Fast GPU 3D Diffeomorphic Image Registration
| Autor: | Brunn, Malte, Himthani, Naveen, Biros, George, Mehl, Miriam, Mang, Andreas |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Journal of Parallel and Distributed Computing 149:149-162, 2021 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.jpdc.2020.11.006 |
| Popis: | 3D image registration is one of the most fundamental and computationally expensive operations in medical image analysis. Here, we present a mixed-precision, Gauss--Newton--Krylov solver for diffeomorphic registration of two images. Our work extends the publicly available CLAIRE library to GPU architectures. Despite the importance of image registration, only a few implementations of large deformation diffeomorphic registration packages support GPUs. Our contributions are new algorithms to significantly reduce the run time of the two main computational kernels in CLAIRE: calculation of derivatives and scattered-data interpolation. We deploy (i) highly-optimized, mixed-precision GPU-kernels for the evaluation of scattered-data interpolation, (ii) replace Fast-Fourier-Transform (FFT)-based first-order derivatives with optimized 8th-order finite differences, and (iii) compare with state-of-the-art CPU and GPU implementations. As a highlight, we demonstrate that we can register $256^3$ clinical images in less than 6 seconds on a single NVIDIA Tesla V100. This amounts to over 20$\times$ speed-up over the current version of CLAIRE and over 30$\times$ speed-up over existing GPU implementations. Comment: 20 pages, 6 figures, 8 tables |
| Databáze: | arXiv |
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