  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 radiation
dose 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 CT
segmentation. Organs challenging to segment (e.g. intestines) are incorporated
from a matched phantom template, using a diffeomorphic registration model
developed for multi-organ phantom-voxels. The resulting full-patient phantoms
are used to assess organ doses during routine CT exams. Result: iPhantom was
validated on both the XCAT (n=50) and an independent clinical (n=10) dataset
with similar accuracy. iPhantom precisely predicted all organ locations with
good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and
DSC of 0.3-0.9 for all other organs. iPhantom showed less than 10% dose errors
for the majority of organs, which was notably superior to the state-of-the-art
baseline method (20-35% dose errors). Conclusion: iPhantom enables automated
and accurate creation of patient-specific phantoms and, for the first time,
provides sufficient and automated patient-specific dose estimates for CT
dosimetry. Significance: The new framework brings the creation and application
of CHPs to the level of individual CHPs through automation, achieving a wider
and precise organ localization, paving the way for clinical monitoring, and
personalized optimization, and large-scale research.
