Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing
We present a method for accelerating the acquisition of phase-encoded velocity images by the use of compressed sensing (CS), a technique that exploits the observation that an under-sampled signal can be accurately reconstructed by utilising the prior knowledge that it is sparse or compressible. We p...
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Veröffentlicht in: | Journal of magnetic resonance (1997) 2010-04, Vol.203 (2), p.236-246 |
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container_title | Journal of magnetic resonance (1997) |
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creator | Holland, D.J. Malioutov, D.M. Blake, A. Sederman, A.J. Gladden, L.F. |
description | We present a method for accelerating the acquisition of phase-encoded velocity images by the use of compressed sensing (CS), a technique that exploits the observation that an under-sampled signal can be accurately reconstructed by utilising the prior knowledge that it is sparse or compressible. We present results of both simulated and experimental measurements of liquid flow through a packed bed of spherical glass beads. For this system, the best image reconstruction used a spatial finite-differences transform. The reconstruction was further improved by utilising prior knowledge of the liquid distribution within the image. Using this approach, we demonstrate that for a sampling fraction of ∼30% of the full k-space data set, the velocity can be recovered with a relative error of 11%, which is below the visually detectable limit. Furthermore, the error in the total flow measured using the CS reconstruction is |
doi_str_mv | 10.1016/j.jmr.2010.01.001 |
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We present results of both simulated and experimental measurements of liquid flow through a packed bed of spherical glass beads. For this system, the best image reconstruction used a spatial finite-differences transform. The reconstruction was further improved by utilising prior knowledge of the liquid distribution within the image. Using this approach, we demonstrate that for a sampling fraction of ∼30% of the full k-space data set, the velocity can be recovered with a relative error of 11%, which is below the visually detectable limit. Furthermore, the error in the total flow measured using the CS reconstruction is <3% for sampling fractions ⩾30%. Thus, quantitative velocity images were obtained in a third of the acquisition time required using conventional imaging. The reduction in data acquisition time can also be exploited in acquiring images at a higher spatial resolution, which increases the accuracy of the measurements by reducing errors arising from partial volume effects. To illustrate this, the CS algorithm was used to reconstruct gas-phase velocity images at a spatial resolution of 230μm×230μm. Images at this spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques.</description><identifier>ISSN: 1090-7807</identifier><identifier>EISSN: 1096-0856</identifier><identifier>DOI: 10.1016/j.jmr.2010.01.001</identifier><identifier>PMID: 20138789</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Algorithms ; Compressed ; Compressed sensing ; Data Compression - methods ; Error analysis ; Gas ; Imaging ; Liquid ; Magnetic resonance ; Magnetic Resonance Spectroscopy - methods ; Marketing ; Porous media ; Reconstruction ; Rheology - methods ; Sampling ; Spatial resolution ; Velocity imaging</subject><ispartof>Journal of magnetic resonance (1997), 2010-04, Vol.203 (2), p.236-246</ispartof><rights>2010 Elsevier Inc.</rights><rights>2010 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-482ee285028bed448bcf8f2190c426f366ca0627259bce91de32a33468aa4d303</citedby><cites>FETCH-LOGICAL-c384t-482ee285028bed448bcf8f2190c426f366ca0627259bce91de32a33468aa4d303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1090780710000029$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20138789$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Holland, D.J.</creatorcontrib><creatorcontrib>Malioutov, D.M.</creatorcontrib><creatorcontrib>Blake, A.</creatorcontrib><creatorcontrib>Sederman, A.J.</creatorcontrib><creatorcontrib>Gladden, L.F.</creatorcontrib><title>Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing</title><title>Journal of magnetic resonance (1997)</title><addtitle>J Magn Reson</addtitle><description>We present a method for accelerating the acquisition of phase-encoded velocity images by the use of compressed sensing (CS), a technique that exploits the observation that an under-sampled signal can be accurately reconstructed by utilising the prior knowledge that it is sparse or compressible. We present results of both simulated and experimental measurements of liquid flow through a packed bed of spherical glass beads. For this system, the best image reconstruction used a spatial finite-differences transform. The reconstruction was further improved by utilising prior knowledge of the liquid distribution within the image. Using this approach, we demonstrate that for a sampling fraction of ∼30% of the full k-space data set, the velocity can be recovered with a relative error of 11%, which is below the visually detectable limit. Furthermore, the error in the total flow measured using the CS reconstruction is <3% for sampling fractions ⩾30%. Thus, quantitative velocity images were obtained in a third of the acquisition time required using conventional imaging. The reduction in data acquisition time can also be exploited in acquiring images at a higher spatial resolution, which increases the accuracy of the measurements by reducing errors arising from partial volume effects. To illustrate this, the CS algorithm was used to reconstruct gas-phase velocity images at a spatial resolution of 230μm×230μm. Images at this spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques.</description><subject>Algorithms</subject><subject>Compressed</subject><subject>Compressed sensing</subject><subject>Data Compression - methods</subject><subject>Error analysis</subject><subject>Gas</subject><subject>Imaging</subject><subject>Liquid</subject><subject>Magnetic resonance</subject><subject>Magnetic Resonance Spectroscopy - methods</subject><subject>Marketing</subject><subject>Porous media</subject><subject>Reconstruction</subject><subject>Rheology - methods</subject><subject>Sampling</subject><subject>Spatial resolution</subject><subject>Velocity imaging</subject><issn>1090-7807</issn><issn>1096-0856</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkE1LxDAURYMoOn78ADfSnauOL0nbpLiSwS8YEEQ3bkKavGqGaTuTtAP-e1NndKmbvCSce-EdQs4pTCnQ4moxXTR-yiC-gU4B6B6ZUCiLFGRe7H_fIRUSxBE5DmERAZoLOCRHMcKlkOWEvD2jHYxr3xOre51osx5ccL3r2qR3DYbEtcnqQwdMsTWdRZtscNkZ138mrtHvY3AI42m6ZuUxhEgEbMevU3JQ62XAs908Ia93ty-zh3T-dP84u5mnhsusTzPJEJnMgckKbZbJytSyZrQEk7Gi5kVhNBRMsLysDJbUImea86yQWmeWAz8hl9vele_WA4ZeNS4YXC51i90QlMi54EJQ9j_JOS-BsTySdEsa34XgsVYrH_f1n4qCGt2rhYru1eheAVVRbcxc7NqHqkH7m_iRHYHrLYDRxsahV8G4qBWt82h6ZTv3R_0XbsmUvQ</recordid><startdate>201004</startdate><enddate>201004</enddate><creator>Holland, D.J.</creator><creator>Malioutov, D.M.</creator><creator>Blake, A.</creator><creator>Sederman, A.J.</creator><creator>Gladden, L.F.</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>201004</creationdate><title>Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing</title><author>Holland, D.J. ; Malioutov, D.M. ; Blake, A. ; Sederman, A.J. ; Gladden, L.F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-482ee285028bed448bcf8f2190c426f366ca0627259bce91de32a33468aa4d303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Compressed</topic><topic>Compressed sensing</topic><topic>Data Compression - methods</topic><topic>Error analysis</topic><topic>Gas</topic><topic>Imaging</topic><topic>Liquid</topic><topic>Magnetic resonance</topic><topic>Magnetic Resonance Spectroscopy - methods</topic><topic>Marketing</topic><topic>Porous media</topic><topic>Reconstruction</topic><topic>Rheology - methods</topic><topic>Sampling</topic><topic>Spatial resolution</topic><topic>Velocity imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Holland, D.J.</creatorcontrib><creatorcontrib>Malioutov, D.M.</creatorcontrib><creatorcontrib>Blake, A.</creatorcontrib><creatorcontrib>Sederman, A.J.</creatorcontrib><creatorcontrib>Gladden, L.F.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of magnetic resonance (1997)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Holland, D.J.</au><au>Malioutov, D.M.</au><au>Blake, A.</au><au>Sederman, A.J.</au><au>Gladden, L.F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing</atitle><jtitle>Journal of magnetic resonance (1997)</jtitle><addtitle>J Magn Reson</addtitle><date>2010-04</date><risdate>2010</risdate><volume>203</volume><issue>2</issue><spage>236</spage><epage>246</epage><pages>236-246</pages><issn>1090-7807</issn><eissn>1096-0856</eissn><abstract>We present a method for accelerating the acquisition of phase-encoded velocity images by the use of compressed sensing (CS), a technique that exploits the observation that an under-sampled signal can be accurately reconstructed by utilising the prior knowledge that it is sparse or compressible. We present results of both simulated and experimental measurements of liquid flow through a packed bed of spherical glass beads. For this system, the best image reconstruction used a spatial finite-differences transform. The reconstruction was further improved by utilising prior knowledge of the liquid distribution within the image. Using this approach, we demonstrate that for a sampling fraction of ∼30% of the full k-space data set, the velocity can be recovered with a relative error of 11%, which is below the visually detectable limit. Furthermore, the error in the total flow measured using the CS reconstruction is <3% for sampling fractions ⩾30%. Thus, quantitative velocity images were obtained in a third of the acquisition time required using conventional imaging. The reduction in data acquisition time can also be exploited in acquiring images at a higher spatial resolution, which increases the accuracy of the measurements by reducing errors arising from partial volume effects. To illustrate this, the CS algorithm was used to reconstruct gas-phase velocity images at a spatial resolution of 230μm×230μm. Images at this spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>20138789</pmid><doi>10.1016/j.jmr.2010.01.001</doi><tpages>11</tpages></addata></record> |
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subjects | Algorithms Compressed Compressed sensing Data Compression - methods Error analysis Gas Imaging Liquid Magnetic resonance Magnetic Resonance Spectroscopy - methods Marketing Porous media Reconstruction Rheology - methods Sampling Spatial resolution Velocity imaging |
title | Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing |
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