XCAT-30: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans
<|reference_start|>XCAT-30: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans: 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 VITs. 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, the more realistic computational 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 automatic segmentation models and performing three forms of automated quality control on the segmented organ masks. The result is the release of over 2500 new computational phantoms, so-named XCAT3.0 after the ubiquitous XCAT computational construct. This new formation embodies 140 structures and represents a comprehensive approach to detailed anatomical modeling. The developed computational phantoms are formatted in both voxelized and 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/.<|reference_end|>
@article{dahal2024xcat-3.0:,
    title={XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived
  from CT Scans},
    author={Lavsen Dahal, Mobina Ghojoghnejad, Dhrubajyoti Ghosh, Yubraj Bhandari,
  David Kim, Fong Chi Ho, Fakrul Islam Tushar, Sheng Luoa, Kyle J. Lafata,
  Ehsan Abadi, Ehsan Samei, Joseph Y. Lo, W. Paul Segars},
    journal={arXiv preprint arXiv:2405.11133},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11133},
    primaryClass={eess.IV cs.CV}
}