Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VIT. However, the current libraries of computational phantoms face limitations, particularly in terms of sample size and diversity. Insufficient representation of the population hampers accurate assessment of imaging technologies across different patient groups. Traditionally, phantoms were created by manual segmentation, which is a laborious and time-consuming task, impeding the expansion of phantom libraries. This study presents a framework for creating realistic computational phantoms using a suite of four deep learning segmentation models, then the organ masks undergo three forms of automated quality control. The final result is the release of over 2500 computational phantoms with up to 140 structures illustrating a sophisticated approach to detailed anatomical modeling. Phantoms are available in both voxelized or surface mesh formats. The framework is combined with an in-house CT scanner simulator to produce realistic CT images. The framework has the potential to advance virtual imaging trials, facilitating comprehensive and reliable evaluations of medical imaging technologies. Phantoms may be requested at https://cvit.duke.edu/resources/, code, model weights, and sample CT images are available at https://xcat-3.github.io.
@misc{dahal2024xcat30,
title={XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans},
author={Lavsen Dahal and Mobina Ghojoghnejad and Dhrubajyoti Ghosh and Yubraj Bhandari and David Kim and Fong Chi Ho and Fakrul Islam Tushar and Sheng Luoa and Kyle J. Lafata and Ehsan Abadi and Ehsan Samei and Joseph Y. Lo and W. Paul Segars},
year={2024},
eprint={2405.11133},
archivePrefix={arXiv},
primaryClass={eess.IV}
}