed method. The number of VSVs used in calculation was optimized based on the estimated dose accuracy and computation time. RESULTS: The generated VSVs showed a great consistency with the results calculated using direct Monte Carlo simulation. For [F-18]-FDG, the proposed VSV method with number of VSV of 9 shows the best relative average organ absorbed dose uncertainty of 3.25% while the calculation time was reduced by 99% and 97% compared to the Monte Carlo simulation and traditional multiple VSV methods, respectively. CONCLUSIONS: In this work, we provided a method to generate the VSV kernels for any radioisotope based on the pre-calculated mono-energetic VSV database and significantly reduced the time cost for the multiple VSVs dosimetry approach. A software was developed to generate VSV kernels for any radioisotope in 19 mediums.

Automatic organ completion with image stitching for personalized radiation dosimetry in CT examinations.

PURPOSE: Computed tomography (CT) image-based patient-specific voxel-based dosimetry has difficulties complementing missing tissues for organs located partially inside or completely outside the image volume. Previous studies constructed patient-specific whole-body models by rescaling reference phantoms or extending regional CT images with manually adjusted phantoms. This study proposes a methodology for automatic organ completion of regional CT images for CT dosimetry using a stitching approach. METHODS: Virtual clinical trials were performed by truncating whole-body CT images to generate virtual clinical chest and abdominopelvic CT images. Corresponding anchor images for each patient were selected according to sex and similarity of the axial length and water equivalent diameter of the virtual regional CT images. Automatic image stitching was performed by transformation initialization and iteration, while the stitched CT images and organ atlas were used in GPU-based Geant4 Monte Carlo simulations to generate a radiation dose map and absorbed organ dose. To evaluate the performance of the stitching model in radiation dosimetry, organ mass differences and Jaccard's coefficient of stitched and rescaled anchor images were calculated, and the radiation doses were compared among the corresponding values from the VirtualDose®, original whole-body CT, stitching model, regional CT, registration-based rescaling method, and WED-based rescaling method. RESULTS: The anatomical accuracy of stitched images was significantly improved. For organs partially inside the image volume, organ dose estimation from the stitching model could be more accurate than that reported in previous studies. The absolute differences in effective dose from the stitched images were 6.55% and 4.81% for chest and abdominopelvic CT scans, respectively. CONCLUSION: The proposed automatic stitching model partially complements organs inside or outside the CT scan range and provides more accurate anatomical representations for radiation dosimetry than traditional phantom rescaling methods.

Construction of a digital fetus library for radiation dosimetry.

PURPOSE: Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity 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. Nonuniform rational B-spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit 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. A series of anatomical parameters, including femur length, humerus length, biparietal diameter, abdominal circumference (FAC), and head circumference, 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 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.

Tracers progress for positron emission tomography imaging of glial-related disease.

Glial cells play an essential part in the neuron system. They can not only serve as structural blocks in the human brain but also participate in many biological processes. Extensive studies have shown that astrocytes and microglia play an important role in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, as well as glioma, epilepsy, ischemic stroke, and infections. Positron emission tomography is a functional imaging technique providing molecular-level information before anatomic changes are visible and has been widely used in many above-mentioned diseases. In this review, we focus on the positron emission tomography tracers used in pathologies related to glial cells, such as glioma, Alzheimer's disease, and neuroinflammation.

Multi-scale Monte Carlo simulations of gold nanoparticle-induced DNA damages for kilovoltage X-ray irradiation in a xenograft mouse model using TOPAS-nBio.

Background: Gold nanoparticles (AuNPs) are considered as promising agents to increase the radiosensitivity of tumor cells. However, the biological mechanisms of radiation enhancement effects of AuNPs are still not well understood. We present a multi-scale Monte Carlo simulation framework within TOPAS-nBio to investigate the increase of DNA damage due to the presence of AuNPs in mouse tumor models. Methods: A tumor was placed inside a voxel mouse model and irradiated with either 100 kVp or 200 kVp x-ray beams. Phase spaces were employed to transfer particles from the macroscopic (voxel) scale to the microscopic scale, which consists of a cell geometry including a detailed mouse DNA model. Radiosensitizing effects were calculated in the presence and absence of hybrid nanoparticles with a Fe2O3 core surrounded by a gold layer (AuFeNPs). To simulate DNA damage even for very small energy tracks, Geant4-DNA physics and chemistry models were used on microscopic scale. Results: An AuFeNP induced enhancement of both dose and DNA strand breaks has been established for different scenarios. Produced chemical radicals including hydroxyl molecules, which were assumed to be responsible for DNA damage through chemical reactions, were found to be significantly increased. We further observed a dependency of the results on the location of the cells within the tumor for 200 kVp x-ray beams. Conclusions: Our multi-scale approach allows to study irradiation induced physical and chemical effects on cells. We showed a potential increase in cell radiosensitization caused by relatively small concentrations of AuFeNPs. Our new methodology allows the individual adjustment of parameters in each simulation step and therefore can be used for other studies investigating the radiosensitizing effects of AuFeNPs or AuNPs in living cells.

Development of a nonhuman primate computational phantom for radiation dosimetry.

PURPOSE: The nonhuman primate (NHP) is an important animal model for evaluating the response of the human body to radiation exposure owing to similarities between its organ structure, genome, life span, and metabolism. However, there is a lack of radiation dosimetry estimations for NHPs. The aim of this work is to construct a computational phantom of NHPs and estimate absorbed fractions and specific absorbed fractions for internal radiation dosimetry. MATERIALS AND METHODS: A female rhesus monkey was frozen and sectioned using a cryomacrotome. The transaxial sectioned images were imported into Adobe Photoshop for segmentation of internal organs. A total of 31 organs/tissues were identified and labeled. The segmented voxel phantom was then converted to a boundary representation (BREP) phantom to enable easy alteration of the phantom to mimic monkeys of different stature. The BREP model was then voxelized and imported into the MCNPX Monte Carlo radiation transport code for electron and photon dosimetry calculations. To estimate the appropriateness of using human phantoms as surrogate models for NHPs, absorbed fractions (AFs) and specific absorbed fractions (SAFs) of monoenergetic electrons and photons were calculated and compared with the ICRP reference newborn female phantom and a 1-yr-old female phantom. RESULTS: Considerable differences were observed for both self-absorbed and cross-absorbed doses for some organs between the NHP phantom and newborn phantom. For example, the ratios of the self-absorbed SAF(stomach wall) Monkey to SAF(stomach wall) Newborn ranged from 0.06 at 10 keV to 0.29 at 3 MeV for photons while the corresponding ratios to cross-absorbed SAF(liver→kidney) Monkey to SAF(liver→kidney) Newborn ranged from 0.78 at 50 keV to 5.78 at 10 keV for photons. Conversely, values of self-absorbed SAF were much higher (ratios of 2.39-4.19) for the brain and much lower for the uterus (0.51-0.61) in the monkey model compared to the newborn phantom. These dose differences can be attributed to the disparities between organ masses, shapes, and positions between the two phantoms. CONCLUSIONS: The developed NHP model can be exploited for the assessment of radiation dose to NHPs in preclinical radiation dosimetry studies and radiation therapy research.

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

OBJECTIVES: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated construction of patient-specific computational phantoms based on actual patient CT images to enable accurate estimation of conceptus dose. METHODS: We developed a 3D deep convolutional network algorithm for automated segmentation of CT images to build realistic computational phantoms. The neural network architecture consists of analysis and synthesis paths with four resolution levels each, trained on manually labeled CT scans of six identified anatomical structures. Thirty-two CT exams were augmented to 128 datasets and randomly split into 80%/20% for training/testing. The absorbed doses for six segmented organs/tissues from abdominal CT scans were estimated using Monte Carlo calculations. The resulting radiation doses were then compared between the computational models generated using automated segmentation and manual segmentation, serving as reference. RESULTS: The Dice similarity coefficient for identified internal organs between manual segmentation and automated segmentation results varies from 0.92 to 0.98 while the mean Hausdorff distance for the uterus is 16.1 mm. The mean absorbed dose for the uterus is 2.9 mGy whereas the mean organ dose differences between manual and automated segmentation techniques are 0.07%, - 0.45%, - 1.55%, - 0.48%, - 0.12%, and 0.28% for the kidney, liver, lung, skeleton, uterus, and total body, respectively. CONCLUSION: The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. KEY POINTS: • The conceptus dose during diagnostic radiology and nuclear medicine imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. • The proposed methodology allows automated construction of realistic computational models that can be exploited to estimate patient-specific organ radiation doses from radiological imaging procedures. • The dosimetric results can be used for the risk-benefit analysis of radiation hazards to conceptus from diagnostic imaging procedures, thus guiding the decision-making process.

Effects of body habitus on internal radiation dose calculations using the 5-year-old anthropomorphic male models.

Computational phantoms are commonly used in internal radiation dosimetry to assess the amount and distribution pattern of energy deposited in various parts of the human body from different internal radiation sources. Radiation dose assessments are commonly performed on predetermined reference computational phantoms while the argument for individualized patient-specific radiation dosimetry exists. This study aims to evaluate the influence of body habitus on internal dosimetry and to quantify the uncertainties in dose estimation correlated with the use of fixed reference models. The 5-year-old IT'IS male phantom was modified to match target anthropometric parameters, including body weight, body height and sitting height/stature ratio (SSR), determined from reference databases, thus enabling the creation of 125 5-year-old habitus-dependent male phantoms with 10th, 25th, 50th, 75th and 90th percentile body morphometries. We evaluated the absorbed fractions and the mean absorbed dose to the target region per unit cumulative activity in the source region (S-values) of F-18 in 46 source regions for the generated 125 anthropomorphic 5-year-old hybrid male phantoms using the Monte Carlo N-Particle eXtended general purpose Monte Carlo transport code and calculated the absorbed dose and effective dose of five 18F-labelled radiotracers for children of various habitus. For most organs, the S-value of F-18 presents stronger statistical correlations with body weight, standing height and sitting height than BMI and SSR. The self-absorbed fraction and self-absorbed S-values of F-18 and the absorbed dose and effective dose of 18F-labelled radiotracers present with the strongest statistical correlations with body weight. For 18F-Amino acids, 18F-Brain receptor substances, 18F-FDG, 18F-L-DOPA and 18F-FBPA, the mean absolute effective dose differences between phantoms of different habitus and fixed reference models are 11.4%, 11.3%, 10.8%, 13.3% and 11.4%, respectively. Total body weight, standing height and sitting height have considerable effects on human internal dosimetry. Radiation dose calculations for individual subjects using the most closely matched habitus-dependent computational phantom should be considered as an alternative to improve the accuracy of the estimates.

Computational hybrid anthropometric paediatric phantom library for internal radiation dosimetry.

Hybrid computational phantoms combine voxel-based and simplified equation-based modelling approaches to provide unique advantages and more realism for the construction of anthropomorphic models. In this work, a methodology and C++ code are developed to generate hybrid computational phantoms covering statistical distributions of body morphometry in the paediatric population. The paediatric phantoms of the Virtual Population Series (IT'IS Foundation, Switzerland) were modified to match target anthropometric parameters, including body mass, body length, standing height and sitting height/stature ratio, determined from reference databases of the National Centre for Health Statistics and the National Health and Nutrition Examination Survey. The phantoms were selected as representative anchor phantoms for the newborn, 1, 2, 5, 10 and 15 years-old children, and were subsequently remodelled to create 1100 female and male phantoms with 10th, 25th, 50th, 75th and 90th body morphometries. Evaluation was performed qualitatively using 3D visualization and quantitatively by analysing internal organ masses. Overall, the newly generated phantoms appear very reasonable and representative of the main characteristics of the paediatric population at various ages and for different genders, body sizes and sitting stature ratios. The mass of internal organs increases with height and body mass. The comparison of organ masses of the heart, kidney, liver, lung and spleen with published autopsy and ICRP reference data for children demonstrated that they follow the same trend when correlated with age. The constructed hybrid computational phantom library opens up the prospect of comprehensive radiation dosimetry calculations and risk assessment for the paediatric population of different age groups and diverse anthropometric parameters.

Development of computational pregnant female and fetus models and assessment of radiation dose from positron-emitting tracers.

PURPOSE: Molecular imaging using PET and hybrid (PET/CT and PET/MR) modalities nowadays plays a pivotal role in the clinical setting for diagnosis and staging, treatment response monitoring, and radiation therapy treatment planning of a wide range of oncologic malignancies. The developing embryo/fetus presents a high sensitivity to ionizing radiation. Therefore, estimation of the radiation dose delivered to the embryo/fetus and pregnant patients from PET examinations to assess potential radiation risks is highly praised. METHODS: We constructed eight embryo/fetus models at various gestation periods with 25 identified tissues according to reference data recommended by the ICRP publication 89 representing the anatomy of the developing embryo/fetus. The developed embryo/fetus models were integrated into realistic anthropomorphic computational phantoms of the pregnant female and used for estimating, using Monte Carlo calculations, S-values of common positron-emitting radionuclides, organ absorbed dose, and effective dose of a number of positron-emitting labeled radiotracers. RESULTS: The absorbed dose is nonuniformly distributed in the fetus. The absorbed dose of the kidney and liver of the 8-week-old fetus are about 47.45 % and 44.76 % higher than the average absorbed dose of the fetal total body for all investigated radiotracers. For 18F-FDG, the fetal effective doses are 2.90E-02, 3.09E-02, 1.79E-02, 1.59E-02, 1.47E-02, 1.40E-02, 1.37E-02, and 1.27E-02 mSv/MBq at the 8th, 10th, 15th, 20th, 25th, 30th, 35th, and 38th weeks of gestation, respectively. CONCLUSION: The developed pregnant female/fetus models matching the ICRP reference data can be exploited by dedicated software packages for internal and external dose calculations. The generated S-values will be useful to produce new standardized dose estimates to pregnant patients and embryo/fetus from a variety of positron-emitting labeled radiotracers.

Development of computational small animal models and their applications in preclinical imaging and therapy research.

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.

Assessment of radiation dose in nuclear cardiovascular imaging using realistic computational models.

PURPOSE: Nuclear cardiology plays an important role in clinical assessment and has enormous impact on the management of a variety of cardiovascular diseases. Pediatric patients at different age groups are exposed to a spectrum of radiation dose levels and associated cancer risks different from those of adults in diagnostic nuclear medicine procedures. Therefore, comprehensive radiation dosimetry evaluations for commonly used myocardial perfusion imaging (MPI) and viability radiotracers in target population (children and adults) at different age groups are highly desired. METHODS: Using Monte Carlo calculations and biological effects of ionizing radiation VII model, we calculate the S-values for a number of radionuclides (Tl-201, Tc-99m, I-123, C-11, N-13, O-15, F-18, and Rb-82) and estimate the absorbed dose and effective dose for 12 MPI radiotracers in computational models including the newborn, 1-, 5-, 10-, 15-yr-old, and adult male and female computational phantoms. RESULTS: For most organs, (201)Tl produces the highest absorbed dose whereas (82)Rb and (15)O-water produce the lowest absorbed dose. For the newborn baby and adult patient, the effective dose of (82)Rb is 48% and 77% lower than that of (99m)Tc-tetrofosmin (rest), respectively. CONCLUSIONS: (82)Rb results in lower effective dose in adults compared to (99m)Tc-labeled tracers. However, this advantage is less apparent in children. The produced dosimetric databases for various radiotracers used in cardiovascular imaging, using new generation of computational models, can be used for risk-benefit assessment of a spectrum of patient population in clinical nuclear cardiology practice.

Effect of respiratory motion on internal radiation dosimetry.

PURPOSE: Estimation of the radiation dose to internal organs is essential for the assessment of radiation risks and benefits to patients undergoing diagnostic and therapeutic nuclear medicine procedures including PET. Respiratory motion induces notable internal organ displacement, which influences the absorbed dose for external exposure to radiation. However, to their knowledge, the effect of respiratory motion on internal radiation dosimetry has never been reported before. METHODS: Thirteen computational models representing the adult male at different respiratory phases corresponding to the normal respiratory cycle were generated from the 4D dynamic XCAT phantom. Monte Carlo calculations were performed using the mcnp transport code to estimate the specific absorbed fractions (SAFs) of monoenergetic photons/electrons, the S-values of common positron-emitting radionuclides (C-11, N-13, O-15, F-18, Cu-64, Ga-68, Rb-82, Y-86, and I-124), and the absorbed dose of (18)F-fluorodeoxyglucose ((18)F-FDG) in 28 target regions for both the static (average of dynamic frames) and dynamic phantoms. RESULTS: The self-absorbed dose for most organs/tissues is only slightly influenced by respiratory motion. However, for the lung, the self-absorbed SAF is about 11.5% higher at the peak exhale phase than the peak inhale phase for photon energies above 50 keV. The cross-absorbed dose is obviously affected by respiratory motion for many combinations of source-target pairs. The cross-absorbed S-values for the heart contents irradiating the lung are about 7.5% higher in the peak exhale phase than the peak inhale phase for different positron-emitting radionuclides. For (18)F-FDG, organ absorbed doses are less influenced by respiratory motion. CONCLUSIONS: Respiration-induced volume variations of the lungs and the repositioning of internal organs affect the self-absorbed dose of the lungs and cross-absorbed dose between organs in internal radiation dosimetry. The dynamic anatomical model provides more accurate internal radiation dosimetry estimates for the lungs and abdominal organs based on realistic modeling of respiratory motion. This work also contributes to a better understanding of model-induced uncertainties in internal radiation dosimetry.

Fetal and maternal absorbed dose estimates for positron-emitting molecular imaging probes.

UNLABELLED: PET and hybrid (PET/CT and PET/MR) imaging currently play a pivotal role in clinical diagnosis, staging and restaging, treatment, and surveillance of several diseases. As such, limiting the radiation exposure of special patients, such as pregnant women, from PET procedures is an important challenge that needs to be appropriately addressed because of the high sensitivity of the developing embryo/fetus to ionizing radiation. Therefore, accurate radiation dose calculation for the embryo/fetus and pregnant patient from common positron-emitting radiotracers is highly desired. METHODS: To obtain representative estimates of radiation dose to the human body, realistic biologic and physical models should be used. In this work, we evaluate the S values of 9 positron-emitting radionuclides ((11)C, (13)N, (15)O, (18)F, (64)Cu, (68)Ga, (82)Rb, (86)Y, and (124)I) and the absorbed and effective doses for 21 positron-emitting labeled radiotracers using realistic anthropomorphic computational phantoms of early pregnancy and at 3-, 6-, and 9-mo of gestation and the most recent biokinetic data available. The Monte Carlo N-Particle eXtended general-purpose Monte Carlo code was used for radiation transport simulation. RESULTS: The absorbed dose to the pregnant model is less influenced by the gestation for most organs or tissues, but the anatomic changes of the maternal body increases the effective dose for some radiotracers. For (18)F-FDG, the estimated absorbed doses to the embryo/fetus are 3.05E-02, 2.27E-02, 1.50E-02, and 1.33E-02 mGy/MBq at early pregnancy and 3-, 6-, and 9-mo gestation, respectively. The absorbed dose is nonuniformly distributed in the fetus and would be 1.03-2 times higher in the fetal brain than in other fetal soft tissues. CONCLUSION: The generated S values can be exploited to estimate the radiation dose delivered to pregnant patients and the embryo/fetus from various PET radiotracers used in clinical and research settings. The generated dosimetric database of radiotracers using new-generation computational models can be used for the assessment of radiation risks to pregnant women and the embryo/fetus undergoing PET/CT imaging procedures. This work also contributes to a better understanding of the absorbed dose distribution in the fetus.