Determining organ dose: the holy grail.

Abstract

Among the various metrics to quantify CT radiation dose, organ dose is generally regarded as one of the best to reflect patient radiation burden. Organ dose is dependent on two main factors, namely patient anatomy and irradiation field. An accurate estimation of organ dose requires detailed modeling of both factors. The modeling of patient anatomy needs to reflect the anatomical diversity and complexity across the population so that the attributes of a given clinical patient can be properly accounted for. The modeling of the irradiation field needs to accurately reflect the CT system condition, especially the tube current modulation (TCM) technique. We present an atlas-based method to model patient anatomy via a library of computational phantoms with representative ages, sizes and genders. A clinical patient is matched with a corresponding computational phantom to obtain a representation of patient anatomy. The irradiation field of the CT system is modeled using a validated Monte Carlo simulation program. The tube current modulation profiles are simulated using a manufacturer-generalizable ray-tracing algorithm. Combining the patient model, Monte Carlo results, and TCM profile, organ doses are obtained by multiplying organ dose values from a fixed mA scan (normalized to CTDIvol-normalized, denoted as h organ ) and an adjustment factor that reflects the specific irradiation of each organ. The accuracy of the proposed method was quantified by simulating clinical abdominopelvic examinations of 58 patients. The predicted organ doses showed good agreement with simulated organ dose across all organs and modulation schemes. For an average CTDIvol of a CT exam of 10 mGy, the absolute median error across all organs was 0.64 mGy (-0.21 and 0.97 for 25th and 75th percentiles, respectively). The percentage differences were within 15%. The study demonstrates that it is feasible to estimate organ doses in clinical CT examinations for protocols without and with tube current modulation. The methodology can be used for both prospective and retrospective estimation of organ dose.