Name
Abstract | ||
|---|---|---|
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 2563 clinical images in less than 6 s on a single NVIDIA Tesla V100. This amounts to over 20× speed-up over the current version of CLAIRE and over 30× speed-up over existing GPU implementations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jpdc.2020.11.006 | Journal of Parallel and Distributed Computing |
Keywords | DocType | Volume |
GPU computing,Parallel optimization,Diffeomorphic image registration,Mixed-precision solver,Gauss–Newton–Krylov method | Journal | 149 |
ISSN | Citations | PageRank |
0743-7315 | 1 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Malte Brunn | 1 | 1 | 0.71 |
| Naveen Himthani | 2 | 1 | 0.71 |
| George Biros | 3 | 938 | 77.86 |
| Miriam Mehl | 4 | 106 | 15.93 |
| Andreas Mang | 5 | 35 | 10.57 |