ArticleJournal of parallel and distributed computing2021
Fast GPU 3D diffeomorphic image registration.
Article in Journal of parallel and distributed computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CLAIRE-Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications.Journal of imaging · 2022Article
- Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework.Sensors (Basel, Switzerland) · 2022Article
- Learning the Effect of Registration Hyperparameters with HyperMorph.The journal of machine learning for biomedical imaging · 2022Article
- Accurate Point Cloud Registration with Robust Optimal Transport.Advances in neural information processing systems · 2021Article
- CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures.Journal of open source software · 2021Article
- Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems.International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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 256
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