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A Matrix-Free Approach to Parallel and Memory-Efficient Deformable Image Registration

 
: König, L.; Rühaak, J.; Derksen, A.; Lellmann, J.

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SIAM journal on scientific computing 40 (2018), Nr.3, S.B858-B888
ISSN: 0196-5204
ISSN: 1064-8275
ISSN: 0885-7474
Englisch
Zeitschriftenaufsatz
Fraunhofer MEVIS ()

Abstract
We present a novel computational approach to fast and memory-efficient deformable image registration. In the variational registration model, the computation of the objective function derivatives is the computationally most expensive operation, both in terms of runtime and memory requirements. In order to target this bottleneck, we analyze the matrix structure of gradient and Hessian computations for the case of the normalized gradient fields distance measure and curvature regularization. Based on this analysis, we derive equivalent matrix-free closed-form expressions for derivative computations, eliminating the need for storing intermediate results and the costs of sparse matrix arithmetic. This has further benefits: (1) matrix computations can be fully parallelized, (2) memory complexity for derivative computation is reduced from linear to constant, and (3) overall computation times are substantially reduced. In comparison with an optimized matrix-based reference implementation, the CPU implementation achieves speedup factors between 3.1 and 9.7, and we are able to handle substantially higher resolutions. Using a GPU implementation, we achieve an additional speedup factor of up to 9.2. Furthermore, we evaluated the approach on real-world medical datasets. On 10 publicly available lung computed tomography (CT) images from the DIR-Lab 4DCT dataset, we achieve the best mean landmark error of 0.93mm compared to other submissions on the DIR-Lab website, with an average runtime of only 9.23s. Complete nonrigid registration of full-size three-dimensional thorax-abdomen CT volumes from oncological followup is achieved in 12.6s. The experimental results show that the proposed matrix-free algorithm enables the use of variational registration models also in applications which were previously impractical due to memory or runtime restrictions.


: http://publica.fraunhofer.de/dokumente/N-515503.html