Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition

Purpose Quasi‐diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient,D1,2$$ {D}_{1,2} $$ in mm2 s−1 and a fractional exponent, α$$ \upalpha $$, defining the non‐Gaussianity of the...

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Veröffentlicht in:Magnetic resonance in medicine 2022-12, Vol.88 (6), p.2532-2547
Hauptverfasser: Spilling, Catherine A., Howe, Franklyn A., Barrick, Thomas R.
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Howe, Franklyn A.
Barrick, Thomas R.
description Purpose Quasi‐diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient,D1,2$$ {D}_{1,2} $$ in mm2 s−1 and a fractional exponent, α$$ \upalpha $$, defining the non‐Gaussianity of the diffusion signal decay. Here, the b‐value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi‐b‐value reference (MbR) dataset comprising 29 diffusion‐sensitized images arrayed between b=0$$ b=0 $$ and 5000 s mm−2. The effects of varying maximum b‐value (bmax$$ {b}_{\mathrm{max}} $$), number of b‐value shells, and the effects of Rician noise were investigated. Results QDTI measures showed bmax$$ {b}_{\mathrm{max}} $$ dependence, most significantly for α$$ \upalpha $$ in white matter, which monotonically decreased with higher bmax$$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b‐value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation ofD1,2$$ \kern0.50em {D}_{1,2} $$ and underestimation of α$$ \upalpha $$ in white matter, and overestimation of D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax=5000$$ {b}_{\mathrm{max}}=5000 $$ s mm−2, and 4 b‐value shells at bmax=3960$$ {b}_{\mathrm{max}}=3960 $$ s mm−2, providing minimal bias in D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ compared to the MbR. Conclusion A highly detailed optimization of non‐Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non‐Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.
doi_str_mv 10.1002/mrm.29420
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QDI provides estimates of the diffusion coefficient,D1,2$$ {D}_{1,2} $$ in mm2 s−1 and a fractional exponent, α$$ \upalpha $$, defining the non‐Gaussianity of the diffusion signal decay. Here, the b‐value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi‐b‐value reference (MbR) dataset comprising 29 diffusion‐sensitized images arrayed between b=0$$ b=0 $$ and 5000 s mm−2. The effects of varying maximum b‐value (bmax$$ {b}_{\mathrm{max}} $$), number of b‐value shells, and the effects of Rician noise were investigated. Results QDTI measures showed bmax$$ {b}_{\mathrm{max}} $$ dependence, most significantly for α$$ \upalpha $$ in white matter, which monotonically decreased with higher bmax$$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b‐value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation ofD1,2$$ \kern0.50em {D}_{1,2} $$ and underestimation of α$$ \upalpha $$ in white matter, and overestimation of D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax=5000$$ {b}_{\mathrm{max}}=5000 $$ s mm−2, and 4 b‐value shells at bmax=3960$$ {b}_{\mathrm{max}}=3960 $$ s mm−2, providing minimal bias in D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ compared to the MbR. Conclusion A highly detailed optimization of non‐Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non‐Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.</description><identifier>ISSN: 0740-3194</identifier><identifier>EISSN: 1522-2594</identifier><identifier>DOI: 10.1002/mrm.29420</identifier><identifier>PMID: 36054778</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Accuracy ; Anisotropy ; Brain - diagnostic imaging ; Brain - pathology ; Decay ; Diffusion ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; Estimates ; Humans ; Image Processing, Computer-Assisted - methods ; Imaging Methodology ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Medical imaging ; Neuroimaging ; Noise ; non‐Gaussian diffusion MRI ; Optimization ; Parameterization ; Quantitative analysis ; quasi‐diffusion MRI ; Random walk ; Reliability ; Reproducibility of Results ; Shells ; Substantia alba ; Tensors ; White Matter</subject><ispartof>Magnetic resonance in medicine, 2022-12, Vol.88 (6), p.2532-2547</ispartof><rights>2022 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2022. 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><cites>FETCH-LOGICAL-c4030-9f4870667385e916e3be45b108cf0f37987870f3ff56e0bca90f4c2d7f9a64493</cites><orcidid>0000-0002-7135-3351 ; 0000-0002-6908-5079 ; 0000-0003-1783-3650</orcidid></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.29420$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmrm.29420$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36054778$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Spilling, Catherine A.</creatorcontrib><creatorcontrib>Howe, Franklyn A.</creatorcontrib><creatorcontrib>Barrick, Thomas R.</creatorcontrib><title>Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition</title><title>Magnetic resonance in medicine</title><addtitle>Magn Reson Med</addtitle><description>Purpose Quasi‐diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient,D1,2$$ {D}_{1,2} $$ in mm2 s−1 and a fractional exponent, α$$ \upalpha $$, defining the non‐Gaussianity of the diffusion signal decay. Here, the b‐value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi‐b‐value reference (MbR) dataset comprising 29 diffusion‐sensitized images arrayed between b=0$$ b=0 $$ and 5000 s mm−2. The effects of varying maximum b‐value (bmax$$ {b}_{\mathrm{max}} $$), number of b‐value shells, and the effects of Rician noise were investigated. Results QDTI measures showed bmax$$ {b}_{\mathrm{max}} $$ dependence, most significantly for α$$ \upalpha $$ in white matter, which monotonically decreased with higher bmax$$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b‐value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation ofD1,2$$ \kern0.50em {D}_{1,2} $$ and underestimation of α$$ \upalpha $$ in white matter, and overestimation of D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax=5000$$ {b}_{\mathrm{max}}=5000 $$ s mm−2, and 4 b‐value shells at bmax=3960$$ {b}_{\mathrm{max}}=3960 $$ s mm−2, providing minimal bias in D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ compared to the MbR. Conclusion A highly detailed optimization of non‐Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non‐Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.</description><subject>Accuracy</subject><subject>Anisotropy</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - pathology</subject><subject>Decay</subject><subject>Diffusion</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Estimates</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Imaging Methodology</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medical imaging</subject><subject>Neuroimaging</subject><subject>Noise</subject><subject>non‐Gaussian diffusion MRI</subject><subject>Optimization</subject><subject>Parameterization</subject><subject>Quantitative analysis</subject><subject>quasi‐diffusion MRI</subject><subject>Random walk</subject><subject>Reliability</subject><subject>Reproducibility of Results</subject><subject>Shells</subject><subject>Substantia alba</subject><subject>Tensors</subject><subject>White Matter</subject><issn>0740-3194</issn><issn>1522-2594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kdFqFDEUhkNR7Hb1oi8gA72xF9OemSSTyU1Biq1CS0H0OmSzJ2vKTLKbzLSsVz6Cz9gnacatRQWvAjlfvvyHn5DDCk4qgPq0j_1JLVkNe2RW8bouay7ZCzIDwaCklWT75CClWwCQUrBXZJ82wJkQ7Yzc36wH17vvenDBF8EWm1En9_Dj59JZO6bpstcrj4MzRcQUvPYGC5fvnF8VNsTpgR_ckAV3WGhjxqjNttB-WWQxZhNa64xDP-TpZnTJTV-9Ji-t7hK-eTrn5OvFhy_nH8urm8tP5--vSsOAQiktawU0jaAtR1k1SBfI-KKC1liwVMhW5Lml1vIGYWG0BMtMvRRW6oYxSefkbOddj4selybHiLpT65hXiFsVtFN_T7z7plbhTskWGAeWBe-eBDFsRkyD6l0y2HXaYxiTqgVIQQXPEefk6B_0NozR5_UyVUnatryZhMc7ysSQUkT7HKYCNdWpcp3qV52Zfftn-mfyd38ZON0B967D7f9N6vrz9U75CNOArsQ</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Spilling, Catherine A.</creator><creator>Howe, Franklyn A.</creator><creator>Barrick, Thomas R.</creator><general>Wiley Subscription Services, Inc</general><general>John Wiley and Sons 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><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7135-3351</orcidid><orcidid>https://orcid.org/0000-0002-6908-5079</orcidid><orcidid>https://orcid.org/0000-0003-1783-3650</orcidid></search><sort><creationdate>202212</creationdate><title>Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition</title><author>Spilling, Catherine A. ; Howe, Franklyn A. ; Barrick, Thomas R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4030-9f4870667385e916e3be45b108cf0f37987870f3ff56e0bca90f4c2d7f9a64493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Anisotropy</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - pathology</topic><topic>Decay</topic><topic>Diffusion</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Estimates</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Imaging Methodology</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medical imaging</topic><topic>Neuroimaging</topic><topic>Noise</topic><topic>non‐Gaussian diffusion MRI</topic><topic>Optimization</topic><topic>Parameterization</topic><topic>Quantitative analysis</topic><topic>quasi‐diffusion MRI</topic><topic>Random walk</topic><topic>Reliability</topic><topic>Reproducibility of Results</topic><topic>Shells</topic><topic>Substantia alba</topic><topic>Tensors</topic><topic>White Matter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Spilling, Catherine A.</creatorcontrib><creatorcontrib>Howe, Franklyn A.</creatorcontrib><creatorcontrib>Barrick, Thomas R.</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley 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 &amp; Medical Complete (Alumni)</collection><collection>Biochemistry Abstracts 1</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Magnetic resonance in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Spilling, Catherine A.</au><au>Howe, Franklyn A.</au><au>Barrick, Thomas R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn Reson Med</addtitle><date>2022-12</date><risdate>2022</risdate><volume>88</volume><issue>6</issue><spage>2532</spage><epage>2547</epage><pages>2532-2547</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>Purpose Quasi‐diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient,D1,2$$ {D}_{1,2} $$ in mm2 s−1 and a fractional exponent, α$$ \upalpha $$, defining the non‐Gaussianity of the diffusion signal decay. Here, the b‐value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi‐b‐value reference (MbR) dataset comprising 29 diffusion‐sensitized images arrayed between b=0$$ b=0 $$ and 5000 s mm−2. The effects of varying maximum b‐value (bmax$$ {b}_{\mathrm{max}} $$), number of b‐value shells, and the effects of Rician noise were investigated. Results QDTI measures showed bmax$$ {b}_{\mathrm{max}} $$ dependence, most significantly for α$$ \upalpha $$ in white matter, which monotonically decreased with higher bmax$$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b‐value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation ofD1,2$$ \kern0.50em {D}_{1,2} $$ and underestimation of α$$ \upalpha $$ in white matter, and overestimation of D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax=5000$$ {b}_{\mathrm{max}}=5000 $$ s mm−2, and 4 b‐value shells at bmax=3960$$ {b}_{\mathrm{max}}=3960 $$ s mm−2, providing minimal bias in D1,2$$ {D}_{1,2} $$ and α$$ \upalpha $$ compared to the MbR. Conclusion A highly detailed optimization of non‐Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non‐Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>36054778</pmid><doi>10.1002/mrm.29420</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-7135-3351</orcidid><orcidid>https://orcid.org/0000-0002-6908-5079</orcidid><orcidid>https://orcid.org/0000-0003-1783-3650</orcidid><oa>free_for_read</oa></addata></record>
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subjects Accuracy
Anisotropy
Brain - diagnostic imaging
Brain - pathology
Decay
Diffusion
Diffusion coefficient
Diffusion Magnetic Resonance Imaging - methods
Estimates
Humans
Image Processing, Computer-Assisted - methods
Imaging Methodology
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Neuroimaging
Noise
non‐Gaussian diffusion MRI
Optimization
Parameterization
Quantitative analysis
quasi‐diffusion MRI
Random walk
Reliability
Reproducibility of Results
Shells
Substantia alba
Tensors
White Matter
title Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition
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