Multi-contrast computed tomography healthy kidney atlas

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 for the kidney organ across non-contrast CT and early arterial, late arterial, venous and delayed contrast-enhanced CT. We introduce a deep learning-based volume interest extraction method by localizing the 2D slices with a representative score and crop within the range of the abdominal interest. An automated two-stage hierarchal registration pipeline is then performed to register abdominal volumes to a high-resolution CT atlas template with DEEDS affine and non-rigid registration. To generate and evaluate the atlas framework, multi-contrast modality CT scans of 500 subjects (without reported history of renal disease, age: 15–50 years, 250 males & 250 females) were processed. PDD-Net with affine registration achieved the best overall mean DICE for portal venous phase multi-organs label transfer with the registration pipeline (0.540 ± 0.275, p