Jo JW, Gahm JK. G-RMOS: GPU-accelerated Riemannian Metric Optimization on Surfaces.
Comput Biol Med 2022;
150:106167. [PMID:
37859279 DOI:
10.1016/j.compbiomed.2022.106167]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/18/2022] [Accepted: 10/01/2022] [Indexed: 11/20/2022]
Abstract
Surface mapping is used in various brain imaging studies, such as for mapping gray matter atrophy patterns in Alzheimer's disease. Riemannian metrics on surface (RMOS) is a state-of-the-art surface mapping algorithm that optimizes Riemannian metrics to establish one-to-one correspondences between surfaces in the Laplace-Beltrami embedding space. However, owing to the complex calculation with accurate one-to-one correspondences, RMOS registration takes a long time. In this study, we propose G-RMOS, a graphics processing unit (GPU)-accelerated RMOS registration pipeline that uses three GPU kernel design strategies: 1. using GPU computing capability with a batch scheme; 2. using the cache in the GPU block to minimize memory latency in register and shared memory; and 3. maximizing the effective number of instructions per GPU cycle using instruction level parallelism. Using the experimental results, we compare the acceleration speed of the G-RMOS framework with that of RMOS using hippocampus and cortical surfaces, and show that G-RMOS achieves a significant speedup in surface mapping. We also compare the memory requirements for cortical surface mapping and show that G-RMOS uses less memory than RMOS.
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