Dynamic magnetic resonance inverse imaging of human brain function
MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel recons...
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Veröffentlicht in: | Magnetic resonance in medicine 2006-10, Vol.56 (4), p.787-802 |
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creator | Lin, Fa-Hsuan Wald, Lawrence L. Ahlfors, Seppo P. Hämäläinen, Matti S. Kwong, Kenneth K. Belliveau, John W. |
description | MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20‐ms temporal resolution in a visual stimulation experiment using a 90‐channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade‐off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc. |
doi_str_mv | 10.1002/mrm.20997 |
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In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20‐ms temporal resolution in a visual stimulation experiment using a 90‐channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade‐off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.</description><identifier>ISSN: 0740-3194</identifier><identifier>EISSN: 1522-2594</identifier><identifier>DOI: 10.1002/mrm.20997</identifier><identifier>PMID: 16964616</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Brain Mapping - methods ; Computer Simulation ; dSPM ; electroencephalgraphy ; Electroencephalography ; Humans ; Image Enhancement - methods ; Image Processing, Computer-Assisted - methods ; InI ; inverse ; Magnetic Resonance Imaging - methods ; Magnetoencephalography ; minimum-norm ; MNE ; MRI ; parallel MRI</subject><ispartof>Magnetic resonance in medicine, 2006-10, Vol.56 (4), p.787-802</ispartof><rights>Copyright © 2006 Wiley‐Liss, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4937-fc24494d8befcec6e72c1e1ea5050106c67b409a743f84b4acd4ea733ae96da53</citedby><cites>FETCH-LOGICAL-c4937-fc24494d8befcec6e72c1e1ea5050106c67b409a743f84b4acd4ea733ae96da53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fmrm.20997$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmrm.20997$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16964616$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Fa-Hsuan</creatorcontrib><creatorcontrib>Wald, Lawrence L.</creatorcontrib><creatorcontrib>Ahlfors, Seppo P.</creatorcontrib><creatorcontrib>Hämäläinen, Matti S.</creatorcontrib><creatorcontrib>Kwong, Kenneth K.</creatorcontrib><creatorcontrib>Belliveau, John W.</creatorcontrib><title>Dynamic magnetic resonance inverse imaging of human brain function</title><title>Magnetic resonance in medicine</title><addtitle>Magn. Reson. Med</addtitle><description>MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. The InI approach was inspired by electroencephalography (EEG) and magnetoencephalography (MEG) source localization techniques. Dynamic MR InI was evaluated by means of numerical simulations. InI was also applied to measure BOLD hemodynamic time curves at 20‐ms temporal resolution in a visual stimulation experiment using a 90‐channel head array. InI is expected to improve the time resolution of MRI and provide increased flexibility in the trade‐off between spatial and temporal resolution for studies of dynamic activation patterns in the human brain. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.</description><subject>Brain Mapping - methods</subject><subject>Computer Simulation</subject><subject>dSPM</subject><subject>electroencephalgraphy</subject><subject>Electroencephalography</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>InI</subject><subject>inverse</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Magnetoencephalography</subject><subject>minimum-norm</subject><subject>MNE</subject><subject>MRI</subject><subject>parallel MRI</subject><issn>0740-3194</issn><issn>1522-2594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkEFPwjAUxxujEUQPfgGzk4mHQbt27XpUVDQBNETDsenKG05Zh-2m8u0dgnoynt5L3u__T94PoWOCuwTjqFe4ohthKcUOapM4isIolmwXtbFgOKREshY68P4Z4zXD9lGLcMkZJ7yNLi5XVhe5CQo9t1A1iwNfWm0NBLl9A-eb2dxyOw_KLHiqC22D1OncBlltTZWX9hDtZXrh4Wg7O-jx-uqhfxMO7wa3_fNhaJikIsxMxJhksySFzIDhICJDgICOcYwJ5oaLlGGpBaNZwlKmzYyBFpRqkHymY9pBp5vepStfa_CVKnJvYLHQFsraK55ITLmU_4IRTjhmnDbg2QY0rvTeQaaWrnnWrRTBam1WNWbVl9mGPdmW1mkBs19yq7IBehvgPV_A6u8mNZqMvivDTSL3FXz8JLR7UVxQEavpeKCm8n5yMU4SNaCfOBqSBw</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Lin, Fa-Hsuan</creator><creator>Wald, Lawrence L.</creator><creator>Ahlfors, Seppo P.</creator><creator>Hämäläinen, Matti S.</creator><creator>Kwong, Kenneth K.</creator><creator>Belliveau, John W.</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><scope>BSCLL</scope><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>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>200610</creationdate><title>Dynamic magnetic resonance inverse imaging of human brain function</title><author>Lin, Fa-Hsuan ; Wald, Lawrence L. ; Ahlfors, Seppo P. ; Hämäläinen, Matti S. ; Kwong, Kenneth K. ; Belliveau, John W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4937-fc24494d8befcec6e72c1e1ea5050106c67b409a743f84b4acd4ea733ae96da53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Brain Mapping - methods</topic><topic>Computer Simulation</topic><topic>dSPM</topic><topic>electroencephalgraphy</topic><topic>Electroencephalography</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>InI</topic><topic>inverse</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Magnetoencephalography</topic><topic>minimum-norm</topic><topic>MNE</topic><topic>MRI</topic><topic>parallel MRI</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Fa-Hsuan</creatorcontrib><creatorcontrib>Wald, Lawrence L.</creatorcontrib><creatorcontrib>Ahlfors, Seppo P.</creatorcontrib><creatorcontrib>Hämäläinen, Matti S.</creatorcontrib><creatorcontrib>Kwong, Kenneth K.</creatorcontrib><creatorcontrib>Belliveau, John W.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Magnetic resonance in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Fa-Hsuan</au><au>Wald, Lawrence L.</au><au>Ahlfors, Seppo P.</au><au>Hämäläinen, Matti S.</au><au>Kwong, Kenneth K.</au><au>Belliveau, John W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic magnetic resonance inverse imaging of human brain function</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn. Reson. Med</addtitle><date>2006-10</date><risdate>2006</risdate><volume>56</volume><issue>4</issue><spage>787</spage><epage>802</epage><pages>787-802</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>MRI is widely used for noninvasive hemodynamic‐based functional brain imaging. In traditional spatial encoding, however, gradient switching limits the temporal resolution, which makes it difficult to unambiguously identify possible fast nonhemodynamic changes. In this paper we propose a novel reconstruction approach, called dynamic inverse imaging (InI), that is capable of providing millisecond temporal resolution when highly parallel detection is used. To achieve an order‐of‐magnitude speedup in generating time‐resolved contrast estimates and dynamic statistical parametric maps (dSPMs), the spatial information is derived from an array of detectors rather than by time‐consuming gradient‐encoding methods. 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subjects | Brain Mapping - methods Computer Simulation dSPM electroencephalgraphy Electroencephalography Humans Image Enhancement - methods Image Processing, Computer-Assisted - methods InI inverse Magnetic Resonance Imaging - methods Magnetoencephalography minimum-norm MNE MRI parallel MRI |
title | Dynamic magnetic resonance inverse imaging of human brain function |
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