Computational hybrid anthropometric paediatric phantom library for internal radiation dosimetry

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.

Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Monte Carlo-based patient internal dosimetry in fluoroscopy-guided interventional procedures: A review

PURPOSE

This systematic review aims to understand the dose estimation approaches and their major challenges. Specifically, we focused on state-of-the-art Monte Carlo (MC) methods in fluoroscopy-guided interventional procedures.

METHODS

All relevant studies were identified through keyword searches in electronic databases from inception until September 2020. The searched publications were reviewed, categorised and analysed based on their respective methodology.

RESULTS

Hundred and one publications were identified which utilised existing MC-based applications/programs or customised MC simulations. Two outstanding challenges were identified that contribute to uncertainties in the virtual simulation reconstruction. The first challenge involves the use of anatomical models to represent individuals. Currently, phantom libraries best balance the needs of clinical practicality with those of specificity. However, mismatches of anatomical variations including body size and organ shape can create significant discrepancies in dose estimations. The second challenge is that the exact positioning of the patient relative to the beam is generally unknown. Most dose prediction models assume the patient is located centrally on the examination couch, which can lead to significant errors.

CONCLUSION

The continuing rise of computing power suggests a near future where MC methods become practical for routine clinical dosimetry. Dynamic, deformable phantoms help to improve patient specificity, but at present are only limited to adjustment of gross body volume. Dynamic internal organ displacement or reshaping is likely the next logical frontier. Image-based alignment is probably the most promising solution to enable this, but it must be automated to be clinically practical.

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organ-at-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼ 8    mm for liver LR, IS, and spleen AP, IS; ∼ 5    mm for liver AP and spleen LR; ∼ 80 % for abdomen sDSC; and ∼ 60 % to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially ( + 5 - mm error for spleen IS, - 10 % sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.

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.

Construction of patient-specific computational models for organ dose estimation in radiological imaging

PURPOSE

Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ-level dose assessment in x-ray computed tomography (CT) imaging.

METHODS

Regional voxel models representing reference patient-specific computational phantoms were generated through image segmentation of CT images for four patients. The best-fitting anthropomorphic phantoms were selected from a previously developed comprehensive phantom library according to patient's anthropometric parameters, then registered to the anatomical masks (skeleton, lung, and body contour) of patients to produce a patient-specific whole-body phantom. Well-established image registration metrics including Jaccard's coefficients for each organ, organ mass, body perimeter, organ-surface distance, and effective diameter are compared between the reference patient model, registered model, and anchor phantoms. A previously validated Monte Carlo code is utilized to calculate the absorbed dose in target organs along with the effective dose delivered to patients. The calculated absorbed doses from the reference patient models are then compared with the produced personalized model, anchor phantom, and those reported by commercial dose monitoring systems.

RESULTS

The evaluated organ-surface distance and body effective diameter metrics show a mean absolute difference between patient regional voxel models, serving as reference, and patient-specific models around 4.4% and 4.5%, respectively. Organ-level radiation doses of patient-specific models are in good agreement with those of the corresponding patient regional voxel models with a mean absolute difference of 9.1%. The mean absolute difference of organ doses for the best-fitting model extracted from the phantom library and Radimetrics™ commercial dose tracking software are 15.5% and 41.1%, respectively.

CONCLUSION

The results suggest that the proposed methodology improves the accuracy of organ-level dose estimation in CT, especially for extreme cases [high body mass index (BMI) and large skeleton]. Patient-specific radiation dose calculation and risk assessment can be performed using the proposed methodology for both monitoring of cumulative radiation exposure of patients and epidemiological studies. Further validation using a larger database is warranted.

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.

Averaged head phantoms from magnetic resonance images of Korean children and young adults

Increased use of mobile phones raises concerns about the health risks of electromagnetic radiation. Phantom heads are routinely used for radiofrequency dosimetry simulations, and the purpose of this study was to construct averaged phantom heads for children and young adults. Using magnetic resonance images (MRI), sectioned cadaver images, and a hybrid approach, we initially built template phantoms representing 6-, 9-, 12-, 15-year-old children and young adults. Our subsequent approach revised the template phantoms using 29 averaged items that were identified by averaging the MRI data from 500 children and young adults. In females, the brain size and cranium thickness peaked in the early teens and then decreased. This is contrary to what was observed in males, where brain size and cranium thicknesses either plateaued or grew continuously. The overall shape of brains was spherical in children and became ellipsoidal by adulthood. In this study, we devised a method to build averaged phantom heads by constructing surface and voxel models. The surface model could be used for phantom manipulation, whereas the voxel model could be used for compliance test of specific absorption rate (SAR) for users of mobile phones or other electronic devices.

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.