Abstract
We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU). We build on recent matrix-free variational approaches and specialize the concepts to the massively-parallel manycore architecture provided by the GPU. Compared to a parallel and optimized CPU implementation, this allows us to achieve an average speedup of 32.53 on 986 real-world CT thorax-abdomen follow-up scans. At a resolution of approximately 256 3 voxels, the average runtime is 1.99 seconds for the full registration. On the publicly available DIR-lab benchmark, our method ranks third with respect to average landmark error at an average runtime of 0.32 seconds.
Originalsprache | Englisch |
---|---|
Titel | Bildverarbeitung für die Medizin 2019 |
Seitenumfang | 6 |
Herausgeber (Verlag) | Springer Berlin Heidelberg |
Erscheinungsdatum | 07.02.2019 |
Seiten | 302-307 |
ISBN (Print) | 978-3-658-25325-7 |
ISBN (elektronisch) | 978-3-658-25326-4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 07.02.2019 |
Veranstaltung | Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Deutschland Dauer: 17.03.2019 → 19.03.2019 Konferenznummer: 224899 |