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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Veröffentlicht in:Journal of magnetic resonance imaging 2017-02, Vol.45 (2), p.525-534
Hauptverfasser: Leong, Chen Onn, Liew, Yih Miin, Bilgen, Mehmet, Abdul Aziz, Yang Faridah, Chee, Kok Han, Chiam, Yin Kia, Lim, Einly
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container_end_page 534
container_issue 2
container_start_page 525
container_title Journal of magnetic resonance imaging
container_volume 45
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
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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 &lt; 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P &lt; 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 &lt; 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P &lt; 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. 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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 &lt; 0.01). The median position errors were 0.82 mm versus 1.23 mm. The αES correlates significantly with REF (r = 0.91, P &lt; 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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