Sparse Signal Recovery Methods for Multiplexing PET Detector Readout
Nuclear medicine imaging detectors are commonly multiplexed to reduce the number of readout channels. Because the underlying detector signals have a sparse representation, sparse recovery methods such as compressed sensing may be used to develop new multiplexing schemes. Random methods may be used t...
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| Published in: | IEEE transactions on medical imaging 2013-05, Vol.32 (5), p.932-942 |
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Sparse Signal Recovery Methods for Multiplexing PET Detector Readout |
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Article |
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Chinn, G. Olcott, P. D. Levin, C. S. |
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Algorithms Compressed sensing Compressive sensing Computer Simulation Crystals Detectors image acquisition Multiplexing nuclear imaging optimization Positron emission tomography positron emission tomography (PET) Positron-Emission Tomography - instrumentation Positron-Emission Tomography - methods probabilistic and statistical method Signal Processing, Computer-Assisted Signal-To-Noise Ratio Vectors |
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IEEE transactions on medical imaging, 2013-05, Vol.32 (5), p.932-942 |
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Nuclear medicine imaging detectors are commonly multiplexed to reduce the number of readout channels. Because the underlying detector signals have a sparse representation, sparse recovery methods such as compressed sensing may be used to develop new multiplexing schemes. Random methods may be used to create sensing matrices that satisfy the restricted isometry property. However, the restricted isometry property provides little guidance for developing multiplexing networks with good signal-to-noise recovery capability. In this work, we describe compressed sensing using a maximum likelihood framework and develop a new method for constructing multiplexing (sensing) matrices that can recover signals more accurately in a mean square error sense compared to sensing matrices constructed by random construction methods. Signals can then be recovered by maximum likelihood estimation constrained to the support recovered by either greedy ℓ 0 iterative algorithms or ℓ 1 -norm minimization techniques. We show that this new method for constructing and decoding sensing matrices recovers signals with 4%-110% higher SNR than random Gaussian sensing matrices, up to 100% higher SNR than partial DCT sensing matrices 50%-2400% higher SNR than cross-strip multiplexing, and 22%-210% higher SNR than Anger multiplexing for photoelectric events. |
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D.</creatorcontrib><creatorcontrib>Levin, C. S.</creatorcontrib><title>Sparse Signal Recovery Methods for Multiplexing PET Detector Readout</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>Nuclear medicine imaging detectors are commonly multiplexed to reduce the number of readout channels. Because the underlying detector signals have a sparse representation, sparse recovery methods such as compressed sensing may be used to develop new multiplexing schemes. Random methods may be used to create sensing matrices that satisfy the restricted isometry property. However, the restricted isometry property provides little guidance for developing multiplexing networks with good signal-to-noise recovery capability. In this work, we describe compressed sensing using a maximum likelihood framework and develop a new method for constructing multiplexing (sensing) matrices that can recover signals more accurately in a mean square error sense compared to sensing matrices constructed by random construction methods. Signals can then be recovered by maximum likelihood estimation constrained to the support recovered by either greedy ℓ 0 iterative algorithms or ℓ 1 -norm minimization techniques. We show that this new method for constructing and decoding sensing matrices recovers signals with 4%-110% higher SNR than random Gaussian sensing matrices, up to 100% higher SNR than partial DCT sensing matrices 50%-2400% higher SNR than cross-strip multiplexing, and 22%-210% higher SNR than Anger multiplexing for photoelectric events.</description><subject>Algorithms</subject><subject>Compressed sensing</subject><subject>Compressive sensing</subject><subject>Computer Simulation</subject><subject>Crystals</subject><subject>Detectors</subject><subject>image acquisition</subject><subject>Multiplexing</subject><subject>nuclear imaging</subject><subject>optimization</subject><subject>Positron emission tomography</subject><subject>positron emission tomography (PET)</subject><subject>Positron-Emission Tomography - instrumentation</subject><subject>Positron-Emission Tomography - methods</subject><subject>probabilistic and statistical method</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal-To-Noise Ratio</subject><subject>Vectors</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNo9kE1PwkAURSdGI4juTUxMl26K89mZWRpAJYFooAt3k-n0gTWF1pnWyL-3BGT1Fvfcm7yD0C3BQ0Kwfkzn0yHFhA0p5QlR9Az1iRAqpoJ_nKM-plLFGCe0h65C-MKYcIH1JepRxqVgXPfReFlbHyBaFuutLaMFuOoH_C6aQ_NZ5SFaVT6at2VT1CX8Ftt19D5JozE04JouWYDNq7a5RhcrWwa4Od4BSp8n6eg1nr29TEdPs9gxopvYEpflkDmeKFBc0O64LGFOWeoy6QBLLIBIcFoSJ0DLzFKZOJVZyC3hbIAeDrO1r75bCI3ZFMFBWdotVG0wpPuIaaUJ61B8QJ2vQvCwMrUvNtbvDMFmr8506sxenTmq6yr3x_U220B-Kvy76oC7A1AAwClOuCSUSfYHtONyug</recordid><startdate>20130501</startdate><enddate>20130501</enddate><creator>Chinn, G.</creator><creator>Olcott, P. 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S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-a1cbdebc468e845268ecb63c8a2cb7ce0705e17ec971c5e97ba276c8baeda143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Compressed sensing</topic><topic>Compressive sensing</topic><topic>Computer Simulation</topic><topic>Crystals</topic><topic>Detectors</topic><topic>image acquisition</topic><topic>Multiplexing</topic><topic>nuclear imaging</topic><topic>optimization</topic><topic>Positron emission tomography</topic><topic>positron emission tomography (PET)</topic><topic>Positron-Emission Tomography - instrumentation</topic><topic>Positron-Emission Tomography - methods</topic><topic>probabilistic and statistical method</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Signal-To-Noise Ratio</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Chinn, G.</creatorcontrib><creatorcontrib>Olcott, P. D.</creatorcontrib><creatorcontrib>Levin, C. S.</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><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chinn, G.</au><au>Olcott, P. D.</au><au>Levin, C. S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sparse Signal Recovery Methods for Multiplexing PET Detector Readout</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2013-05-01</date><risdate>2013</risdate><volume>32</volume><issue>5</issue><spage>932</spage><epage>942</epage><pages>932-942</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>Nuclear medicine imaging detectors are commonly multiplexed to reduce the number of readout channels. Because the underlying detector signals have a sparse representation, sparse recovery methods such as compressed sensing may be used to develop new multiplexing schemes. Random methods may be used to create sensing matrices that satisfy the restricted isometry property. However, the restricted isometry property provides little guidance for developing multiplexing networks with good signal-to-noise recovery capability. In this work, we describe compressed sensing using a maximum likelihood framework and develop a new method for constructing multiplexing (sensing) matrices that can recover signals more accurately in a mean square error sense compared to sensing matrices constructed by random construction methods. Signals can then be recovered by maximum likelihood estimation constrained to the support recovered by either greedy ℓ 0 iterative algorithms or ℓ 1 -norm minimization techniques. We show that this new method for constructing and decoding sensing matrices recovers signals with 4%-110% higher SNR than random Gaussian sensing matrices, up to 100% higher SNR than partial DCT sensing matrices 50%-2400% higher SNR than cross-strip multiplexing, and 22%-210% higher SNR than Anger multiplexing for photoelectric events.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>23475349</pmid><doi>10.1109/TMI.2013.2246182</doi></addata></record> |