Literature DB >> 18790393

Accurate automatic papillary muscle identification for quantitative left ventricle mass measurements in cardiac magnetic resonance imaging.

Sharon Kirschbaum1, Jean-Paul Aben, Timo Baks, Amber Moelker, Katerina Gruszczynska, Gabriel P Krestin, Wim J van der Giessen, Dirk J Duncker, Pim J de Feyter, Robert-Jan M van Geuns.   

Abstract

RATIONALE AND
OBJECTIVES: We sought to evaluate the automatic detection of the papillary muscle and to determine its influence on quantitative left ventricular (LV) mass assessment.
MATERIALS AND METHODS: Twenty-eight Yorkshire-Landrace swine and 10 volunteers underwent cardiac magnetic resonance imaging (CMR) of the left ventricle. The variability in measurements of LV papillary muscles traced automatically and manually were compared to intra- and interobserver variabilities. CMR-derived LV mass with the papillary muscle included or excluded from LV mass measurements was compared to true mass at autopsy of the Yorkshire-Landrace swine.
RESULTS: Automatic LV papillary muscle mass from all subjects correlated well with manually derived LV papillary muscle mass measurements (r = 0.84) with no significant bias between both measurements (mean difference +/- SD, 0.0 +/- 1.5 g; P = .98). The variability in results related to the contour detection method used was not statistically significant different compared to intra- and interobserver variabilities (P = .08 and P = .97, respectively). LV mass measurements including the papillary muscle showed significantly less underestimation (-10.6 +/- 7.1 g) with the lowest percentage variability (6%) compared to measurements excluding the papillary muscles (mean underestimation, -15.1 +/- 7.4 g percentage variability, 7%).
CONCLUSION: The automatic algorithm for detecting the papillary muscle was accurate with variabilities comparable to intra- and interobserver variabilities. LV mass is determined most accurately when the papillary muscles are included in the LV mass measurements. Taken together, these observations warrant the inclusion of automatic contour detection of papillary muscle mass in studies that involve the determination of LV mass.

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Year:  2008        PMID: 18790393     DOI: 10.1016/j.acra.2008.04.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  5 in total

1.  Improved left ventricular mass quantification with partial voxel interpolation: in vivo and necropsy validation of a novel cardiac MRI segmentation algorithm.

Authors:  Noel C F Codella; Hae Yeoun Lee; David S Fieno; Debbie W Chen; Sandra Hurtado-Rua; Minisha Kochar; John Paul Finn; Robert Judd; Parag Goyal; Jesse Schenendorf; Matthew D Cham; Richard B Devereux; Martin Prince; Yi Wang; Jonathan W Weinsaft
Journal:  Circ Cardiovasc Imaging       Date:  2011-11-21       Impact factor: 7.792

Review 2.  LV mass assessed by echocardiography and CMR, cardiovascular outcomes, and medical practice.

Authors:  Anderson C Armstrong; Samuel Gidding; Ola Gjesdal; Colin Wu; David A Bluemke; João A C Lima
Journal:  JACC Cardiovasc Imaging       Date:  2012-08

3.  Statistical agreement of left ventricle measurements using cardiac magnetic resonance and 2D echocardiography in ischemic heart failure.

Authors:  Katarzyna Gruszczyńska; Lukasz J Krzych; Krzysztof S Gołba; Jolanta Biernat; Tomasz Roleder; Marek A Deja; Piotr Ulbrych; Marcin Malinowski; Piotr Janusiewicz; Stanisław Woś; Jan Baron
Journal:  Med Sci Monit       Date:  2012-03

Review 4.  A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging.

Authors:  Peng Peng; Karim Lekadir; Ali Gooya; Ling Shao; Steffen E Petersen; Alejandro F Frangi
Journal:  MAGMA       Date:  2016-01-25       Impact factor: 2.310

Review 5.  Artificial Intelligence, Machine Learning, and Cardiovascular Disease.

Authors:  Pankaj Mathur; Shweta Srivastava; Xiaowei Xu; Jawahar L Mehta
Journal:  Clin Med Insights Cardiol       Date:  2020-09-09
  5 in total

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