T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms
Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T of the myocytic component (T ). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects o...
Gespeichert in:
Veröffentlicht in: | Magnetic resonance in medicine 2020-11, Vol.84 (5), p.2656-2670 |
---|---|
Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2670 |
---|---|
container_issue | 5 |
container_start_page | 2656 |
container_title | Magnetic resonance in medicine |
container_volume | 84 |
creator | Keene, Kevin R Beenakker, Jan-Willem M Hooijmans, Melissa T Naarding, Karin J Niks, Erik H Otto, Louise A M van der Pol, W Ludo Tannemaat, Martijn R Kan, Hermien E Froeling, Martijn |
description | Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T
of the myocytic component (T
). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects of inaccuracies in the T
calibration remain unexplored. Hence, we aimed to improve the performance of extended phase graph fitting methods over a large range of fat fractions, by including the slice-selection flip angle profile, a through-plane chemical-shift displacement correction, and optimized calibration of T
.
Simulation experiments were used to study the influence of the slice flip-angle profile with chemical-shift and T
estimations. Next, in vivo data from four neuromuscular disease cohorts were studied for different T
calibration methods and T
estimations.
Excluding slice flip-angle profiles or chemical-shift displacement resulted in a bias in T
up to 10 ms. Furthermore, a wrongly calibrated T
caused a bias of up to 4 ms in T
. For the in vivo data, one-component calibration led to a lower T
compared with a two-component method, and T
decreased with increasing fat fractions.
In vivo data showed a decline in T
for increasing fat fractions, which has important implications for clinical studies, especially in multicenter settings. We recommend using an extended phase graph-based model for fitting T
from MSE sequences with two-component T
calibration. Moreover, we recommend including the slice flip-angle profile in the model with correction for through-plane chemical-shift displacements. |
doi_str_mv | 10.1002/mrm.28290 |
format | Article |
fullrecord | <record><control><sourceid>pubmed_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1002_mrm_28290</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>32306450</sourcerecordid><originalsourceid>FETCH-LOGICAL-c182t-1700ff91803f583fed38df3f9218d1077ae873fbb7d582c12a173581b6d11ee33</originalsourceid><addsrcrecordid>eNo90L1OwzAUhmELgWgpDNwA8sqQcmwntT2iij-pEkuZIyc-bgxxGtmp1N49KQWmM5xH3_AScstgzgD4Q4hhzhXXcEamrOA844XOz8kUZA6ZYDqfkKuUPgFAa5lfkongAhZ5AVPi15TTiK3Zm8Fvu2zwAWkwfe-7DfUdbdC0Q3OgprPU-oQmoaXpC1scTEvDLtUt0l06atwP2Nnx3Tejopto-oaadrONfmhCuiYXzrQJb37vjHw8P62Xr9nq_eVt-bjKaqb4kDEJ4JxmCoQrlHBohbJOOM2ZsgykNKikcFUlbaF4zbhhUhSKVQvLGKIQM3J_2q3jNqWIruyjDyYeSgblMVc55ip_co327mT7XRXQ_su_PuIb77FmrQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Keene, Kevin R ; Beenakker, Jan-Willem M ; Hooijmans, Melissa T ; Naarding, Karin J ; Niks, Erik H ; Otto, Louise A M ; van der Pol, W Ludo ; Tannemaat, Martijn R ; Kan, Hermien E ; Froeling, Martijn</creator><creatorcontrib>Keene, Kevin R ; Beenakker, Jan-Willem M ; Hooijmans, Melissa T ; Naarding, Karin J ; Niks, Erik H ; Otto, Louise A M ; van der Pol, W Ludo ; Tannemaat, Martijn R ; Kan, Hermien E ; Froeling, Martijn</creatorcontrib><description>Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T
of the myocytic component (T
). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects of inaccuracies in the T
calibration remain unexplored. Hence, we aimed to improve the performance of extended phase graph fitting methods over a large range of fat fractions, by including the slice-selection flip angle profile, a through-plane chemical-shift displacement correction, and optimized calibration of T
.
Simulation experiments were used to study the influence of the slice flip-angle profile with chemical-shift and T
estimations. Next, in vivo data from four neuromuscular disease cohorts were studied for different T
calibration methods and T
estimations.
Excluding slice flip-angle profiles or chemical-shift displacement resulted in a bias in T
up to 10 ms. Furthermore, a wrongly calibrated T
caused a bias of up to 4 ms in T
. For the in vivo data, one-component calibration led to a lower T
compared with a two-component method, and T
decreased with increasing fat fractions.
In vivo data showed a decline in T
for increasing fat fractions, which has important implications for clinical studies, especially in multicenter settings. We recommend using an extended phase graph-based model for fitting T
from MSE sequences with two-component T
calibration. Moreover, we recommend including the slice flip-angle profile in the model with correction for through-plane chemical-shift displacements.</description><identifier>ISSN: 0740-3194</identifier><identifier>EISSN: 1522-2594</identifier><identifier>DOI: 10.1002/mrm.28290</identifier><identifier>PMID: 32306450</identifier><language>eng</language><publisher>United States</publisher><subject>Algorithms ; Calibration ; Computer Simulation ; Magnetic Resonance Imaging ; Muscle, Skeletal - diagnostic imaging ; Phantoms, Imaging</subject><ispartof>Magnetic resonance in medicine, 2020-11, Vol.84 (5), p.2656-2670</ispartof><rights>2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c182t-1700ff91803f583fed38df3f9218d1077ae873fbb7d582c12a173581b6d11ee33</citedby><cites>FETCH-LOGICAL-c182t-1700ff91803f583fed38df3f9218d1077ae873fbb7d582c12a173581b6d11ee33</cites><orcidid>0000-0001-9300-9888 ; 0000-0003-2929-0390 ; 0000-0002-5772-7177 ; 0000-0001-5022-3745 ; 0000-0003-2998-4683 ; 0000-0001-5892-5143 ; 0000-0003-3841-0497 ; 0000-0002-8970-2740 ; 0000-0003-0479-5587 ; 0000-0002-2233-1383</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32306450$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Keene, Kevin R</creatorcontrib><creatorcontrib>Beenakker, Jan-Willem M</creatorcontrib><creatorcontrib>Hooijmans, Melissa T</creatorcontrib><creatorcontrib>Naarding, Karin J</creatorcontrib><creatorcontrib>Niks, Erik H</creatorcontrib><creatorcontrib>Otto, Louise A M</creatorcontrib><creatorcontrib>van der Pol, W Ludo</creatorcontrib><creatorcontrib>Tannemaat, Martijn R</creatorcontrib><creatorcontrib>Kan, Hermien E</creatorcontrib><creatorcontrib>Froeling, Martijn</creatorcontrib><title>T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms</title><title>Magnetic resonance in medicine</title><addtitle>Magn Reson Med</addtitle><description>Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T
of the myocytic component (T
). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects of inaccuracies in the T
calibration remain unexplored. Hence, we aimed to improve the performance of extended phase graph fitting methods over a large range of fat fractions, by including the slice-selection flip angle profile, a through-plane chemical-shift displacement correction, and optimized calibration of T
.
Simulation experiments were used to study the influence of the slice flip-angle profile with chemical-shift and T
estimations. Next, in vivo data from four neuromuscular disease cohorts were studied for different T
calibration methods and T
estimations.
Excluding slice flip-angle profiles or chemical-shift displacement resulted in a bias in T
up to 10 ms. Furthermore, a wrongly calibrated T
caused a bias of up to 4 ms in T
. For the in vivo data, one-component calibration led to a lower T
compared with a two-component method, and T
decreased with increasing fat fractions.
In vivo data showed a decline in T
for increasing fat fractions, which has important implications for clinical studies, especially in multicenter settings. We recommend using an extended phase graph-based model for fitting T
from MSE sequences with two-component T
calibration. Moreover, we recommend including the slice flip-angle profile in the model with correction for through-plane chemical-shift displacements.</description><subject>Algorithms</subject><subject>Calibration</subject><subject>Computer Simulation</subject><subject>Magnetic Resonance Imaging</subject><subject>Muscle, Skeletal - diagnostic imaging</subject><subject>Phantoms, Imaging</subject><issn>0740-3194</issn><issn>1522-2594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo90L1OwzAUhmELgWgpDNwA8sqQcmwntT2iij-pEkuZIyc-bgxxGtmp1N49KQWmM5xH3_AScstgzgD4Q4hhzhXXcEamrOA844XOz8kUZA6ZYDqfkKuUPgFAa5lfkongAhZ5AVPi15TTiK3Zm8Fvu2zwAWkwfe-7DfUdbdC0Q3OgprPU-oQmoaXpC1scTEvDLtUt0l06atwP2Nnx3Tejopto-oaadrONfmhCuiYXzrQJb37vjHw8P62Xr9nq_eVt-bjKaqb4kDEJ4JxmCoQrlHBohbJOOM2ZsgykNKikcFUlbaF4zbhhUhSKVQvLGKIQM3J_2q3jNqWIruyjDyYeSgblMVc55ip_co327mT7XRXQ_su_PuIb77FmrQ</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Keene, Kevin R</creator><creator>Beenakker, Jan-Willem M</creator><creator>Hooijmans, Melissa T</creator><creator>Naarding, Karin J</creator><creator>Niks, Erik H</creator><creator>Otto, Louise A M</creator><creator>van der Pol, W Ludo</creator><creator>Tannemaat, Martijn R</creator><creator>Kan, Hermien E</creator><creator>Froeling, Martijn</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-9300-9888</orcidid><orcidid>https://orcid.org/0000-0003-2929-0390</orcidid><orcidid>https://orcid.org/0000-0002-5772-7177</orcidid><orcidid>https://orcid.org/0000-0001-5022-3745</orcidid><orcidid>https://orcid.org/0000-0003-2998-4683</orcidid><orcidid>https://orcid.org/0000-0001-5892-5143</orcidid><orcidid>https://orcid.org/0000-0003-3841-0497</orcidid><orcidid>https://orcid.org/0000-0002-8970-2740</orcidid><orcidid>https://orcid.org/0000-0003-0479-5587</orcidid><orcidid>https://orcid.org/0000-0002-2233-1383</orcidid></search><sort><creationdate>202011</creationdate><title>T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms</title><author>Keene, Kevin R ; Beenakker, Jan-Willem M ; Hooijmans, Melissa T ; Naarding, Karin J ; Niks, Erik H ; Otto, Louise A M ; van der Pol, W Ludo ; Tannemaat, Martijn R ; Kan, Hermien E ; Froeling, Martijn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c182t-1700ff91803f583fed38df3f9218d1077ae873fbb7d582c12a173581b6d11ee33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Computer Simulation</topic><topic>Magnetic Resonance Imaging</topic><topic>Muscle, Skeletal - diagnostic imaging</topic><topic>Phantoms, Imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Keene, Kevin R</creatorcontrib><creatorcontrib>Beenakker, Jan-Willem M</creatorcontrib><creatorcontrib>Hooijmans, Melissa T</creatorcontrib><creatorcontrib>Naarding, Karin J</creatorcontrib><creatorcontrib>Niks, Erik H</creatorcontrib><creatorcontrib>Otto, Louise A M</creatorcontrib><creatorcontrib>van der Pol, W Ludo</creatorcontrib><creatorcontrib>Tannemaat, Martijn R</creatorcontrib><creatorcontrib>Kan, Hermien E</creatorcontrib><creatorcontrib>Froeling, Martijn</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Magnetic resonance in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Keene, Kevin R</au><au>Beenakker, Jan-Willem M</au><au>Hooijmans, Melissa T</au><au>Naarding, Karin J</au><au>Niks, Erik H</au><au>Otto, Louise A M</au><au>van der Pol, W Ludo</au><au>Tannemaat, Martijn R</au><au>Kan, Hermien E</au><au>Froeling, Martijn</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn Reson Med</addtitle><date>2020-11</date><risdate>2020</risdate><volume>84</volume><issue>5</issue><spage>2656</spage><epage>2670</epage><pages>2656-2670</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>Multi-echo spin-echo (MSE) transverse relaxometry mapping using multi-component models is used to study disease activity in neuromuscular disease by assessing the T
of the myocytic component (T
). Current extended phase graph algorithms are not optimized for fat fractions above 50% and the effects of inaccuracies in the T
calibration remain unexplored. Hence, we aimed to improve the performance of extended phase graph fitting methods over a large range of fat fractions, by including the slice-selection flip angle profile, a through-plane chemical-shift displacement correction, and optimized calibration of T
.
Simulation experiments were used to study the influence of the slice flip-angle profile with chemical-shift and T
estimations. Next, in vivo data from four neuromuscular disease cohorts were studied for different T
calibration methods and T
estimations.
Excluding slice flip-angle profiles or chemical-shift displacement resulted in a bias in T
up to 10 ms. Furthermore, a wrongly calibrated T
caused a bias of up to 4 ms in T
. For the in vivo data, one-component calibration led to a lower T
compared with a two-component method, and T
decreased with increasing fat fractions.
In vivo data showed a decline in T
for increasing fat fractions, which has important implications for clinical studies, especially in multicenter settings. We recommend using an extended phase graph-based model for fitting T
from MSE sequences with two-component T
calibration. Moreover, we recommend including the slice flip-angle profile in the model with correction for through-plane chemical-shift displacements.</abstract><cop>United States</cop><pmid>32306450</pmid><doi>10.1002/mrm.28290</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9300-9888</orcidid><orcidid>https://orcid.org/0000-0003-2929-0390</orcidid><orcidid>https://orcid.org/0000-0002-5772-7177</orcidid><orcidid>https://orcid.org/0000-0001-5022-3745</orcidid><orcidid>https://orcid.org/0000-0003-2998-4683</orcidid><orcidid>https://orcid.org/0000-0001-5892-5143</orcidid><orcidid>https://orcid.org/0000-0003-3841-0497</orcidid><orcidid>https://orcid.org/0000-0002-8970-2740</orcidid><orcidid>https://orcid.org/0000-0003-0479-5587</orcidid><orcidid>https://orcid.org/0000-0002-2233-1383</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0740-3194 |
ispartof | Magnetic resonance in medicine, 2020-11, Vol.84 (5), p.2656-2670 |
issn | 0740-3194 1522-2594 |
language | eng |
recordid | cdi_crossref_primary_10_1002_mrm_28290 |
source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
subjects | Algorithms Calibration Computer Simulation Magnetic Resonance Imaging Muscle, Skeletal - diagnostic imaging Phantoms, Imaging |
title | T 2 relaxation-time mapping in healthy and diseased skeletal muscle using extended phase graph algorithms |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T13%3A54%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pubmed_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=T%202%20relaxation-time%20mapping%20in%20healthy%20and%20diseased%20skeletal%20muscle%20using%20extended%20phase%20graph%20algorithms&rft.jtitle=Magnetic%20resonance%20in%20medicine&rft.au=Keene,%20Kevin%20R&rft.date=2020-11&rft.volume=84&rft.issue=5&rft.spage=2656&rft.epage=2670&rft.pages=2656-2670&rft.issn=0740-3194&rft.eissn=1522-2594&rft_id=info:doi/10.1002/mrm.28290&rft_dat=%3Cpubmed_cross%3E32306450%3C/pubmed_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/32306450&rfr_iscdi=true |