Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques
We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. Multi-volume 3D high-resolution cardiac images w...
Gespeichert in:
Veröffentlicht in: | PloS one 2021-09, Vol.16 (9), p.e0252777-e0252777 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | e0252777 |
---|---|
container_issue | 9 |
container_start_page | e0252777 |
container_title | PloS one |
container_volume | 16 |
creator | Zhu, Dan Ding, Haiyan Zviman, M. Muz Halperin, Henry Schär, Michael Herzka, Daniel A |
description | We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (R.sub.net) 2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when R.sub.net >3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all R.sub.net (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on R.sub.net . The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for R.sub.net >3. |
doi_str_mv | 10.1371/journal.pone.0252777 |
format | Article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_proquest_miscellaneous_2571919842</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A674968906</galeid><doaj_id>oai_doaj_org_article_b9db53a99edd4ce79742c0bd0ad5b55e</doaj_id><sourcerecordid>A674968906</sourcerecordid><originalsourceid>FETCH-LOGICAL-c669t-698fb14365cf3568a88223887a0ca005dceb18dfb717bb76a6e5e59ea68e6c953</originalsourceid><addsrcrecordid>eNqNk1uL1TAQx4so7kW_gWBBkPXhHNukufkgHNbbgYUFXX0NaTptc2iTmrRevr2pp8pW9kH6kDL5zT-Z_2SS5EmebXPM8pcHN3mruu3gLGwzRBBj7F5ymguMNhRl-P6t_5PkLIRDlhHMKX2YnOCCZLQQ9DRpd1pDB16Nxjbp99Z1sGlB-THFb9IblPZqGOLOq3TfD0qPqavTyVbgg-qHbk4JY8yFxkBIla1SD9rZGJv0aJxNR9CtNV8nCI-SB7XqAjxe1vPk87u3N5cfNlfX7_eXu6uNplSMGyp4XeYFpkTXmFCuOEcIc85UplUsoNJQ5ryqS5azsmRUUSBABCjKgWpB8Hny9Kg7dC7IxaQgEWF5UVBBWCT2R6Jy6iAHb3rlf0qnjPwdcL6RsX6jO5ClqEqClRBQVYUGJliBdFZWmapISQhErdfLaVPZQ7ycjXZ0K9H1jjWtbNw3yQuMOMJR4GIR8G62aZS9CbEjnbLgpuO9RS54gSL67B_07uoWqlGxAGNrF8_Vs6jcURZ7zkVGI7W9g4pfBb2JHYTaxPgq4cUqITIj_BgbNYUg958-_j97_WXNPr_FxpfXjW1w3TS_nrAGiyOovQvBQ_3X5DyT80D8cUPOAyGXgcC_AFQz_YU</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2571446957</pqid></control><display><type>article</type><title>Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques</title><source>Public Library of Science (PLoS) Journals Open Access</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Zhu, Dan ; Ding, Haiyan ; Zviman, M. Muz ; Halperin, Henry ; Schär, Michael ; Herzka, Daniel A</creator><creatorcontrib>Zhu, Dan ; Ding, Haiyan ; Zviman, M. Muz ; Halperin, Henry ; Schär, Michael ; Herzka, Daniel A</creatorcontrib><description>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (R.sub.net) 2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when R.sub.net >3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all R.sub.net (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on R.sub.net . The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for R.sub.net >3.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0252777</identifier><identifier>PMID: 34506496</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Bias ; Biology and Life Sciences ; Biomedical engineering ; Cardiology ; Engineering and Technology ; Heart ; Heart attacks ; Heart muscle ; High resolution ; Human subjects ; Image acquisition ; Image reconstruction ; Image resolution ; Image segmentation ; Mapping ; Medicine ; Medicine and Health Sciences ; Modelling ; Myocardial infarction ; Radiology ; Research and Analysis Methods ; Root-mean-square errors ; Sampling ; Sparsity ; Spatial discrimination ; Spatial resolution ; Structure ; Swine ; Three dimensional imaging ; Ventricle</subject><ispartof>PloS one, 2021-09, Vol.16 (9), p.e0252777-e0252777</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (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-c669t-698fb14365cf3568a88223887a0ca005dceb18dfb717bb76a6e5e59ea68e6c953</citedby><cites>FETCH-LOGICAL-c669t-698fb14365cf3568a88223887a0ca005dceb18dfb717bb76a6e5e59ea68e6c953</cites><orcidid>0000-0002-9400-7814 ; 0000-0002-0940-1519 ; 0000-0003-0203-3642</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432823/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432823/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids></links><search><creatorcontrib>Zhu, Dan</creatorcontrib><creatorcontrib>Ding, Haiyan</creatorcontrib><creatorcontrib>Zviman, M. Muz</creatorcontrib><creatorcontrib>Halperin, Henry</creatorcontrib><creatorcontrib>Schär, Michael</creatorcontrib><creatorcontrib>Herzka, Daniel A</creatorcontrib><title>Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques</title><title>PloS one</title><description>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (R.sub.net) 2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when R.sub.net >3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all R.sub.net (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on R.sub.net . The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for R.sub.net >3.</description><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Biomedical engineering</subject><subject>Cardiology</subject><subject>Engineering and Technology</subject><subject>Heart</subject><subject>Heart attacks</subject><subject>Heart muscle</subject><subject>High resolution</subject><subject>Human subjects</subject><subject>Image acquisition</subject><subject>Image reconstruction</subject><subject>Image resolution</subject><subject>Image segmentation</subject><subject>Mapping</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Myocardial infarction</subject><subject>Radiology</subject><subject>Research and Analysis Methods</subject><subject>Root-mean-square errors</subject><subject>Sampling</subject><subject>Sparsity</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Structure</subject><subject>Swine</subject><subject>Three dimensional imaging</subject><subject>Ventricle</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1uL1TAQx4so7kW_gWBBkPXhHNukufkgHNbbgYUFXX0NaTptc2iTmrRevr2pp8pW9kH6kDL5zT-Z_2SS5EmebXPM8pcHN3mruu3gLGwzRBBj7F5ymguMNhRl-P6t_5PkLIRDlhHMKX2YnOCCZLQQ9DRpd1pDB16Nxjbp99Z1sGlB-THFb9IblPZqGOLOq3TfD0qPqavTyVbgg-qHbk4JY8yFxkBIla1SD9rZGJv0aJxNR9CtNV8nCI-SB7XqAjxe1vPk87u3N5cfNlfX7_eXu6uNplSMGyp4XeYFpkTXmFCuOEcIc85UplUsoNJQ5ryqS5azsmRUUSBABCjKgWpB8Hny9Kg7dC7IxaQgEWF5UVBBWCT2R6Jy6iAHb3rlf0qnjPwdcL6RsX6jO5ClqEqClRBQVYUGJliBdFZWmapISQhErdfLaVPZQ7ycjXZ0K9H1jjWtbNw3yQuMOMJR4GIR8G62aZS9CbEjnbLgpuO9RS54gSL67B_07uoWqlGxAGNrF8_Vs6jcURZ7zkVGI7W9g4pfBb2JHYTaxPgq4cUqITIj_BgbNYUg958-_j97_WXNPr_FxpfXjW1w3TS_nrAGiyOovQvBQ_3X5DyT80D8cUPOAyGXgcC_AFQz_YU</recordid><startdate>20210910</startdate><enddate>20210910</enddate><creator>Zhu, Dan</creator><creator>Ding, Haiyan</creator><creator>Zviman, M. Muz</creator><creator>Halperin, Henry</creator><creator>Schär, Michael</creator><creator>Herzka, Daniel A</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9400-7814</orcidid><orcidid>https://orcid.org/0000-0002-0940-1519</orcidid><orcidid>https://orcid.org/0000-0003-0203-3642</orcidid></search><sort><creationdate>20210910</creationdate><title>Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques</title><author>Zhu, Dan ; Ding, Haiyan ; Zviman, M. Muz ; Halperin, Henry ; Schär, Michael ; Herzka, Daniel A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-698fb14365cf3568a88223887a0ca005dceb18dfb717bb76a6e5e59ea68e6c953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bias</topic><topic>Biology and Life Sciences</topic><topic>Biomedical engineering</topic><topic>Cardiology</topic><topic>Engineering and Technology</topic><topic>Heart</topic><topic>Heart attacks</topic><topic>Heart muscle</topic><topic>High resolution</topic><topic>Human subjects</topic><topic>Image acquisition</topic><topic>Image reconstruction</topic><topic>Image resolution</topic><topic>Image segmentation</topic><topic>Mapping</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Modelling</topic><topic>Myocardial infarction</topic><topic>Radiology</topic><topic>Research and Analysis Methods</topic><topic>Root-mean-square errors</topic><topic>Sampling</topic><topic>Sparsity</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Structure</topic><topic>Swine</topic><topic>Three dimensional imaging</topic><topic>Ventricle</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Dan</creatorcontrib><creatorcontrib>Ding, Haiyan</creatorcontrib><creatorcontrib>Zviman, M. Muz</creatorcontrib><creatorcontrib>Halperin, Henry</creatorcontrib><creatorcontrib>Schär, Michael</creatorcontrib><creatorcontrib>Herzka, Daniel A</creatorcontrib><collection>CrossRef</collection><collection>Opposing Viewpoints in Context (Gale)</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Dan</au><au>Ding, Haiyan</au><au>Zviman, M. Muz</au><au>Halperin, Henry</au><au>Schär, Michael</au><au>Herzka, Daniel A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques</atitle><jtitle>PloS one</jtitle><date>2021-09-10</date><risdate>2021</risdate><volume>16</volume><issue>9</issue><spage>e0252777</spage><epage>e0252777</epage><pages>e0252777-e0252777</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (R.sub.net) 2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when R.sub.net >3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all R.sub.net (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on R.sub.net . The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for R.sub.net >3.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>34506496</pmid><doi>10.1371/journal.pone.0252777</doi><tpages>e0252777</tpages><orcidid>https://orcid.org/0000-0002-9400-7814</orcidid><orcidid>https://orcid.org/0000-0002-0940-1519</orcidid><orcidid>https://orcid.org/0000-0003-0203-3642</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2021-09, Vol.16 (9), p.e0252777-e0252777 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_proquest_miscellaneous_2571919842 |
source | Public Library of Science (PLoS) Journals Open Access; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Bias Biology and Life Sciences Biomedical engineering Cardiology Engineering and Technology Heart Heart attacks Heart muscle High resolution Human subjects Image acquisition Image reconstruction Image resolution Image segmentation Mapping Medicine Medicine and Health Sciences Modelling Myocardial infarction Radiology Research and Analysis Methods Root-mean-square errors Sampling Sparsity Spatial discrimination Spatial resolution Structure Swine Three dimensional imaging Ventricle |
title | Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T16%3A38%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accelerating%20whole-heart%203D%20T2%20mapping:%20Impact%20of%20undersampling%20strategies%20and%20reconstruction%20techniques&rft.jtitle=PloS%20one&rft.au=Zhu,%20Dan&rft.date=2021-09-10&rft.volume=16&rft.issue=9&rft.spage=e0252777&rft.epage=e0252777&rft.pages=e0252777-e0252777&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0252777&rft_dat=%3Cgale_plos_%3EA674968906%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2571446957&rft_id=info:pmid/34506496&rft_galeid=A674968906&rft_doaj_id=oai_doaj_org_article_b9db53a99edd4ce79742c0bd0ad5b55e&rfr_iscdi=true |