  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
across non-contrast CT and early arterial, late arterial, venous and delayed
contrast enhanced CT. Briefly, we introduce a deep learning-based volume of
interest extraction method and an automated two-stage hierarchal registration
pipeline to register abdominal volumes to a high-resolution CT atlas template.
To generate and evaluate the atlas, multi-contrast modality CT scans of 500
subjects (without reported history of renal disease, age: 15-50 years, 250
males & 250 females) were processed. We demonstrate a stable generalizability
of the atlas template for integrating the normal kidney variation from small to
large, across contrast modalities and populations with great variability of
demographics. The linkage of atlas and demographics provided a better
understanding of the variation of kidney anatomy across populations.
