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Among the various metrics that quantify radiation dose in computed tomography (CT), organ dose is one of the most representative quantities reflecting patient-specific radiation burden.1 Accurate estimation of organ dose requires one to effectively model the patient anatomy and the irradiation field. As illustrated in previous studies, the patient anatomy factor can be modeled using a library of computational phantoms with representative body habitus.2 However, the modeling of irradiation field can be practically challenging, especially for CT exams performed with tube current modulation. The central challenge is to effectively quantify the scatter irradiation field created by the dynamic change of tube current. In this study, we present a convolution-based technique to effectively quantify the primary and scatter irradiation field for TCM examinations. The organ dose for a given clinical patient can then be rapidly determined using the convolution-based method, a patient-matching technique, and a library of computational phantoms. 58 adult patients were included in this study (age range: 18–70 y.o., weight range: 60–180 kg). One computational phantom was created based on the clinical images of each patient. Each patient was optimally matched against one of the remaining 57 computational phantoms using a leave-one-out strategy. For each computational phantom, the organ dose coefficients (CTDIvol-normalized organ dose) under fixed tube current were simulated using a validated Monte Carlo simulation program. Such organ dose coefficients were multiplied by a scaling factor, (CTDIvol )organ, convolution that quantifies the regional irradiation field. The convolution-based organ dose was compared with the organ dose simulated from Monte Carlo program with TCM profiles explicitly modeled on the original phantom created based on patient images. The estimation error was within 10% across all organs and modulation profiles for abdominopelvic examination. This strategy enables prospective and retrospective patient-specific dose estimation without the need of Monte Carlo simulation.
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