PROJECT SUMMARY The approximately 76 million Computed Tomography (CT) scans performed in the U.S. each year are responsible for half the ionizing radiation delivered by medical procedures. Concern about stochastic cancer risks and recent overdosing incidents has led to increased radiation dose monitoring and mandated dose reporting in several states. The problem addressed by this proposal is that current dose reporting metrics quantify the dose delivered to a plastic cylinder or dose to a phantom model, not the dose to the organs of a specific patient. Numerous national and international reports have identified individual organ dose as the most relevant metric that should be reported. Existing automated tools do not model the patient's anatomy and have >40% organ dose error for some reported cases. This project will develop an automated software tool to provide the new capability of accurate, rapid, and personalized reporting of the radiation dose delivered to a patient's organs as part of every CT scan. This project will leverage expertise from the radiology and radiation oncology fields to develop innovative algorithms that will provide the new capability of personalized CT organ dose estimates that account for scanner and anatomical complexities. To achieve the project aims, a rapid Boltzmann Transport Equation solver will be optimized for CT imaging, expanded to model scanner complexities, and validated against gold-standard simulations and phantom experiments. Automated atlas-based segmentation algorithms will be developed, validated, and combined with novel methods to robustly estimate organ dose. The complete dose estimation tool will be validated in a study of 500 pediatric CT datasets, which will provide valuable information about the magnitude and variation of pediatric CT dose in clinical practice. The resulting organ-dose database will be made publically available as a resource for clinical and technical research. The expected impact of the proposed software tool is: (1) Patient-specific organ doses and dose maps incorporated into electronic medical records for personalized dose reports. (2) Personalized dose minimization when combined with dynamic filters and adaptive tube current modulation. (3) Databases for protocol optimization and epidemiological studies of organ dose and cancer incidence based on accurate dose estimates that quantify the variation in organ dose across the population.