Prospective motion correction in functional MRI
Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysi...
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Veröffentlicht in: | NeuroImage (Orlando, Fla.) Fla.), 2017-07, Vol.154, p.33-42 |
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description | Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging. |
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Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. 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All rights reserved.</rights><rights>Copyright Elsevier Limited Jul 1, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-e311d6ebd1e47034ec2dfe05852d07772b8d8f67d1d974868215d8fb0a25eac43</citedby><cites>FETCH-LOGICAL-c507t-e311d6ebd1e47034ec2dfe05852d07772b8d8f67d1d974868215d8fb0a25eac43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1912668634?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,3548,27923,27924,45994,64384,64386,64388,72240</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27845256$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zaitsev, Maxim</creatorcontrib><creatorcontrib>Akin, Burak</creatorcontrib><creatorcontrib>LeVan, Pierre</creatorcontrib><creatorcontrib>Knowles, Benjamin R.</creatorcontrib><title>Prospective motion correction in functional MRI</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging.</description><subject>Adaptive dynamic MRI</subject><subject>Data processing</subject><subject>fMRI</subject><subject>Fourier transforms</subject><subject>Function MRI</subject><subject>Functional magnetic resonance imaging</subject><subject>Functional Neuroimaging - methods</subject><subject>Functional Neuroimaging - standards</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image Processing, Computer-Assisted - standards</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Magnetic Resonance Imaging - standards</subject><subject>Motion correction</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Physiology</subject><subject>Prospective motion correction</subject><subject>Real-time motion correction</subject><subject>Scanning</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFUU1P3DAUtFBRWbb9CyhSL70k-Nlx7Fwqtah8SItAiJ6trP1CvcrGWztZqf8eh4UtcOnJo_G8j3lDSAa0AArV6arocQzerZsHLFhiCoCCQnlAZkBrkddCsg8TFjxXAPUROY5xRSmtoVQfyRGTqhRMVDNyeht83KAZ3BaztR-c7zPjQ5iYBF2ftWP_hJsuu767-kQO26aL-Pn5nZNf5z_vzy7zxc3F1dn3RW4ElUOOHMBWuLSApaS8RMNsi1QowSyVUrKlsqqtpAVby1JVioFIxJI2TGBjSj4n33Z9N-NyjdZgP4Sm05uQPIe_2jdOv_3p3W_94LdalExSylODr88Ngv8zYhz02kWDXdf06MeoQfFacsk5TdIv76QrP4ZkOKlqYFWlKj5tpHYqky4WA7b7ZYDqKRW90v9S0VMqGkCnVFLpyWsz-8KXGJLgx06A6aRbh0FH47A3aN2UhLbe_X_KI5sqoyg</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Zaitsev, Maxim</creator><creator>Akin, Burak</creator><creator>LeVan, Pierre</creator><creator>Knowles, Benjamin R.</creator><general>Elsevier Inc</general><general>Elsevier Limited</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>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170701</creationdate><title>Prospective motion correction in functional MRI</title><author>Zaitsev, Maxim ; 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subjects | Adaptive dynamic MRI Data processing fMRI Fourier transforms Function MRI Functional magnetic resonance imaging Functional Neuroimaging - methods Functional Neuroimaging - standards Humans Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - standards Motion correction NMR Nuclear magnetic resonance Physiology Prospective motion correction Real-time motion correction Scanning Sensitivity Statistical analysis |
title | Prospective motion correction in functional MRI |
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