IPhantom: An automated framework in generating personalized computational phantoms for organ-based radiation dosimetry
We propose an automated framework to generate 3D detailed person-specific computational phantoms directly from patient medical images. We investigate the feasibility of this framework in terms of accurately generating patient-specific phantoms and the clinical utility in estimating patient-specific organ dose for CT images. The proposed framework generates 3D volumetric phantoms with a comprehensive set of radiosensitive organs, by fusing patient image data with prior anatomical knowledge from a library of computational phantoms in a two-stage approach. In the first stage, the framework segments a selected set of organs from patient medical images as anchors. In the second stage, conditioned on the segmented organs, the framework generates unsegmented anatomies through mappings between anchor and nonanchor organs learned from libraries of phantoms with rich anatomy. We applied this framework to clinical CT images and demonstrated its utility for patient-specific organ dosimetry. The result showed the framework generates patientspecific phantoms in ∼10 seconds and provides Monte Carlo based organ dose estimation in ∼30 seconds with organ dose errors <10% for the majority of organs. The framework shows the potential for large scale and real-time clinic analysis, standardization, and optimization.
