Faster dense deformable image registration by utilizing both CPU and GPU

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Sökning: WFRF:(Ahlström Håkan) > (2020-2021) > Faster dense deform...

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Fältnamn	Indikatorer	Metadata
000		03869naa a2200409 4500
001		oai:DiVA.org:uu-437222
003		SwePub
008		210308s2021 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4372222 URI
024	7	a https://doi.org/10.1117/1.JMI.8.1.0140022 DOI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Ekström, Simon,d 1991-u Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)4 aut0 (Swepub:uu)simek989
245	1 0	a Faster dense deformable image registration by utilizing both CPU and GPU
264	1	c 2021
338		a electronic2 rdacarrier
520		a Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
650	7	a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng
653		a Atlas-based segmentation
653		a brain MRI
653		a deformable image registration
653		a graphics processing unit
700	1	a Pilia, Martinou Uppsala universitet,Radiologi4 aut
700	1	a Kullberg, Joel,d 1979-u Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)4 aut0 (Swepub:uu)jokul377
700	1	a Ahlström, Håkan,d 1953-u Uppsala universitet,Radiologi,Antaros Medical AB (Sweden)4 aut0 (Swepub:uu)hakanahl
700	1	a Strand, Robin,d 1978-u Uppsala universitet,Radiologi,Institutionen för informationsteknologi4 aut0 (Swepub:uu)rostr275
700	1	a Malmberg, Filip,d 1980-u Uppsala universitet,Radiologi4 aut0 (Swepub:uu)filma606
710	2	a Uppsala universitetb Radiologi4 org
773	0	t Journal of Medical Imagingg 8:1q 8:1x 2329-4302x 2329-4310
856	4	u https://doi.org/10.1117/1.JMI.8.1.014002y Fulltext
856	4	u https://uu.diva-portal.org/smash/get/diva2:1535351/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-437222
856	4 8	u https://doi.org/10.1117/1.JMI.8.1.014002

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Av författaren/redakt...: Ekström, Simon, ...; Pilia, Martino; Kullberg, Joel, ...; Ahlström, Håkan, ...; Strand, Robin, 1 ...; Malmberg, Filip, ...

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