Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging
Purpose To propose a cardiac motion tracking model that evaluates wall motion abnormality in postmyocardial infarction patients. Correlation between the motion parameter of the model and left ventricle (LV) function was also determined. Materials and Methods Twelve male patients with post‐ST elevati...
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creator | Leong, Chen Onn Liew, Yih Miin Bilgen, Mehmet Abdul Aziz, Yang Faridah Chee, Kok Han Chiam, Yin Kia Lim, Einly |
description | Purpose
To propose a cardiac motion tracking model that evaluates wall motion abnormality in postmyocardial infarction patients. Correlation between the motion parameter of the model and left ventricle (LV) function was also determined.
Materials and Methods
Twelve male patients with post‐ST elevation myocardial infarction (post‐STEMI) and 10 healthy controls of the same gender were recruited to undergo cardiac magnetic resonance imaging (MRI) using a 1.5T scanner. Using an infarct‐specific LV division approach, the late gadolinium enhancement (LGE) MRI images were used to divide the LV on the tagged MRI images into infarct, adjacent, and remote sectors. Motion tracking was performed using the infarct‐specific two‐parameter empirical deformable model (TPEDM). The match quality was defined as the position error computed using root‐mean‐square (RMS) distance between the estimated and expert‐verified tag intersections. The position errors were compared with the ones from our previously published fixed‐sector TPEDM. Cine MRI images were used to calculate regional ejection fraction (REF). Correlation between the end‐systolic contraction parameter (αES) with REF was determined.
Results
The position errors in the proposed model were significantly lower than the fixed‐sector model (P < 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P < 0.01).
Conclusion
The infarct‐specific TPEDM combines the morphological and functional information from LGE and tagged MRI images. It was shown to outperform the fixed‐sector model in assessing regional LV dysfunction. The significant correlation between αES and REF added prognostic value because it indicated an impairment of cardiac function with the increase of infarct transmurality.
Level of Evidence: 3
J. Magn. Reson. Imaging 2017;45:525–534. |
doi_str_mv | 10.1002/jmri.25390 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_1859498160</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1826721084</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3910-dcd61182fd94444852c50721135c247eb149d3705c8bf22b3331bebdf72e9fd63</originalsourceid><addsrcrecordid>eNqNkc1u1DAUhS0EoqWw4QFQJDZsUnz9k9jLquKnqFWlCtaRY1-PPIqdwU6EZscj8Iw8CZ5pYcGq3vjI59O5uj6EvAZ6DpSy99uYwzmTXNMn5BQkYy2TqntaNZW8BUX7E_KilC2lVGshn5MT1gtQIOkpWS5KwVIipqWZfROSN9kuv3_-Kju0wQfbWJNdMLaJ8xLm1Lh98WuyR72WkDbVcDgdhEmuieu0hPpipiaaTcKlJmQsczLJYhMxbyr5kjzzZir46uE-I98-fvh6-bm9vv10dXlx3VqugbbOug5AMe-0qEdJZiXtGQCXlokeRxDa8Z5Kq0bP2Mg5hxFH53uG2ruOn5F397m7PH9fsSxDDMXiNJmE81oGUFILraCjj0BZV0dTJSr69j90O6851UUOgZ0QUhypNw_UOkZ0wy6HaPJ--Pv1FYB74EeYcP_PBzocSh0OpQ7HUocvN3dXR8X_AGR0lhk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1856445484</pqid></control><display><type>article</type><title>Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><source>Wiley Online Library (Open Access Collection)</source><creator>Leong, Chen Onn ; Liew, Yih Miin ; Bilgen, Mehmet ; Abdul Aziz, Yang Faridah ; Chee, Kok Han ; Chiam, Yin Kia ; Lim, Einly</creator><creatorcontrib>Leong, Chen Onn ; Liew, Yih Miin ; Bilgen, Mehmet ; Abdul Aziz, Yang Faridah ; Chee, Kok Han ; Chiam, Yin Kia ; Lim, Einly</creatorcontrib><description>Purpose
To propose a cardiac motion tracking model that evaluates wall motion abnormality in postmyocardial infarction patients. Correlation between the motion parameter of the model and left ventricle (LV) function was also determined.
Materials and Methods
Twelve male patients with post‐ST elevation myocardial infarction (post‐STEMI) and 10 healthy controls of the same gender were recruited to undergo cardiac magnetic resonance imaging (MRI) using a 1.5T scanner. Using an infarct‐specific LV division approach, the late gadolinium enhancement (LGE) MRI images were used to divide the LV on the tagged MRI images into infarct, adjacent, and remote sectors. Motion tracking was performed using the infarct‐specific two‐parameter empirical deformable model (TPEDM). The match quality was defined as the position error computed using root‐mean‐square (RMS) distance between the estimated and expert‐verified tag intersections. The position errors were compared with the ones from our previously published fixed‐sector TPEDM. Cine MRI images were used to calculate regional ejection fraction (REF). Correlation between the end‐systolic contraction parameter (αES) with REF was determined.
Results
The position errors in the proposed model were significantly lower than the fixed‐sector model (P < 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P < 0.01).
Conclusion
The infarct‐specific TPEDM combines the morphological and functional information from LGE and tagged MRI images. It was shown to outperform the fixed‐sector model in assessing regional LV dysfunction. The significant correlation between αES and REF added prognostic value because it indicated an impairment of cardiac function with the increase of infarct transmurality.
Level of Evidence: 3
J. Magn. Reson. Imaging 2017;45:525–534.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.25390</identifier><identifier>PMID: 27418150</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>cardiac modeling ; Computer Simulation ; Humans ; Image Interpretation, Computer-Assisted - methods ; late gadolinium enhancement ; LV motion analysis ; Magnetic resonance imaging ; Magnetic Resonance Imaging, Cine - methods ; Male ; Middle Aged ; Models, Cardiovascular ; Motion ; Movement ; Multimodal Imaging - methods ; myocardial infarction ; Myocardial Infarction - complications ; Myocardial Infarction - diagnostic imaging ; Myocardial Infarction - physiopathology ; NMR ; Nuclear magnetic resonance ; Reproducibility of Results ; Sensitivity and Specificity ; Subtraction Technique ; tagged MRI ; Tomography ; Ventricular Dysfunction, Left - diagnostic imaging ; Ventricular Dysfunction, Left - etiology ; Ventricular Dysfunction, Left - physiopathology</subject><ispartof>Journal of magnetic resonance imaging, 2017-02, Vol.45 (2), p.525-534</ispartof><rights>2016 International Society for Magnetic Resonance in Medicine</rights><rights>2016 International Society for Magnetic Resonance in Medicine.</rights><rights>2017 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3910-dcd61182fd94444852c50721135c247eb149d3705c8bf22b3331bebdf72e9fd63</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.25390$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.25390$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27418150$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leong, Chen Onn</creatorcontrib><creatorcontrib>Liew, Yih Miin</creatorcontrib><creatorcontrib>Bilgen, Mehmet</creatorcontrib><creatorcontrib>Abdul Aziz, Yang Faridah</creatorcontrib><creatorcontrib>Chee, Kok Han</creatorcontrib><creatorcontrib>Chiam, Yin Kia</creatorcontrib><creatorcontrib>Lim, Einly</creatorcontrib><title>Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Purpose
To propose a cardiac motion tracking model that evaluates wall motion abnormality in postmyocardial infarction patients. Correlation between the motion parameter of the model and left ventricle (LV) function was also determined.
Materials and Methods
Twelve male patients with post‐ST elevation myocardial infarction (post‐STEMI) and 10 healthy controls of the same gender were recruited to undergo cardiac magnetic resonance imaging (MRI) using a 1.5T scanner. Using an infarct‐specific LV division approach, the late gadolinium enhancement (LGE) MRI images were used to divide the LV on the tagged MRI images into infarct, adjacent, and remote sectors. Motion tracking was performed using the infarct‐specific two‐parameter empirical deformable model (TPEDM). The match quality was defined as the position error computed using root‐mean‐square (RMS) distance between the estimated and expert‐verified tag intersections. The position errors were compared with the ones from our previously published fixed‐sector TPEDM. Cine MRI images were used to calculate regional ejection fraction (REF). Correlation between the end‐systolic contraction parameter (αES) with REF was determined.
Results
The position errors in the proposed model were significantly lower than the fixed‐sector model (P < 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P < 0.01).
Conclusion
The infarct‐specific TPEDM combines the morphological and functional information from LGE and tagged MRI images. It was shown to outperform the fixed‐sector model in assessing regional LV dysfunction. The significant correlation between αES and REF added prognostic value because it indicated an impairment of cardiac function with the increase of infarct transmurality.
Level of Evidence: 3
J. Magn. Reson. Imaging 2017;45:525–534.</description><subject>cardiac modeling</subject><subject>Computer Simulation</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>late gadolinium enhancement</subject><subject>LV motion analysis</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging, Cine - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Cardiovascular</subject><subject>Motion</subject><subject>Movement</subject><subject>Multimodal Imaging - methods</subject><subject>myocardial infarction</subject><subject>Myocardial Infarction - complications</subject><subject>Myocardial Infarction - diagnostic imaging</subject><subject>Myocardial Infarction - physiopathology</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Subtraction Technique</subject><subject>tagged MRI</subject><subject>Tomography</subject><subject>Ventricular Dysfunction, Left - diagnostic imaging</subject><subject>Ventricular Dysfunction, Left - etiology</subject><subject>Ventricular Dysfunction, Left - physiopathology</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1u1DAUhS0EoqWw4QFQJDZsUnz9k9jLquKnqFWlCtaRY1-PPIqdwU6EZscj8Iw8CZ5pYcGq3vjI59O5uj6EvAZ6DpSy99uYwzmTXNMn5BQkYy2TqntaNZW8BUX7E_KilC2lVGshn5MT1gtQIOkpWS5KwVIipqWZfROSN9kuv3_-Kju0wQfbWJNdMLaJ8xLm1Lh98WuyR72WkDbVcDgdhEmuieu0hPpipiaaTcKlJmQsczLJYhMxbyr5kjzzZir46uE-I98-fvh6-bm9vv10dXlx3VqugbbOug5AMe-0qEdJZiXtGQCXlokeRxDa8Z5Kq0bP2Mg5hxFH53uG2ruOn5F397m7PH9fsSxDDMXiNJmE81oGUFILraCjj0BZV0dTJSr69j90O6851UUOgZ0QUhypNw_UOkZ0wy6HaPJ--Pv1FYB74EeYcP_PBzocSh0OpQ7HUocvN3dXR8X_AGR0lhk</recordid><startdate>201702</startdate><enddate>201702</enddate><creator>Leong, Chen Onn</creator><creator>Liew, Yih Miin</creator><creator>Bilgen, Mehmet</creator><creator>Abdul Aziz, Yang Faridah</creator><creator>Chee, Kok Han</creator><creator>Chiam, Yin Kia</creator><creator>Lim, Einly</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201702</creationdate><title>Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging</title><author>Leong, Chen Onn ; Liew, Yih Miin ; Bilgen, Mehmet ; Abdul Aziz, Yang Faridah ; Chee, Kok Han ; Chiam, Yin Kia ; Lim, Einly</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3910-dcd61182fd94444852c50721135c247eb149d3705c8bf22b3331bebdf72e9fd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>cardiac modeling</topic><topic>Computer Simulation</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>late gadolinium enhancement</topic><topic>LV motion analysis</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging, Cine - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Models, Cardiovascular</topic><topic>Motion</topic><topic>Movement</topic><topic>Multimodal Imaging - methods</topic><topic>myocardial infarction</topic><topic>Myocardial Infarction - complications</topic><topic>Myocardial Infarction - diagnostic imaging</topic><topic>Myocardial Infarction - physiopathology</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Subtraction Technique</topic><topic>tagged MRI</topic><topic>Tomography</topic><topic>Ventricular Dysfunction, Left - diagnostic imaging</topic><topic>Ventricular Dysfunction, Left - etiology</topic><topic>Ventricular Dysfunction, Left - physiopathology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leong, Chen Onn</creatorcontrib><creatorcontrib>Liew, Yih Miin</creatorcontrib><creatorcontrib>Bilgen, Mehmet</creatorcontrib><creatorcontrib>Abdul Aziz, Yang Faridah</creatorcontrib><creatorcontrib>Chee, Kok Han</creatorcontrib><creatorcontrib>Chiam, Yin Kia</creatorcontrib><creatorcontrib>Lim, Einly</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leong, Chen Onn</au><au>Liew, Yih Miin</au><au>Bilgen, Mehmet</au><au>Abdul Aziz, Yang Faridah</au><au>Chee, Kok Han</au><au>Chiam, Yin Kia</au><au>Lim, Einly</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2017-02</date><risdate>2017</risdate><volume>45</volume><issue>2</issue><spage>525</spage><epage>534</epage><pages>525-534</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Purpose
To propose a cardiac motion tracking model that evaluates wall motion abnormality in postmyocardial infarction patients. Correlation between the motion parameter of the model and left ventricle (LV) function was also determined.
Materials and Methods
Twelve male patients with post‐ST elevation myocardial infarction (post‐STEMI) and 10 healthy controls of the same gender were recruited to undergo cardiac magnetic resonance imaging (MRI) using a 1.5T scanner. Using an infarct‐specific LV division approach, the late gadolinium enhancement (LGE) MRI images were used to divide the LV on the tagged MRI images into infarct, adjacent, and remote sectors. Motion tracking was performed using the infarct‐specific two‐parameter empirical deformable model (TPEDM). The match quality was defined as the position error computed using root‐mean‐square (RMS) distance between the estimated and expert‐verified tag intersections. The position errors were compared with the ones from our previously published fixed‐sector TPEDM. Cine MRI images were used to calculate regional ejection fraction (REF). Correlation between the end‐systolic contraction parameter (αES) with REF was determined.
Results
The position errors in the proposed model were significantly lower than the fixed‐sector model (P < 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P < 0.01).
Conclusion
The infarct‐specific TPEDM combines the morphological and functional information from LGE and tagged MRI images. It was shown to outperform the fixed‐sector model in assessing regional LV dysfunction. The significant correlation between αES and REF added prognostic value because it indicated an impairment of cardiac function with the increase of infarct transmurality.
Level of Evidence: 3
J. Magn. Reson. Imaging 2017;45:525–534.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>27418150</pmid><doi>10.1002/jmri.25390</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | cardiac modeling Computer Simulation Humans Image Interpretation, Computer-Assisted - methods late gadolinium enhancement LV motion analysis Magnetic resonance imaging Magnetic Resonance Imaging, Cine - methods Male Middle Aged Models, Cardiovascular Motion Movement Multimodal Imaging - methods myocardial infarction Myocardial Infarction - complications Myocardial Infarction - diagnostic imaging Myocardial Infarction - physiopathology NMR Nuclear magnetic resonance Reproducibility of Results Sensitivity and Specificity Subtraction Technique tagged MRI Tomography Ventricular Dysfunction, Left - diagnostic imaging Ventricular Dysfunction, Left - etiology Ventricular Dysfunction, Left - physiopathology |
title | Assessment of infarct‐specific cardiac motion dysfunction using modeling and multimodal magnetic resonance merging |
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