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

Abstract

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.

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

“…The pre‐truncated original anchor image is used to generate a computational model using the rescaling method and compared with the proposed stitching model with respect to the estimated organ mass, anatomical accuracy, organ dose, and effective dose. As the registration‐based method has proven to be superior to the best‐fitting model, 31 methods using standardized computational phantoms were not compared in this work. We compared the registration‐based rescaling method with the WED‐based rescaling method.…”

“…29 Rescaled phantoms were also used to represent patients of different sizes for both tissue complement and organ auto-segmentation, 30,31 while patient-specific phantom matching performed better with water equivalent diameter (WED) than other anthropometric measurments. 30 Xie et al 31 developed patientspecific computational models by rescaling phantoms using automatic registration. However, phantom-based representations of individual patients has the disadvantages of inherent anatomy deviation owing to the loss of detailed organ position, which depends on manual affine transformations.…”

“…Rescaled phantoms were also used to represent patients of different sizes for both tissue complement and organ auto‐segmentation, 30,31 while patient‐specific phantom matching performed better with water equivalent diameter (WED) than other anthropometric measurments 30 . Xie et al 31 . developed patient‐specific computational models by rescaling phantoms using automatic registration.…”

An update on computational anthropomorphic anatomical models

“…In state-of-the-art approaches, this process was labor-intensive and time-consuming which limited the construction of patient-specific computational models. 89 Recently Carter et al proposed to use deformable registration techniques to create individualized phantoms to better support patient-specific dosimetry. 90 Thanks to recent advances in artificial intelligence algorithms, fully automated segmentation of medical images became feasible.…”

Whole-body voxel-based internal dosimetry using deep learning

“…This approach assumes a uniform activity distribution within each organ and ignores individual anatomical characteristics. To cope with inter-subject variability of anatomical features, the organ-level dosimetry approach was later extended by developing habitus-specific and patientspecific computational models [5][6][7][8][9]. Furthermore, voxelbased dosimetry techniques have been developed, including dose point kernel [10] and voxel S-value (VSV) [4] approaches.…”