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 non-anchor 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 patient-specific phantoms in similar to 10 seconds and provides Monte Carlo based organ dose estimation in similar to 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.