  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 realistic computational phantom
modeling using a suite of four deep learning segmentation models, followed by
three forms of automated organ segmentation quality control. Over 2500
computational phantoms with up to 140 structures illustrating a sophisticated
approach to detailed anatomical modeling are released. Phantoms are available
in both voxelized and surface mesh formats. The framework is aggregated with an
in-house CT scanner simulator to produce realistic CT images. The framework can
potentially 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.
