Multi-Contrast Computed Tomography Healthy Kidney Atlas
Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo,, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey M., Spraggins, Yuankai Huo, Bennett A. Landman

TL;DR
This paper presents a high-resolution multi-contrast CT kidney atlas created using deep learning and hierarchical registration, capturing normal anatomical variability across diverse populations and imaging protocols.
Contribution
It introduces a novel deep learning-based pipeline for constructing a comprehensive kidney atlas from multi-contrast CT scans of 500 subjects.
Findings
The atlas effectively captures kidney variability across different populations.
The method demonstrates high generalizability across contrast modalities.
The atlas aids in understanding normal kidney anatomical variations.
Abstract
The construction of three-dimensional multi-modal tissue maps provides an opportunity to spur interdisciplinary innovations across temporal and spatial scales through information integration. While the preponderance of effort is allocated to the cellular level and explore the changes in cell interactions and organizations, contextualizing findings within organs and systems is essential to visualize and interpret higher resolution linkage across scales. There is a substantial normal variation of kidney morphometry and appearance across body size, sex, and imaging protocols in abdominal computed tomography (CT). A volumetric atlas framework is needed to integrate and visualize the variability across scales. However, there is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT. Hence, we proposed a high-resolution CT retroperitoneal atlas specifically optimized…
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Taxonomy
TopicsMRI in cancer diagnosis · Advanced X-ray and CT Imaging · Colorectal Cancer Screening and Detection
