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Toward automated and personalized organ dose determination in CT examinations: A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Center for Clinical Research Dalarna. Falu Lasarett, Bild & Funkt Med, SE-79182 Falun, Sweden.
Raysearch Labs AB, Box 3297, SE-10365 Stockholm, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
2019 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 46, no 2, p. 1012-1023Article in journal (Refereed) Published
Abstract [en]

Purpose: Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated.

Methods: Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry.

Results: The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs.

Conclusions: After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.

Place, publisher, year, edition, pages
WILEY , 2019. Vol. 46, no 2, p. 1012-1023
Keywords [en]
radiation exposure [N06, 850, 460, 350, 850], radiation oncology [H02, 403, 740, 650], tomography, x-ray computed [E01, 370, 700, 810, 810], Monte Carlo method [N05, 715, 360, 750, 540], risk management [N04, 452, 871]
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:uu:diva-379276DOI: 10.1002/mp.13357ISI: 000459616200056PubMedID: 30582891OAI: oai:DiVA.org:uu-379276DiVA, id: diva2:1296798
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-03-18Bibliographically approved

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Källman, Hans-ErikAhnesjö, Anders

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