XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.
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2025-07
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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/.
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Dahal, Lavsen, Mobina Ghojoghnejad, Liesbeth Vancoillie, Dhrubajyoti Ghosh, Yubraj Bhandari, David Kim, Fong Chi Ho, Fakrul Islam Tushar, et al. (2025). XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans. Medical image analysis, 103. p. 103636. 10.1016/j.media.2025.103636 Retrieved from https://hdl.handle.net/10161/33676.
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Kyle Jon Lafata
Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments in Radiation Oncology, Radiology, Medical Physics, Electrical & Computer Engineering, and Mathematics. He joined the faculty at Duke in 2020 following postdoctoral training at the US Department of Veterans Affairs. His dissertation work focused on the applied analysis of stochastic partial differential equations and high-dimensional image phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems).
Prof. Lafata has worked in various areas of computational medicine and biology, resulting in over 80 academic papers, 30 invited talks, and more than 100 national conference presentations. At Duke, the Lafata Laboratory focuses on the theory, development, and application of computational oncology. The lab interrogates disease at different length-scales of its biological organization via high-performance computing, multiscale modeling, advanced imaging technology, and the applied analysis of stochastic partial differential equations. Current research interests include tumor topology, cellular dynamics, tumor immune microenvironment, drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity, and biologically-guided adaptative treatment strategies.
Ehsan Abadi
Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Associate Professor in the departments of Radiology and Electrical & Computer Engineering, a faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s research focuses on quantitative imaging and optimization, computational human modeling, medical imaging simulation, and CT imaging in cardiothoracic and musculoskeletal applications. He is actively involved in developing computational anthropomorphic models with various diseases such as COPD, and scanner-specific simulation platforms (e.g., DukeSim) for imaging systems. Currently, his work is centered on identifying and optimizing imaging systems to ensure accurate and precise quantifications of lung and bone diseases.
Joseph Yuan-Chieh Lo
My research is at the intersection of computer vision, machine learning, and medical imaging, with a dual focus on mammography and computed tomography (CT). Together with our industry partner, we developed deep learning algorithms for breast cancer screening with 2D/3D mammography, and that product is now undergoing FDA approval with anticipated rollout to clinics worldwide. We also pioneer the creation of "digital twin" anatomical models from patient imaging data, using these models to forge new paths in CT scan analysis through virtual readers and deep learning techniques. Additionally, we're developing a computer-aided triage system for detecting diseases across multiple organs in body CT scans, leveraging hospital-scale datasets and integrating natural language processing with deep learning for comprehensive disease classification.
William Paul Segars
Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.
These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.
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