Optimal sampling geometries for TV-norm reconstruction of fMRI data
This study explores the ability to reconstruct functional magnetic resonance imaging (fMRI) brain slices from a limited number of K-space samples. We use compressed sensing methods to reconstruct brain imaging activity using different K-space sampling geometries. To determine the optimal sampling ge...
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| creator | Jeromin, O.M. Calhoun, V.D. Pattichis, M.S. |
| description | This study explores the ability to reconstruct functional magnetic resonance imaging (fMRI) brain slices from a limited number of K-space samples. We use compressed sensing methods to reconstruct brain imaging activity using different K-space sampling geometries. To determine the optimal sampling geometry, we compute the reconstruction error. Here, for each geometry, we also estimate the optimal weighting parameters for the total variation (TV) norm and L-2 norm penalty functions. Initial results show that the optimal sampling geometry varies significantly as a function of the required reduction in K-space sampling density (for 60% to 90% reduction). Furthermore, the reconstructed fMRI slices can be used to accurately detect regions of neural activity from a largely reduced number of K-space samples. |
| doi_str_mv | 10.1109/ACSSC.2008.5074649 |
| format | Conference Proceeding |
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Furthermore, the reconstructed fMRI slices can be used to accurately detect regions of neural activity from a largely reduced number of K-space samples.</description><subject>Brain</subject><subject>Compressed sensing</subject><subject>Finite difference methods</subject><subject>Geometry</subject><subject>Image coding</subject><subject>Image reconstruction</subject><subject>Magnetic resonance imaging</subject><subject>Sampling methods</subject><subject>Wavelet domain</subject><issn>1058-6393</issn><issn>2576-2303</issn><isbn>9781424429400</isbn><isbn>1424429404</isbn><isbn>1424429412</isbn><isbn>9781424429417</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UNtKw0AUXG9grPkBfdkfSDx7SXb3sQSrhUrBVl_LmpxTIrmxiQ_-vRHrvMzADMMwjN0JSIUA97AsdrsilQA2zcDoXLszdiO01Fo6LeQ5i2Rm8kQqUBcsdsb-ewCXLBKQ2SRXTl2zeBw_YYbOlHU2YsV2mOrWN3z07dDU3ZEfsW9xCjWOnPrA9-9J14eWByz7bpzCVznVfcd74vTyuuaVn_wtuyLfjBifeMHeVo_74jnZbJ_WxXKT1MJkU1JK0sYajzIrc6qEJUcgFWmsqPyQJBA9ePurvTK6dCQqUOj0nPeUK7Vg93-9NSIehjDvDt-H0x_qB6pwUJU</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Jeromin, O.M.</creator><creator>Calhoun, V.D.</creator><creator>Pattichis, M.S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200810</creationdate><title>Optimal sampling geometries for TV-norm reconstruction of fMRI data</title><author>Jeromin, O.M. ; Calhoun, V.D. ; Pattichis, M.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c2f4787ae25c6fd18f9f023f4edfcb2f1eea0a8fcb2a374c9f1d03e9425caf633</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Brain</topic><topic>Compressed sensing</topic><topic>Finite difference methods</topic><topic>Geometry</topic><topic>Image coding</topic><topic>Image reconstruction</topic><topic>Magnetic resonance imaging</topic><topic>Sampling methods</topic><topic>Wavelet domain</topic><toplevel>online_resources</toplevel><creatorcontrib>Jeromin, O.M.</creatorcontrib><creatorcontrib>Calhoun, V.D.</creatorcontrib><creatorcontrib>Pattichis, M.S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jeromin, O.M.</au><au>Calhoun, V.D.</au><au>Pattichis, M.S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Optimal sampling geometries for TV-norm reconstruction of fMRI data</atitle><btitle>2008 42nd Asilomar Conference on Signals, Systems and Computers</btitle><stitle>ACSSC</stitle><date>2008-10</date><risdate>2008</risdate><spage>1397</spage><epage>1401</epage><pages>1397-1401</pages><issn>1058-6393</issn><eissn>2576-2303</eissn><isbn>9781424429400</isbn><isbn>1424429404</isbn><eisbn>1424429412</eisbn><eisbn>9781424429417</eisbn><abstract>This study explores the ability to reconstruct functional magnetic resonance imaging (fMRI) brain slices from a limited number of K-space samples. 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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Brain Compressed sensing Finite difference methods Geometry Image coding Image reconstruction Magnetic resonance imaging Sampling methods Wavelet domain |
| title | Optimal sampling geometries for TV-norm reconstruction of fMRI data |
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