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

Martin C W Leong,Ka-Wai Kwok,Nassir Navab,Kit-Hang Lee,Yui-Lun Ng,Zhiyu Liu,Bowen P Y Kwan,Wayne Luk

doi:10.1007/s11548-020-02303-y

Abstract

PurposeIntensity-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.MethodsIntensity-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.ResultsBy 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.ConclusionThe 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.

Highlights

Image registration is a fundamental process in medical image analysis that provides accurate alignment of two image datasets
All experiments are performed using a PC equipped with Intel i7-4790 central processing units (CPUs) (3.6 GHz) and a Nvidia GTX Titan X graphics processing units (GPUs)
A collection of sample brain MRI image is acquired from The Cancer Imaging Archive (TCIA) [26] for the benchmarking purpose

Summary

Introduction

Image registration is a fundamental process in medical image analysis that provides accurate alignment of two image datasets. By intra-operatively registering the two image sets, the surgical roadmap can be integrated with the real-time electro-anatomical mapping to facilitate complete pulmonary vein isolation [7, 8]. Despite the quick and high-resolution imaging of modern imaging systems, reliable intra-op registration is still computationally intensive, often requiring time in the order of minutes. The time for registration should be kept below 10 s right after each EP ablative lesion created [9]. This would allow for simple integration into the surgical workflow, and provide surgeons with up-to-date physiological information, e.g., ablation progress

Methods

Results

Conclusion