A database of 40 patient-based computational models for benchmarking organ dose estimates in CT.
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2020-07-06
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Abstract
PURPOSE:Patient radiation burden in CT can best be characterized through risk estimates derived from organ doses. Organ doses can be estimated by Monte Carlo simulations of the CT procedures on computational phantoms assumed to emulate the patients. However, the results are subject to uncertainties related to how accurately the patient and CT procedure are modeled. Different methods can lead to different results. This paper, based on decades of organ dosimetry research, offers a database of CT scans, scan specifics, and organ doses computed using a validated Monte Carlo simulation of each patient and acquisition. It is aimed that the database can serve as means to benchmark different organ dose estimation methods against a benchmark dataset. ACQUISITION AND VALIDATION METHODS:Organ doses were estimated for 40 adult patients (22 female, 18 female) who underwent Chest and Abdominopelvic CT examinations. Patient-based computational models were created for each patient including 26 organs for female and 25 organs for male cases. A Monte Carlo code, previously validated experimentally, was applied to calculate organ doses under constant and two modulated tube current conditions. DATA FORMAT AND USAGE NOTES:The generated database reports organ dose values for Chest and Abdominopelvic examinations per patient and imaging condition. Patient information and images and scan specifications (energy spectrum, bowtie filter specification, and tube current profiles) are provided. The database is available at publicly accessible digital repositories. POTENTIAL APPLICATIONS:Consistency in patient risk estimation, and associated justification and optimization requires accuracy and consistency in organ dose estimation. The database provided in this paper is a helpful tool to benchmark different organ dose estimation methodologies to facilitate comparisons, assess uncertainties, and improve assessment of risk of CT scans based on organ dose.
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Samei, Ehsan, Francesco Ria, Xiaoyu Tian and Paul W Segars (2020). A database of 40 patient-based computational models for benchmarking organ dose estimates in CT. Medical physics. 10.1002/mp.14373 Retrieved from https://hdl.handle.net/10161/21176.
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Francesco Ria
Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.
Dr. Ria is a member of the American Association of Physicists in Medicine (AAPM) task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the AAPM task group 430 (Comprehensive quantification and dissemination of patient-model-based organ and effective dose estimations and their associated uncertainties for CT examinations), of the AAPM Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.
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