Literature DB >> 15646885

Left ventricular ejection fraction calculation from automatically selected and processed diastolic and systolic frames in short-axis cine-MRI.

Alain Lalande1, N Salvé, A Comte, M C Jaulent, L Legrand, P M Walker, Y Cottin, J E Wolf, F Brunotte.   

Abstract

The calculation of the left ventricular ejection fraction (LVEF) is dependent upon the accurate measurement of diastolic and systolic left ventricular volumes. Although breath-hold cine magnetic resonance imaging (MRI) allows coverage of the whole cardiac cycle with an excellent time resolution, many authors rely on the visual selection of diastolic and the systolic short-axis slices in order to reduce the postprocessing time. An automatic method was developed to detect the endocardial contour on each image, allowing an automatic selection of the systolic frame. The calculated ejection fraction was compared with radionuclide ventriculography (RNV). Sixty-five patients were examined using an electrocardiogram (ECG)-gated gradient echo sequence. Among these examinations, manual and automatic processing with MRI were compared when the time of the systolic frame concorded. Good correlations have been found between the automatic MRI approach and RNV, and between manual and automatic processing on MRI alone. The results show that the automatic determination of the ejection fraction is feasible, and should constitute an important step toward a larger acceptance of MRI as a routine tool in heart disease imaging. One major benefit of using automatic postprocessing is that it may eliminate the visual choice of the systolic frame, inaccurate in more than 50% of the studied patients.

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Year:  2004        PMID: 15646885     DOI: 10.1081/jcmr-200036143

Source DB:  PubMed          Journal:  J Cardiovasc Magn Reson        ISSN: 1097-6647            Impact factor:   5.364


  5 in total

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Journal:  Int J Cardiovasc Imaging       Date:  2015-01-30       Impact factor: 2.357

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Journal:  Int J Comput Assist Radiol Surg       Date:  2016-06-13       Impact factor: 2.924

3.  Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging.

Authors:  Fan Yang; Yan He; Mubashir Hussain; Hong Xie; Pinggui Lei
Journal:  Comput Math Methods Med       Date:  2017-07-26       Impact factor: 2.238

4.  Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

Authors:  Jessica Lebenberg; Alain Lalande; Patrick Clarysse; Irene Buvat; Christopher Casta; Alexandre Cochet; Constantin Constantinidès; Jean Cousty; Alain de Cesare; Stephanie Jehan-Besson; Muriel Lefort; Laurent Najman; Elodie Roullot; Laurent Sarry; Christophe Tilmant; Frederique Frouin; Mireille Garreau
Journal:  PLoS One       Date:  2015-08-19       Impact factor: 3.240

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Authors:  Kuang-Yung Lee; Moyi Li; Mini Manchanda; Ranjan Batra; Konstantinos Charizanis; Apoorva Mohan; Sonisha A Warren; Christopher M Chamberlain; Dustin Finn; Hannah Hong; Hassan Ashraf; Hideko Kasahara; Laura P W Ranum; Maurice S Swanson
Journal:  EMBO Mol Med       Date:  2013-10-08       Impact factor: 12.137

  5 in total

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