Performance-aware programming for intraoperative intensity-based image registration on graphics processing units

Leong MCW, Lee KH, Kwan BPY, Ng YL, Liu Z, Navab N, Luk W, Kwok KW (2021)

Publication Type: Journal article

Publication year: 2021

Journal

International Journal of Computer Assisted Radiology and Surgery Springer Verlag (Germany)

Book Volume: 16

Pages Range: 375-386

Journal Issue: 3

DOI: 10.1007/s11548-020-02303-y

Abstract

Purpose: Intensity-based image registration has been proven essential in many applications accredited to its unparalleled ability to resolve image misalignments. However, long registration time for image realignment prohibits its use in intra-operative navigation systems. There has been much work on accelerating the registration process by improving the algorithm’s robustness, but the innate computation required by the registration algorithm has been unresolved. Methods: Intensity-based registration methods involve operations with high arithmetic load and memory access demand, which supposes to be reduced by graphics processing units (GPUs). Although GPUs are widespread and affordable, there is a lack of open-source GPU implementations optimized for non-rigid image registration. This paper demonstrates performance-aware programming techniques, which involves systematic exploitation of GPU features, by implementing the diffeomorphic log-demons algorithm. Results: By resolving the pinpointed computation bottlenecks on GPU, our implementation of diffeomorphic log-demons on Nvidia GTX Titan X GPU has achieved ~ 95 times speed-up compared to the CPU and registered a 1.3-M voxel image in 286 ms. Even for large 37-M voxel images, our implementation is able to register in 8.56 s, which attained ~ 258 times speed-up. Our solution involves effective employment of GPU computation units, memory, and data bandwidth to resolve computation bottlenecks. Conclusion: The computation bottlenecks in diffeomorphic log-demons are pinpointed, analyzed, and resolved using various GPU performance-aware programming techniques. The proposed fast computation on basic image operations not only enhances the computation of diffeomorphic log-demons, but is also potentially extended to speed up many other intensity-based approaches. Our implementation is open-source on GitHub at https://bit.ly/2PYZxQz.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Hong Kong (HKU) / 香港大學 HK Hong Kong (HK) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Leong, M.C.W., Lee, K.-H., Kwan, B.P.Y., Ng, Y.-L., Liu, Z., Navab, N.,... Kwok, K.-W. (2021). Performance-aware programming for intraoperative intensity-based image registration on graphics processing units. International Journal of Computer Assisted Radiology and Surgery, 16(3), 375-386. https://dx.doi.org/10.1007/s11548-020-02303-y

MLA:

Leong, Martin C. W., et al. "Performance-aware programming for intraoperative intensity-based image registration on graphics processing units." International Journal of Computer Assisted Radiology and Surgery 16.3 (2021): 375-386.

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