Literature DB >> 34643734

Ultrafast Computation of Left Ventricular Ejection Fraction by Using Temporal Intensity Variation in Cine Cardiac Magnetic Resonance.

Amol S Pednekar1, Benjamin Y C Cheong2,3, Raja Muthupillai2,3.   

Abstract

Cardiac magnetic resonance enables comprehensive cardiac evaluation; however, intense time and labor requirements for data acquisition and processing have discouraged many clinicians from using it. We have developed an alternative image-processing algorithm that requires minimal user interaction: an ultrafast algorithm that computes left ventricular ejection fraction (LVEF) by using temporal intensity variation in cine balanced steady-state free precession (bSSFP) short-axis images, with or without contrast medium. We evaluated the algorithm's performance against an expert observer's analysis for segmenting the LV cavity in 65 study participants (LVEF range, 12%-70%). In 12 instances, contrast medium was administered before cine imaging. Bland-Altman analysis revealed quantitative effects of LV basal, midcavity, and apical morphologic variation on the algorithm's accuracy. Total computation time for the LV stack was <2.5 seconds. The algorithm accurately delineated endocardial boundaries in 1,132 of 1,216 slices (93%). When contours in the extreme basal and apical slices were not adequate, they were replaced with manually drawn contours. The Bland-Altman mean differences were <1.2 mL (0.8%) for end-diastolic volume, <5 mL (6%) for end-systolic volume, and <3% for LVEF. Standard deviation of the difference was ≤4.1% of LV volume for all sections except the midcavity in end-systole (8.3% of end-systolic volume). We conclude that temporal intensity variation-based ultrafast LVEF computation is clinically accurate across a range of LV shapes and wall motions and is suitable for postcontrast cine SSFP imaging. Our algorithm enables real-time processing of cine bSSFP images on a commercial scanner console within 3 seconds in an unobtrusive automated process.
© 2021 by the Texas Heart® Institute, Houston.

Entities:  

Keywords:  Algorithms; automation; cardiac volume/physiology; image processing, computer-assisted/methods; magnetic resonance imaging, cine/methods; predictive value of tests; reference values; reproducibility of results; ventricular dysfunction, left/diagnosis

Mesh:

Year:  2021        PMID: 34643734      PMCID: PMC8717753          DOI: 10.14503/THIJ-20-7238

Source DB:  PubMed          Journal:  Tex Heart Inst J        ISSN: 0730-2347


  34 in total

1.  A level set approach for shape-driven segmentation and tracking of the left ventricle.

Authors:  Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2003-06       Impact factor: 10.048

2.  Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.

Authors:  Maria Lorenzo-Valdés; Gerardo I Sanchez-Ortiz; Andrew G Elkington; Raad H Mohiaddin; Daniel Rueckert
Journal:  Med Image Anal       Date:  2004-09       Impact factor: 8.545

3.  Automated segmentation of the left ventricle in cardiac MRI.

Authors:  Michael R Kaus; Jens von Berg; Jürgen Weese; Wiro Niessen; Vladimir Pekar
Journal:  Med Image Anal       Date:  2004-09       Impact factor: 8.545

4.  Automatic cardiac MRI segmentation using a biventricular deformable medial model.

Authors:  Hui Sun; Alejandro F Frangi; Hongzhi Wang; Federico M Sukno; Catalina Tobon-Gomez; Paul A Yushkevich
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

5.  Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI.

Authors:  Hae-Yeoun Lee; Noel C F Codella; Matthew D Cham; Jonathan W Weinsaft; Yi Wang
Journal:  IEEE Trans Biomed Eng       Date:  2009-02-06       Impact factor: 4.538

6.  Automatic functional analysis of left ventricle in cardiac cine MRI.

Authors:  Ying-Li Lu; Kim A Connelly; Alexander J Dick; Graham A Wright; Perry E Radau
Journal:  Quant Imaging Med Surg       Date:  2013-08

7.  A 3-D active shape model driven by fuzzy inference: application to cardiac CT and MR.

Authors:  Hans C van Assen; Mikhail G Danilouchkine; Martijn S Dirksen; Johan H C Reiber; Boudewijn P F Lelieveldt
Journal:  IEEE Trans Inf Technol Biomed       Date:  2008-09

8.  Influence of ejection fraction on cardiovascular outcomes in a broad spectrum of heart failure patients.

Authors:  Scott D Solomon; Nagesh Anavekar; Hicham Skali; John J V McMurray; Karl Swedberg; Salim Yusuf; Christopher B Granger; Eric L Michelson; Duolao Wang; Stuart Pocock; Marc A Pfeffer
Journal:  Circulation       Date:  2005-12-05       Impact factor: 29.690

9.  Automatic identification of the left ventricle in cardiac cine-MR images: dual-contrast cluster analysis and scout-geometry approaches.

Authors:  Amol S Pednekar; Raja Muthupillai; Veronica V Lenge; Ioannis A Kakadiaris; Scott D Flamm
Journal:  J Magn Reson Imaging       Date:  2006-05       Impact factor: 4.813

10.  Left ventricle segmentation using graph searching on intensity and gradient and a priori knowledge (lvGIGA) for short-axis cardiac magnetic resonance imaging.

Authors:  Hae-Yeoun Lee; Noel Codella; Matthew Cham; Martin Prince; Jonathan Weinsaft; Yi Wang
Journal:  J Magn Reson Imaging       Date:  2008-12       Impact factor: 4.813

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