<i>i</i>Phantom: A framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Objective: This study aims to develop and validate a novel framework, <i>i</i>Phantom, 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: Given a volume of patient CT images, <i>i</i>Phantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are 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 digital-twin phantoms are used to assess organ doses during routine CT exams. Result: <i>i</i>Phantom was validated on both with a set of XCAT digital phantoms (n=50) and an independent clinical dataset (n=10) with similar accuracy. <i>i</i>Phantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. <i>i</i>Phantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). Conclusion: <i>i</i>Phantom 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 (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.
Fu, W; Sharma, S; Abadi, E; Iliopoulos, AS; Wang, Q; Sun, X; Lo, JYC; Segars, WP; Samei, E
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