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Fast GPU 3D diffeomorphic image registration
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DOI：10.1016/j.jpdc.2020.11.006.png)
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Abstract
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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 s on a single NVIDIA Tesla V100. This amounts to over 20x speed-up over the current version of CLAIRE and over 30x speed-up over existing GPU implementations. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
GPU computing
Parallel optimization
Diffeomorphic image registration
Mixed-precision solver
Gauss-Newton-Krylov method
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