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Objective: This study aims to develop and validate a novel framework,iPhantom, for automated creation of patient-specific phantoms or digital-twins(DT) using patient medical images. The framework is applied to assess radiationdose to radiosensitive organs in CT imaging of individual patients. Method:From patient CT images, iPhantom segments selected anchor organs (e.g. liver,bones, pancreas) using a learning-based model developed for multi-organ CTsegmentation. Organs challenging to segment (e.g. intestines) are incorporatedfrom a matched phantom template, using a diffeomorphic registration modeldeveloped for multi-organ phantom-voxels. The resulting full-patient phantomsare used to assess organ doses during routine CT exams. Result: iPhantom wasvalidated on both the XCAT (n=50) and an independent clinical (n=10) datasetwith similar accuracy. iPhantom precisely predicted all organ locations withgood accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs andDSC of 0.3-0.9 for all other organs. iPhantom showed less than 10% dose errorsfor the majority of organs, which was notably superior to the state-of-the-artbaseline method (20-35% dose errors). Conclusion: iPhantom enables automatedand accurate creation of patient-specific phantoms and, for the first time,provides sufficient and automated patient-specific dose estimates for CTdosimetry. Significance: The new framework brings the creation and applicationof CHPs to the level of individual CHPs through automation, achieving a widerand precise organ localization, paving the way for clinical monitoring, andpersonalized optimization, and large-scale research.

iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry