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Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain
Purpose Slice‐wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher‐order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, λ. Theor...
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Published in: | Magnetic resonance in medicine 2022-03, Vol.87 (3), p.1218-1230 |
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container_title | Magnetic resonance in medicine |
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creator | Shi, Yuhang Clare, Stuart Vannesjo, Signe Johanna |
description | Purpose
Slice‐wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher‐order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, λ.
Theory and Methods
λ was selected for each slice separately in a fully automatic procedure based on a combination of boundary constraints and an L‐curve search algorithm. Shimming performance was evaluated for second order slice‐wise shimming of the brain at 7T, by simulation on a database of field maps from 143 subjects, and by direct measurements in 8 subjects.
Results
Simulations yielded on average 36% reduction in the shim current norm for just 0.4 Hz increase in residual field SD as compared to unconstrained unregularized optimization. In vivo results yielded on average 34.0 Hz residual field SD as compared to 34.3 Hz with a constrained unregularized optimization, while simultaneously reducing the shim current norm to 2.8 A from 3.9 A. The proposed regularization also reduced the average step in the shim current between slices.
Conclusion
Slice‐wise variable Tikhonov regularization yielded reduced current norm and current steps to a negligible cost in field inhomogeneity. The method holds promise to increase the robustness, and thereby the utility, of higher‐order shim updating. |
doi_str_mv | 10.1002/mrm.28951 |
format | article |
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Slice‐wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher‐order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, λ.
Theory and Methods
λ was selected for each slice separately in a fully automatic procedure based on a combination of boundary constraints and an L‐curve search algorithm. Shimming performance was evaluated for second order slice‐wise shimming of the brain at 7T, by simulation on a database of field maps from 143 subjects, and by direct measurements in 8 subjects.
Results
Simulations yielded on average 36% reduction in the shim current norm for just 0.4 Hz increase in residual field SD as compared to unconstrained unregularized optimization. In vivo results yielded on average 34.0 Hz residual field SD as compared to 34.3 Hz with a constrained unregularized optimization, while simultaneously reducing the shim current norm to 2.8 A from 3.9 A. The proposed regularization also reduced the average step in the shim current between slices.
Conclusion
Slice‐wise variable Tikhonov regularization yielded reduced current norm and current steps to a negligible cost in field inhomogeneity. The method holds promise to increase the robustness, and thereby the utility, of higher‐order shim updating.</description><identifier>ISSN: 0740-3194</identifier><identifier>EISSN: 1522-2594</identifier><identifier>DOI: 10.1002/mrm.28951</identifier><identifier>PMID: 34783374</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Brain ; Brain - diagnostic imaging ; Brain Mapping ; Brain slice preparation ; Constraints ; dynamic B0 shimming ; higher‐order shimming ; Homogeneity ; Humans ; Image Processing, Computer-Assisted ; Inhomogeneity ; Magnetic Resonance Imaging ; Mathematical analysis ; Noise propagation ; Optimization ; Regularization ; Search algorithms ; shim optimization</subject><ispartof>Magnetic resonance in medicine, 2022-03, Vol.87 (3), p.1218-1230</ispartof><rights>2021 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine</rights><rights>2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3881-357b8e7ac3851ce4c973399fca69c82810dbe0dffaaf2f56e8d26850f94033973</citedby><cites>FETCH-LOGICAL-c3881-357b8e7ac3851ce4c973399fca69c82810dbe0dffaaf2f56e8d26850f94033973</cites><orcidid>0000-0003-2432-4192 ; 0000-0001-8852-6100</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34783374$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Yuhang</creatorcontrib><creatorcontrib>Clare, Stuart</creatorcontrib><creatorcontrib>Vannesjo, Signe Johanna</creatorcontrib><title>Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain</title><title>Magnetic resonance in medicine</title><addtitle>Magn Reson Med</addtitle><description>Purpose
Slice‐wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher‐order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, λ.
Theory and Methods
λ was selected for each slice separately in a fully automatic procedure based on a combination of boundary constraints and an L‐curve search algorithm. Shimming performance was evaluated for second order slice‐wise shimming of the brain at 7T, by simulation on a database of field maps from 143 subjects, and by direct measurements in 8 subjects.
Results
Simulations yielded on average 36% reduction in the shim current norm for just 0.4 Hz increase in residual field SD as compared to unconstrained unregularized optimization. In vivo results yielded on average 34.0 Hz residual field SD as compared to 34.3 Hz with a constrained unregularized optimization, while simultaneously reducing the shim current norm to 2.8 A from 3.9 A. The proposed regularization also reduced the average step in the shim current between slices.
Conclusion
Slice‐wise variable Tikhonov regularization yielded reduced current norm and current steps to a negligible cost in field inhomogeneity. The method holds promise to increase the robustness, and thereby the utility, of higher‐order shim updating.</description><subject>Algorithms</subject><subject>Brain</subject><subject>Brain - diagnostic imaging</subject><subject>Brain Mapping</subject><subject>Brain slice preparation</subject><subject>Constraints</subject><subject>dynamic B0 shimming</subject><subject>higher‐order shimming</subject><subject>Homogeneity</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Inhomogeneity</subject><subject>Magnetic Resonance Imaging</subject><subject>Mathematical analysis</subject><subject>Noise propagation</subject><subject>Optimization</subject><subject>Regularization</subject><subject>Search algorithms</subject><subject>shim optimization</subject><issn>0740-3194</issn><issn>1522-2594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp1kc1u1DAUhS1ERYfCghdAltjAIq3_EtvsqoqfSq2QoKwtj3PdcUniYCeMyoonQDwjT4KnGbpAYmVf-bvn-N6D0DNKjikh7KRP_TFTuqYP0IrWjFWs1uIhWhEpSMWpFofocc43hBCtpXiEDrmQinMpVujnp03ocRyn0IfvdgpxwNswbXCC6909ehyGCRLk6fePX3kEF3xw-Cp82cQhftthc2fTvvU1Ph3HLrhFZ4o4g4tDWzpjaiHhXN6gVNuQAedi3IfheucxbQCvkw3DE3TgbZfh6f48Qp_fvrk6e19dfHh3fnZ6UTmuFK14LdcKpC1VTR0IpyXnWntnG-0UU5S0ayCt99Z65usGVMsaVROvBSmg5Efo5aI7pvh1LtOZPmQHXWcHiHM2ZYGKSCVYU9AX_6A3cU5D-Z1hDaO0uPG6UK8WyqWYcwJvxhR6m24NJWYXkikhmbuQCvt8rzive2jvyb-pFOBkAbahg9v_K5nLj5eL5B9PQqE1</recordid><startdate>202203</startdate><enddate>202203</enddate><creator>Shi, Yuhang</creator><creator>Clare, Stuart</creator><creator>Vannesjo, Signe Johanna</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</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>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>M7Z</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2432-4192</orcidid><orcidid>https://orcid.org/0000-0001-8852-6100</orcidid></search><sort><creationdate>202203</creationdate><title>Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain</title><author>Shi, Yuhang ; Clare, Stuart ; Vannesjo, Signe Johanna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3881-357b8e7ac3851ce4c973399fca69c82810dbe0dffaaf2f56e8d26850f94033973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain Mapping</topic><topic>Brain slice preparation</topic><topic>Constraints</topic><topic>dynamic B0 shimming</topic><topic>higher‐order shimming</topic><topic>Homogeneity</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Inhomogeneity</topic><topic>Magnetic Resonance Imaging</topic><topic>Mathematical analysis</topic><topic>Noise propagation</topic><topic>Optimization</topic><topic>Regularization</topic><topic>Search algorithms</topic><topic>shim optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Yuhang</creatorcontrib><creatorcontrib>Clare, Stuart</creatorcontrib><creatorcontrib>Vannesjo, Signe Johanna</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biochemistry Abstracts 1</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>Shi, Yuhang</au><au>Clare, Stuart</au><au>Vannesjo, Signe Johanna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn Reson Med</addtitle><date>2022-03</date><risdate>2022</risdate><volume>87</volume><issue>3</issue><spage>1218</spage><epage>1230</epage><pages>1218-1230</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>Purpose
Slice‐wise shimming can improve field homogeneity, but suffers from large noise propagation in the shim calculation. Here, we propose a robust shim current optimization for higher‐order dynamic shim updating, based on Tikhonov regularization with a variable regularization parameter, λ.
Theory and Methods
λ was selected for each slice separately in a fully automatic procedure based on a combination of boundary constraints and an L‐curve search algorithm. Shimming performance was evaluated for second order slice‐wise shimming of the brain at 7T, by simulation on a database of field maps from 143 subjects, and by direct measurements in 8 subjects.
Results
Simulations yielded on average 36% reduction in the shim current norm for just 0.4 Hz increase in residual field SD as compared to unconstrained unregularized optimization. In vivo results yielded on average 34.0 Hz residual field SD as compared to 34.3 Hz with a constrained unregularized optimization, while simultaneously reducing the shim current norm to 2.8 A from 3.9 A. The proposed regularization also reduced the average step in the shim current between slices.
Conclusion
Slice‐wise variable Tikhonov regularization yielded reduced current norm and current steps to a negligible cost in field inhomogeneity. The method holds promise to increase the robustness, and thereby the utility, of higher‐order shim updating.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>34783374</pmid><doi>10.1002/mrm.28951</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-2432-4192</orcidid><orcidid>https://orcid.org/0000-0001-8852-6100</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Brain Brain - diagnostic imaging Brain Mapping Brain slice preparation Constraints dynamic B0 shimming higher‐order shimming Homogeneity Humans Image Processing, Computer-Assisted Inhomogeneity Magnetic Resonance Imaging Mathematical analysis Noise propagation Optimization Regularization Search algorithms shim optimization |
title | Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain |
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