Optimized GPU implementation of learning-based non-rigid multi-modal registration

Zhe Fan, Christoph Vetter, Christoph Guetter, Daphne Yu, Rüdiger Westermann, Arie Kaufman, Chenyang Xu

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

7 Zitate (Scopus)

Abstract

Non-rigid multi-modal volume registration is computationally intensive due to its high-dimensional parameter space, where common CPU computation times are several minutes. Medical imaging applications using registration, however, demand ever faster implementations for several purposes: matching the data acquisition speed, providing smooth user interaction and steering for quality control, and performing population registration involving multiple datasets. Current GPUs offer an opportunity to boost the registration speed through high, computational power at low cost. In our previous work, we have presented a GPU implementation of a non-rigid multi-modal volume registration that was 6-8 limes faster than a software implementation. in this paper, we extend this; work by describing how new features of the DX10-compatible GPUs and additional optimization strategies can be employed to further improve the algorithm performance. We have compared our optimized version with the previous version on the same GPU, and have observed a speedup factor of 3.6. Compared with the software Implementation, we achieve a speedup factor of up to 44.

OriginalspracheEnglisch
TitelMedical Imaging 2008
UntertitelImage Processing
DOIs
PublikationsstatusVeröffentlicht - 2008
VeranstaltungMedical Imaging 2008: Image Processing - San Diego, CA, USA/Vereinigte Staaten
Dauer: 17 Feb. 200819 Feb. 2008

Publikationsreihe

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Band6914
ISSN (Print)1605-7422

Konferenz

KonferenzMedical Imaging 2008: Image Processing
Land/GebietUSA/Vereinigte Staaten
OrtSan Diego, CA
Zeitraum17/02/0819/02/08

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