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The Computational Anatomy Gateway is a software as a service tool for medical imaging researchers to quantify changes in anatomical structures over time, and through the progression of disease. GPU acceleration on the Stampede cluster has enabled the development of new tools, combining advantages of grid based and particle based methods for describing fluid flows, and scaling up analysis from single scans to populations and timeseries. We describe algorithms for estimating average anatomies, and for quantifying atrophy rate over time. We report code performance on different sized datasets, revealing that the number vertices in a triangulated surface presents a bottleneck to our computation. We show results on an example dataset, quantifying atrophy in the entorhinal cortex, a medial temporal lobe brain region whose structure is sensitive changes in early Alzheimer's disease.

Tools for studying populations and timeseries of neuroanatomy enabled through GPU acceleration in the Computational Anatomy Gateway