Dahal Lavsen, Ghojoghnejad Mobina, Vancoillie Liesbeth, Ghosh Dhrubajyoti, Bhandari Yubraj, Kim David, Ho Fong Chi, Tushar Fakrul Islam, Luo Sheng, Lafata Kyle J, Abadi Ehsan, Samei Ehsan, Lo Joseph Y, Segars W Paul
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC, 27708, USA; Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC, 27708, USA.
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC, 27708, USA.
Med Image Anal. 2025 Jul;103:103636. doi: 10.1016/j.media.2025.103636. Epub 2025 May 3.
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 heterogeneity. 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 XCAT 3 generation of computational phantoms. 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/.
虚拟成像试验（VIT）为评估医学成像技术提供了一种经济高效且可扩展的方法。模拟真实患者解剖结构和生理特征的计算体模在VIT中起着核心作用。然而，当前的计算体模库存在局限性，尤其是在样本量和异质性方面。人群代表性不足阻碍了对不同患者群体成像技术的准确评估。传统上，更逼真的计算体模是通过手动分割创建的，这是一项费力且耗时的任务，阻碍了体模库的扩展。本研究提出了一个框架，用于使用一套自动分割模型创建逼真的计算体模，并对分割后的器官掩码执行三种形式的自动质量控制。结果是发布了超过2500个新的XCAT第三代计算体模。这种新形式包含140种结构，代表了一种详细解剖建模的综合方法。所开发的计算体模以体素化和表面网格格式进行格式化。该框架与内部CT扫描仪模拟器相结合，以生成逼真的CT图像。该框架有潜力推动虚拟成像试验，促进对医学成像技术进行全面且可靠的评估。可在https://cvit.duke.edu/resources/索取体模。代码、模型权重和样本CT图像可在https://xcat-3.github.io/获取。
