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Conference Paper: Real-time GPU-based software beamformer designed for advanced imagingmethods research

TitleReal-time GPU-based software beamformer designed for advanced imagingmethods research
Authors
Keywordsgraphicsprocessing units
parallel processing
plane wave imaging
software beamformer
synthetic aperture imaging
Issue Date2010
PublisherIEEE.
Citation
The 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1920-1923 How to Cite?
AbstractHigh computational demand is known to be a technical hurdle for real-timeimplementation of advanced methods like synthetic aperture imaging (SAI) andplane wave imaging (PWI) that work with the pre-beamform data of each arrayelement. In this paper, we present the development of a software beamformer forSAI and PWI with real-time parallel processing capacity. Our beamformer designcomprises a pipelined group of graphics processing units (GPU) that are hostedwithin the same computer workstation. During operation, each available GPU isassigned to perform demodulation and beamforming for one frame of pre-beamformdata acquired from one transmit firing (e.g. point firing for SAI). Tofacilitate parallel computation, the GPUs have been programmed to treat thecalculation of depth pixels from the same image scanline as a block ofprocessing threads that can be executed concurrently, and it would repeat thisprocess for all scanlines to obtain the entire frame of image data i.e.low-resolution image (LRI). To reduce processing latency due to repeated accessof each GPU's global memory, we have made use of each thread block's fast-sharedmemory (to store an entire line of pre-beamform data during demodulation),created texture memory pointers, and utilized global memory caches (to streamrepeatedly used data samples during beamforming). Based on this beamformerarchitecture, a prototype platform has been implemented for SAI and PWI, and itsLRI processing throughput has been measured for test datasets with 40 MHzsampling rate, 32 receive channels, and imaging depths between 5-15 cm. Whenusing two Fermi-class GPUs (GTX-470), our beamformer can compute LRIs of512-by-255 pixels at over 3200 fps and 1300 fps respectively for imaging depthsof 5 cm and 15 cm. This processing throughput is roughly 3.2 times higher than aTesla-class GPU (GTX-275). © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/129641
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorYiu, BYSen_HK
dc.contributor.authorTsang, IKHen_HK
dc.contributor.authorYu, ACHen_HK
dc.date.accessioned2010-12-23T08:40:38Z-
dc.date.available2010-12-23T08:40:38Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1920-1923en_HK
dc.identifier.isbn978-1-4577-0381-2-
dc.identifier.issn1051-0117en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129641-
dc.description.abstractHigh computational demand is known to be a technical hurdle for real-timeimplementation of advanced methods like synthetic aperture imaging (SAI) andplane wave imaging (PWI) that work with the pre-beamform data of each arrayelement. In this paper, we present the development of a software beamformer forSAI and PWI with real-time parallel processing capacity. Our beamformer designcomprises a pipelined group of graphics processing units (GPU) that are hostedwithin the same computer workstation. During operation, each available GPU isassigned to perform demodulation and beamforming for one frame of pre-beamformdata acquired from one transmit firing (e.g. point firing for SAI). Tofacilitate parallel computation, the GPUs have been programmed to treat thecalculation of depth pixels from the same image scanline as a block ofprocessing threads that can be executed concurrently, and it would repeat thisprocess for all scanlines to obtain the entire frame of image data i.e.low-resolution image (LRI). To reduce processing latency due to repeated accessof each GPU's global memory, we have made use of each thread block's fast-sharedmemory (to store an entire line of pre-beamform data during demodulation),created texture memory pointers, and utilized global memory caches (to streamrepeatedly used data samples during beamforming). Based on this beamformerarchitecture, a prototype platform has been implemented for SAI and PWI, and itsLRI processing throughput has been measured for test datasets with 40 MHzsampling rate, 32 receive channels, and imaging depths between 5-15 cm. Whenusing two Fermi-class GPUs (GTX-470), our beamformer can compute LRIs of512-by-255 pixels at over 3200 fps and 1300 fps respectively for imaging depthsof 5 cm and 15 cm. This processing throughput is roughly 3.2 times higher than aTesla-class GPU (GTX-275). © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofProceedings of the IEEE International Ultrasonics Symposium, IEEE IUS 2010en_HK
dc.rightsProceedings of the 2010 IEEE International Ultrasonics Symposium. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectgraphicsprocessing unitsen_HK
dc.subjectparallel processingen_HK
dc.subjectplane wave imagingen_HK
dc.subjectsoftware beamformeren_HK
dc.subjectsynthetic aperture imagingen_HK
dc.titleReal-time GPU-based software beamformer designed for advanced imagingmethods researchen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4577-0381-2&volume=&spage=1920&epage=1923&date=2010&atitle=Real-time+GPU-based+software+beamformer+designed+for+advanced+imaging+methods+research-
dc.identifier.emailYu, ACH:alfred.yu@hku.hken_HK
dc.identifier.authorityYu, ACH=rp00657en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ULTSYM.2010.5935689en_HK
dc.identifier.scopuseid_2-s2.0-80054073904en_HK
dc.identifier.hkuros176828en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80054073904&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1920en_HK
dc.identifier.epage1923en_HK
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1920-1923-
dc.identifier.scopusauthoridYiu, BYS=26657783600en_HK
dc.identifier.scopusauthoridTsang, IKH=26657657600en_HK
dc.identifier.scopusauthoridYu, ACH=8699317700en_HK

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