ESTIMATION OF FETAL AND PEDIATRIC DOSES FROM CHEST CT EXAMINATIONS USING VIRTUALDOSE SOFTWARE.

Pregnant women and children sometimes had to undergo chest computed tomography (CT) scans during the Corona Virus Disease 2019 (COVID-19) pandemic. This study estimated the fetal and pediatric doses from chest CT scans. Organ doses and effective doses were calculated using the VirtualDose-CT software. Two groups of computational human phantoms, pregnant females and pediatric patients were used in this study. The results of doses normalized to volumetric CT Dose Index (CTDIvol) can be used universally for other dosimetry studies. Based on our calculations and international survey data of CTDIvol, fetal absorbed doses from COVID-19-related chest CT were found to be 0.04-0.36, 0.05-0.44 and 0.07-0.61 mGy for 3, 6 and 9 months of pregnancy, respectively. When the scan range is extended to the abdominal region, fetal doses increase by almost 4-fold. Effective doses for COVID-19-related chest CT were 1.62-13.77, 1.58-13.46, 1.57-13.33 and 1.29-10.98 mSv for the newborn, 1-, 5- and 10-y-old children, respectively. In addition, the effects of specific axial scan ranges exceeding the thorax region were evaluated. Although doses from chest CT scans are small, such data allow radiologists and patients to be informed of the dose levels and ways to avoid unnecessary radiation.

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure.

Objective.This paper describes the development and validation of a GPU-accelerated Monte Carlo (MC) dose computing module dedicated to organ dose calculations of individual patients undergoing nuclear medicine (NM) internal radiation exposures involving PET/CT examination.Approach. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features. To validate the code, we compared dose distributions from the point ion source, including18F,11C,15O, and68Ga, calculated for a water phantom against a well-tested MC code, GATE. To demonstrate the clinical utility and advantage of ARCHER-NM, one set of18F-FDG PET/CT data for an adult male NM patient is calculated using the new code. Radiosensitive organs in the CT dataset are segmented using a CNN-based tool called DeepViewer. The PET image intensity maps are converted to radioactivity distributions to allow for MC radiation transport dose calculations at the voxel level. The dose rate maps and corresponding statistical uncertainties were calculated at the acquisition time of PET image.Main results.The water-phantom results show excellent agreement, suggesting that the radiation physics module in the new NM code is adequate. The dose rate results of the18F-FDG PET imaging patient show that ARCHER-NM's results agree very well with those of the GATE within -2.45% to 2.58% (for a total of 28 organs considered in this study). Most impressively, ARCHER-NM obtains such results in 22 s while it takes GATE about 180 min for the same number of 5 × 108simulated decay events.Significance.This is the first study presenting GPU-accelerated patient-specific MC internal radiation dose rate calculations for clinically realistic18F-FDG PET/CT imaging case involving autosegmentation of whole-body PET/CT images. This study suggests that the proposed computing tools-ARCHER-NM- are accurate and fast enough for routine internal dosimetry in NM clinics.

Clinical application and improvement of a CNN-based autosegmentation model for clinical target volumes in cervical cancer radiotherapy.

OBJECTIVE: Clinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN-based autosegmentation of CTV contours in cervical cancer. METHODS: This study included 400 cervical cancer treatment planning cases with CTV delineated by radiation oncologists from three hospitals. The datasets were divided into five subdatasets (80 cases each). The cases in datasets 1, 2, and 3 were delineated by physicians A, B, and C, respectively. The cases in datasets 4 and 5 were delineated by multiple physicians. Dataset 1 was divided into training (50 cases), validation (10 cases), and testing (20 cases) cohorts, and they were used to construct the pretrained model. Datasets 2-5 were regarded as host datasets to evaluate the accuracy of the pretrained model. In the adaptive process, the pretrained model was fine-tuned to measure improvements by gradually adding more training cases selected from the host datasets. The accuracy of the autosegmentation model on each host dataset was evaluated using the corresponding test cases. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD_95) were used to evaluate the accuracy. RESULTS: Before and after adaptive improvements, the average DSC values on the host datasets were 0.818 versus 0.882, 0.763 versus 0.810, 0.727 versus 0.772, and 0.679 versus 0.789, which are improvements of 7.82%, 6.16%, 6.19%, and 16.05%, respectively. The average HD_95 values were 11.143 mm versus 6.853 mm, 22.402 mm versus 14.076 mm, 28.145 mm versus 16.437 mm, and 33.034 mm versus 16.441 mm, which are improvements of 37.94%, 37.17%, 41.60%, and 50.23%, respectively. CONCLUSION: The proposed method improved the adaptability of the CNN-based autosegmentation model when applied to host datasets.

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-ß levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

PURPOSE: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-ß levels thus increasing the workflow in centers involving Alzheimer's Disease (AD) patients. METHODS: PET datasets were collected for 25 patients injected with 18F-AV45 radiopharmaceutical. To generate necessary training data, PET images from both normal-scanning-time (20-min) as well as so-called "shortened-scanning-time" (1-min, 2-min, 5-min, and 10-min) were reconstructed for each patient. Building on our earlier work on MCDNet (Monte Carlo Denoising Net) and a new Wasserstein-GAN algorithm, we developed a new denoising model called MCDNet-2 to predict normal-scanning-time PET images from a series of shortened-scanning-time PET images. The quality of the predicted PET images was quantitatively evaluated using objective metrics including normalized-root-mean-square-error (NRMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR). Furthermore, two radiologists performed subjective evaluations including the qualitative evaluation and a five-point grading evaluation. The denoising performance of the proposed MCDNet-2 was finally compared with those of U-Net, MCDNet, and a traditional denoising method called Gaussian Filtering. RESULTS: The proposed MCDNet-2 can yield good denoising performance in 5-min PET images. In the comparison of denoising methods, MCDNet-2 yielded the best performance in the subjective evaluation although it is comparable with MCDNet in objective comparison (NRMSE, PSNR, and SSIM). In the qualitative evaluation of amyloid-ß positive or negative results, MCDNet-2 was found to achieve a classification accuracy of 100%. CONCLUSIONS: The proposed denoising method has been found to reduce the PET scan time from the normal level of 20 min to 5 min but still maintaining acceptable image quality in correctly diagnosing amyloid-ß levels. These results suggest strongly that deep learning-based methods such as ours can be an attractive solution to the clinical needs to improve PET imaging workflow.

Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

OBJECTIVE: To evaluate the accuracy of a deep learning-based auto-segmentation mode to that of manual contouring by one medical resident, where both entities tried to mimic the delineation "habits" of the same clinical senior physician. METHODS: This study included 125 cervical cancer patients whose clinical target volumes (CTVs) and organs at risk (OARs) were delineated by the same senior physician. Of these 125 cases, 100 were used for model training and the remaining 25 for model testing. In addition, the medical resident instructed by the senior physician for approximately 8 months delineated the CTVs and OARs for the testing cases. The dice similarity coefficient (DSC) and the Hausdorff Distance (HD) were used to evaluate the delineation accuracy for CTV, bladder, rectum, small intestine, femoral-head-left, and femoral-head-right. RESULTS: The DSC values of the auto-segmentation model and manual contouring by the resident were, respectively, 0.86 and 0.83 for the CTV (P < 0.05), 0.91 and 0.91 for the bladder (P > 0.05), 0.88 and 0.84 for the femoral-head-right (P < 0.05), 0.88 and 0.84 for the femoral-head-left (P < 0.05), 0.86 and 0.81 for the small intestine (P < 0.05), and 0.81 and 0.84 for the rectum (P > 0.05). The HD (mm) values were, respectively, 14.84 and 18.37 for the CTV (P < 0.05), 7.82 and 7.63 for the bladder (P > 0.05), 6.18 and 6.75 for the femoral-head-right (P > 0.05), 6.17 and 6.31 for the femoral-head-left (P > 0.05), 22.21 and 26.70 for the small intestine (P > 0.05), and 7.04 and 6.13 for the rectum (P > 0.05). The auto-segmentation model took approximately 2 min to delineate the CTV and OARs while the resident took approximately 90 min to complete the same task. CONCLUSION: The auto-segmentation model was as accurate as the medical resident but with much better efficiency in this study. Furthermore, the auto-segmentation approach offers additional perceivable advantages of being consistent and ever improving when compared with manual approaches.

End-to-end unsupervised cycle-consistent fully convolutional network for 3D pelvic CT-MR deformable registration.

OBJECTIVE: To improve the efficiency of computed tomography (CT)-magnetic resonance (MR) deformable image registration while ensuring the registration accuracy. METHODS: Two fully convolutional networks (FCNs) for generating spatial deformable grids were proposed using the Cycle-Consistent method to ensure the deformed image consistency with the reference image data. In all, 74 pelvic cases consisting of both MR and CT images were studied, among which 64 cases were used as training data and 10 cases as the testing data. All training data were standardized and normalized, following simple image preparation to remove the redundant air. Dice coefficients and average surface distance (ASD) were calculated for regions of interest (ROI) of CT-MR image pairs, before and after the registration. The performance of the proposed method (FCN with Cycle-Consistent) was compared with that of Elastix software, MIM software, and FCN without cycle-consistent. RESULTS: The results show that the proposed method achieved the best performance among the four registration methods tested in terms of registration accuracy and the method was more stable than others in general. In terms of average registration time, Elastix took 64 s, MIM software took 28 s, and the proposed method was found to be significantly faster, taking <0.1 s. CONCLUSION: The proposed method not only ensures the accuracy of deformable image registration but also greatly reduces the time required for image registration and improves the efficiency of the registration process. In addition, compared with other deep learning methods, the proposed method is completely unsupervised and end-to-end.

CONCEPTUAL DESIGN AND PRELIMINARY RESULTS OF A VR-BASED RADIATION SAFETY TRAINING SYSTEM FOR INTERVENTIONAL RADIOLOGISTS.

To help minimise occupational radiation exposure in interventional radiology, we conceptualised a virtual reality-based radiation safety training system to help operators understand complex radiation fields and to avoid high radiation areas through game-like interactive simulations. The preliminary development of the system has yielded results suggesting that the training system can calculate and report the radiation exposure after each training session based on a database precalculated from computational phantoms and Monte Carlo simulations and the position information provided by the Microsoft HoloLens headset. In addition, real-time dose rate and cumulative dose will be displayed to the trainee to help them adjust their practice. This paper presents the conceptual design of the overall hardware and software design, as well as preliminary results to combine HoloLens headset and complex 3D X-ray field spatial distribution data to create a mixed reality environment for safety training purpose in interventional radiology.

A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.

PURPOSE: To develop and test a three-dimensional (3D) deep learning model for predicting 3D voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT). METHODS: A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V50 , V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. The model was also compared with 3D U-Net and the same architecture model without beam configurations input (named as 3D U-Res-Net_O). RESULTS: The 3D U-Res-Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from -1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U-Res-Net_O and being slightly superior to 3D U-Net. CONCLUSIONS: This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation. METHODS: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross-validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population-average patient phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. RESULTS: The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population-averaged phantoms, the new patient-specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU-accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database. CONCLUSION: This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.

Patient-Specific Organ and Effective Dose Estimates in Adult Oncologic CT.

OBJECTIVE. Patient-specific organ and effective dose provides essential information for CT protocol optimization. However, such information is not readily available in the scan records. The purpose of this study was to develop a method to obtain accurate examination- and patient-specific organ and effective dose estimates by use of available scan data and patient body size information for a large cohort of patients. MATERIALS AND METHODS. The data were randomly collected for 1200 patients who underwent CT in a 2-year period. Physical characteristics of the patients and CT technique were processed as inputs for the dose estimator. Organ and effective doses were estimated by use of the inputs and computational human phantoms matched to patients on the basis of sex and effective diameter. Size-based ratios were applied to correct for patient-phantom body size differences. RESULTS. Patients received a mean of 59.9 mGy to the lens of the eye per brain scan, 10.1 mGy to the thyroid per chest scan, 17.5 mGy to the liver per abdomen and pelvis scan, and 19.0 mGy to the liver per body scan. A factor of 2 difference in dose estimates was observed between patients of various habitus. CONCLUSION. Examination- and patient-specific organ and effective doses were estimated for 1200 adult oncology patients undergoing CT. The dose conversion factors calculated facilitate rapid organ and effective dose estimation in clinics. Compared with nonspecific dose estimation methods, patient dose estimations with data specific to the patient and examination can differ by a factor of 2.

Monte carlo study of organ doses and related risk for cancer in Tanzania from scattered photons in cervical radiation treatment involving Co-60 source.

PURPOSE: The present work aimed to evaluate organ doses and related risk for cancer from external beam radiation treatment (EBRT) and high-dose-rate (HDR) brachytherapy (BT) involving Co-60 source for patients with cervical carcinoma in Tanzania based on Monte Carlo methods and to evaluate the secondary cancer risks in their lifetime. METHODS: EBRT and HDR-BR were modelled by using the MCNPX Monte Carlo (MC) code. The MC simulations were performed by using validated models and isocentric irradiation of an adult female computational phantom. The organ doses and cancer risks estimates were obtained. RESULTS: The highest absorbed doses of 6.98 × 10-2 and 5.74 × 10-2 Sv/Gy were recorded in the bladder for BT and EBRT. The higher risk was found for colon at 1.06 × 10-3 in the HDR-BT and 9.75 × 10-5 in the EBRT per 100,000 population at exposure age of 35 years than in the other organs. The risk magnitude decreased with increasing age at exposure. In general, the secondary cancer risks in all sites considered from EBRT and HDR-BR for cervical cancer patient were lower than the baseline risks. CONCLUSIONS: The chances of developing secondary cancer take years following radiation therapy are extremely low, but the results of present study can support to establish a future database on secondary cancer risks involving radiation therapy in patients with cervical cancer by using HDR-BR and EBRT with Co-60 source in Tanzania and other developing countries.

Influences of operator head posture and protective eyewear on eye lens doses in interventional radiology: A Monte Carlo Study.

PURPOSE: To quantify the effects of operator head posture and different types of protective eyewear on the eye lens dose to operators in interventional radiology (IR). METHODS: A deformable computational human phantom, Rensselaer Polytechnic Institute (RPI) Adult Male, consisting of a high-resolution eye model, was used to simulate a radiologist who is performing an interventional radiology procedure. The radiologist phantom was deformed to a set of different head postures. Three different protective eyewear models were incorporated into the posture-deformed radiologist phantom. The eye lens dose of the radiologist was calculated using the Monte Carlo code, MCNP. Effects of the radiologist's head posture and different types of protective eyewear on eye lens doses were studied. The relationship between efficacy of protective eyewear and the radiologist's head posture was investigated. Effects of other parameters on efficacy of protective eyewear were also studied, including the angular position of the radiologist, the gap between the eyewear and the face of the radiologist, and the lead equivalent thickness. RESULTS: The dose to both lenses decreased by 80% as the head posture moved from looking downward to looking upward. Sports wrap glasses were found to reduce doses further than the other two studied models. The efficacy of eyewear was found to be related to radiologist's head posture as well. When the radiologist was looking up, the protective eyewear almost provided no protection to both lenses. Other factors such as the face-to-eyewear distance and the lead equivalent thickness were also found to have an impact on the efficacy of protective eyewear. The dose reduction factor (DRF), defined as the ratio of the dose to the lens without protection to that with protection, decreased from 4.25 to 1.07 as the face-to-eyewear distance increased. The DRF almost doubled when the lead equivalent thickness increased from 0.07 to 0.35 mm. However, further increase in lead equivalent thickness showed little improvement in dose reduction. CONCLUSION: The radiologist's head posture has a significant influence on the eye lens dose in IR. Sports wrap protective eyewear which conforms to the curve of the face is essential for the radiation protection of the eye lens. However, the radiologist's head posture and other exposure parameters should be considered when evaluating the protection of the radiologist's eyes.

VirtualDose-IR: a cloud-based software for reporting organ doses in interventional radiology.

A cloud-based software, VirtualDose-IR (Virtual Phantoms Inc., Albany, New York, USA), designed to report organ doses and effective doses for a diverse patient population from interventional radiology (IR) procedures has been developed and tested. This software is based on a comprehensive database of Monte Carlo-generated organ dose built with a set of 21 anatomically realistic patient phantoms. The patient types included in this database are both male and female people with different ages reflecting reference adults, obese people with different BMIs and pregnant women at different gestational stages. Selectable parameters such as patient type, tube voltage, filtration thickness, beam direction, field size, and irradiation site are also considered in VirtualDose-IR. The software has been implemented using the 'Software as a Service (SaaS)' delivery concept permitting simultaneous multi-user, multi-platform access without requiring local installation. The patient doses resulting from different target sites and patient populations were reported using the VirtualDose-IR system. The patient doses under different source to surface distances (SSD) and beam angles calculated by VirtualDose-IR and Monte Carlo simulations were compared. For most organs, the dose differences between VirtualDose-IR results and Monte Carlo results were less than 0.3 mGy at 15 000 mGy * cm2 kerma-area product (KAP). The organ dose results were compared with measurement data previously reported in literatures. The doses to organs that were located within the irradiation field match closely with experimental measurement data. The differences in the effective dose values between calculated using VirtualDose-IR and those measured were less than 2.5%. The dose errors of most organs between VirtualDose-IR and literature results were less than 40%. These results validate the accuracy of organ doses reported by VirtualDose-IR. With the inclusion of pre-specified clinical IR examination parameters (such as beam direction, target location, field of view and beam quality) and the latest anatomically realistic patient phantoms in Monte Carlo simulations, VirtualDose-IR provides users with accurate dose information in order to systematically compare, evaluate, and optimize IR plans.

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

Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semi-automated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. The article is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.

A single detector energy-resolved proton radiography system: a proof of principle study by Monte Carlo simulations.

Taking advantage of Bragg peak and small spot size, pencil beam scanning proton therapy can deliver a highly conformal dose distribution to target while sparing normal tissues. However, such dose distributions can be highly sensitive to the proton range uncertainty which can reach 5% or higher in lung tissue. One proposed method for reducing range uncertainty is to measure the water equivalent path length (WEPL) by proton radiography. In this study, we followed a newly proposed proton beam radiography technique based on energy resolved dose functions (ERDF) to construct a Monte Carlo model for a single detector energy-resolved proton radiography system (SDPRS). This SDPRS model was constructed in the Monte Carlo software package TOPAS (TOol for PArticle Simulation) and it includes the Mevion HYPERSCAN™ pencil beam scanning treatment head and a 2D dose detector positioned downstream as the imager. A calibration phantom containing a number of tissue equivalent materials was simulated to evaluate the accuracy in WEPL measurement by SDPRS. The mean deviation of the obtained relative stopping power (RSP) from the reference values was 0.31%. Proton radiographs of an anthropomorphic head phantom were also generated to demonstrate the clinical relevance of the technique. Effects of different energy layer spacing and measurement noise were also studied.

The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.

Deformable image registration (DIR) is the key process for contour propagation and dose accumulation in adaptive radiation therapy (ART). However, currently, ART suffers from a lack of understanding of "robustness" of the process involving the image contour based on DIR and subsequent dose variations caused by algorithm itself and the presetting parameters. The purpose of this research is to evaluate the DIR caused variations for contour propagation and dose accumulation during ART using the RayStation treatment planning system. Ten head and neck cancer patients were selected for retrospective studies. Contours were performed by a single radiation oncologist and new treatment plans were generated on the weekly CT scans for all patients. For each DIR process, four deformation vector fields (DVFs) were generated to propagate contours and accumulate weekly dose by the following algorithms: (a) ANACONDA with simple presetting parameters, (b) ANACONDA with detailed presetting parameters, (c) MORFEUS with simple presetting parameters, and (d) MORFEUS with detailed presetting parameters. The geometric evaluation considered DICE coefficient and Hausdorff distance. The dosimetric evaluation included D95 , Dmax , Dmean , Dmin , and Homogeneity Index. For geometric evaluation, the DICE coefficient variations of the GTV were found to be 0.78 ± 0.11, 0.96 ± 0.02, 0.64 ± 0.15, and 0.91 ± 0.03 for simple ANACONDA, detailed ANACONDA, simple MORFEUS, and detailed MORFEUS, respectively. For dosimetric evaluation, the corresponding Homogeneity Index variations were found to be 0.137 ± 0.115, 0.006 ± 0.032, 0.197 ± 0.096, and 0.006 ± 0.033, respectively. The coherent geometric and dosimetric variations also consisted in large organs and small organs. Overall, the results demonstrated that the contour propagation and dose accumulation in clinical ART were influenced by the DIR algorithm, and to a greater extent by the presetting parameters. A quality assurance procedure should be established for the proper use of a commercial DIR for adaptive radiation therapy.

Image guidance doses delivered during radiotherapy: Quantification, management, and reduction: Report of the AAPM Therapy Physics Committee Task Group 180.

BACKGROUND: With radiotherapy having entered the era of image guidance, or image-guided radiation therapy (IGRT), imaging procedures are routinely performed for patient positioning and target localization. The imaging dose delivered may result in excessive dose to sensitive organs and potentially increase the chance of secondary cancers and, therefore, needs to be managed. AIMS: This task group was charged with: a) providing an overview on imaging dose, including megavoltage electronic portal imaging (MV EPI), kilovoltage digital radiography (kV DR), Tomotherapy MV-CT, megavoltage cone-beam CT (MV-CBCT) and kilovoltage cone-beam CT (kV-CBCT), and b) providing general guidelines for commissioning dose calculation methods and managing imaging dose to patients. MATERIALS & METHODS: We briefly review the dose to radiotherapy (RT) patients resulting from different image guidance procedures and list typical organ doses resulting from MV and kV image acquisition procedures. RESULTS: We provide recommendations for managing the imaging dose, including different methods for its calculation, and techniques for reducing it. The recommended threshold beyond which imaging dose should be considered in the treatment planning process is 5% of the therapeutic target dose. DISCUSSION: Although the imaging dose resulting from current kV acquisition procedures is generally below this threshold, the ALARA principle should always be applied in practice. Medical physicists should make radiation oncologists aware of the imaging doses delivered to patients under their care. CONCLUSION: Balancing ALARA with the requirement for effective target localization requires that imaging dose be managed based on the consideration of weighing risks and benefits to the patient.

Patient-specific organ and effective dose estimates in pediatric oncology computed tomography.

PURPOSE: Estimate organ and effective doses from computed tomography scans of pediatric oncologic patients using patient-specific information. MATERIALS AND METHODS: With IRB approval patient-specific scan parameters and patient size obtained from DICOM images and vendor-provided dose monitoring application were obtained for a cross-sectional study of 1250 pediatric patients from 0 through 20 y-olds who underwent head, chest, abdomen-pelvis, or chest-abdomen-pelvis CT scans. Patients were categorized by age. Organ doses and effective doses were estimated using VirtualDose™ CT based on patient-specific information, tube current modulation (TCM), and age-specific realistic phantoms. CTDIvol, DLP, and dose results were compared with those reported in the literature. RESULTS: CTDIvol and DLP varied widely as patient size varied. The 75th percentiles of CTDIvol and DLP were no greater than in the literature with the exception of head scans of 16-20 y-olds and of abdomen-pelvis scans of larger patients. Eye lens dose from a head scan was up to 69 mGy. Mean organ doses agreed with other studies at maximal difference of 38% for chest and 41% for abdomen-pelvis scans. Mean effective dose was generally higher for older patients. The highest effective doses were estimated for the 16-20 y-olds as: head 3.3 mSv, chest 4.1 mSv, abdomen-pelvis 10.0 mSv, chest-abdomen-pelvis 14.0 mSv. CONCLUSION: Patient-specific organ and effective doses have been estimated for pediatric oncologic patients from <1 through 20 y-olds. The effect of TCM was successfully accounted for in the estimates. Output parameters varied with patient size. CTDIvol and DLP results are useful for future protocol optimization.

DEVELOPMENT OF A SET OF MESH-BASED AND AGE-DEPENDENT CHINESE PHANTOMS AND APPLICATION FOR CT DOSE CALCULATIONS.

Phantoms for organ dose calculations are essential in radiation protection dosimetry. This article describes the development of a set of mesh-based and age-dependent phantoms for Chinese populations using reference data recommended by the Chinese government and by the International Atomic Energy Agency (IAEA). Existing mesh-based RPI adult male (RPI-AM) and RPI adult female (RPI-AF) phantoms were deformed to form new phantoms according to anatomical data for the height and weight of Chinese individuals of 5 years old male, 5 years old female, 10 years old male, 10 years old female,15 years old male, 15 years old female, adult male and adult female-named USTC-5 M, USTC-5F, USTC-10M, USTC-10F, USTC-15M, USTC-15F, USTC-AM and USTC-AF, respectively. Following procedures to ensure the accuracy, more than 120 organs/tissues in each model were adjusted to match the Chinese reference parameters and the mass errors were within 0.5%. To demonstrate the usefulness, these new set of phantoms were combined with a fully validated model of the GE LightSpeed Pro 16 multi-detector computed tomography (MDCT) scanner and the GPU-based ARCHER Monte Carlo code to compute organ doses from CT examinations. Organ doses for adult models were then compared with the data of RPI-AM and RPI-AF under the same conditions. The absorbed doses and the effective doses of RPI phantoms are found to be lower than these of the USTC adult phantoms whose body sizes are smaller. Comparisons for the doses among different ages and genders were also made. It was found that teenagers receive more radiation doses than adults do. Such Chinese-specific phantoms are clearly better suited in organ dose studies for the Chinese individuals than phantoms designed for western populations. As already demonstrated, data derived from age-specific Chinese phantoms can help CT operators and designers to optimize image quality and doses.