Literature DB >> 26646347

Semiautomatic detection of myocardial contours in order to investigate normal values of the left ventricular trabeculated mass using MRI.

Stéphanie Bricq1, Julien Frandon2, Monique Bernard3, Maxime Guye3, Mathieu Finas2, Laetitia Marcadet4, Lucile Miquerol4, Frank Kober3, Gilbert Habib5, Daniel Fagret6, Alexis Jacquier3,7, Alain Lalande1,8.   

Abstract

PURPOSE: To propose, assess, and validate a semiautomatic method allowing rapid and reproducible measurement of trabeculated and compacted left ventricular (LV) masses from cardiac magnetic resonance imaging (MRI).
MATERIALS AND METHODS: We developed a method to automatically detect noncompacted, endocardial, and epicardial contours. Papillary muscles were segmented using semiautomatic thresholding and were included in the compacted mass. Blood was removed from trabeculae using the same threshold tool. Trabeculated, compacted masses and ratio of noncompacted to compacted (NC:C) masses were computed. Preclinical validation was performed on four transgenic mice with hypertrabeculation of the LV (high-resolution cine imaging, 11.75T). Then analysis was performed on normal cine-MRI examinations (steady-state free precession [SSFP] sequences, 1.5T or 3T) obtained from 60 healthy participants (mean age 49 ± 16 years) with 10 men and 10 women for each of the following age groups: [20,39], [40,59], and [60,79]. Interobserver and interexamination segmentation reproducibility was assessed by using Bland-Altman analysis and by computing the correlation coefficient.
RESULTS: In normal participants, noncompacted and compacted masses were 6.29 ± 2.03 g/m(2) and 62.17 ± 11.32 g/m(2) , respectively. The NC:C mass ratio was 10.26 ± 3.27%. Correlation between the two observers was from 0.85 for NC:C ratio to 0.99 for end-diastolic volume (P < 10(-5) ). The bias between the two observers was -1.06 ± 1.02 g/m(2) for trabeculated mass, -1.41 ± 2.78 g/m(2) for compacted mass, and -1.51 ± 1.77% for NC:C ratio.
CONCLUSION: We propose a semiautomatic method based on region growing, active contours, and thresholding to calculate the NC:C mass ratio. This method is highly reproducible and might help in the diagnosis of LV noncompaction cardiomyopathy. J. Magn. Reson. Imaging 2016;43:1398-1406.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  cardiovascular magnetic resonance imaging; left ventricle; noncompaction; papillary muscles; trabeculae

Mesh:

Year:  2015        PMID: 26646347     DOI: 10.1002/jmri.25113

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  6 in total

1.  Non-compact myocardium assessment by cardiac magnetic resonance: dependence on image analysis method.

Authors:  Vincenzo Positano; Antonella Meloni; Francesca Macaione; Maria Filomena Santarelli; Laura Pistoia; Andrea Barison; Salvatore Novo; Alessia Pepe
Journal:  Int J Cardiovasc Imaging       Date:  2018-03-09       Impact factor: 2.357

2.  Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction -  comparison between manual and semiautomatic CMR image analysis methods.

Authors:  Marcin Kubik; Alicja Dąbrowska-Kugacka; Karolina Dorniak; Marta Kutniewska-Kubik; Ludmiła Daniłowicz-Szymanowicz; Ewa Lewicka; Edyta Szurowska; Grzegorz Raczak
Journal:  PLoS One       Date:  2020-03-11       Impact factor: 3.240

3.  Recreational marathon running does not cause exercise-induced left ventricular hypertrabeculation.

Authors:  Andrew D'Silva; Gabriella Captur; Anish N Bhuva; Siana Jones; Rachel Bastiaenen; Amna Abdel-Gadir; Sabiha Gati; Jet van Zalen; James Willis; Aneil Malhotra; Irina Chis Ster; Charlotte Manisty; Alun D Hughes; Guy Lloyd; Rajan Sharma; James C Moon; Sanjay Sharma
Journal:  Int J Cardiol       Date:  2020-04-29       Impact factor: 4.164

4.  A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

Authors:  Marcelo Dantas Tavares de Melo; Jose de Arimatéia Batista Araujo-Filho; José Raimundo Barbosa; Camila Rocon; Carlos Danilo Miranda Regis; Alex Dos Santos Felix; Roberto Kalil Filho; Edimar Alcides Bocchi; Ludhmila Abrahão Hajjar; Mahdi Tabassian; Jan D'hooge; Vera Maria Cury Salemi
Journal:  PLoS One       Date:  2021-11-29       Impact factor: 3.240

5.  Deep Learning-based Automated Segmentation of Left Ventricular Trabeculations and Myocardium on Cardiac MR Images: A Feasibility Study.

Authors:  Axel Bartoli; Joris Fournel; Zakarya Bentatou; Gilbert Habib; Alain Lalande; Monique Bernard; Loïc Boussel; François Pontana; Jean-Nicolas Dacher; Badih Ghattas; Alexis Jacquier
Journal:  Radiol Artif Intell       Date:  2020-11-25

6.  Semi-automatic detection of myocardial trabeculation using cardiovascular magnetic resonance: correlation with histology and reproducibility in a mouse model of non-compaction.

Authors:  Julien Frandon; Stéphanie Bricq; Zakarya Bentatou; Laetitia Marcadet; Pierre Antoine Barral; Mathieu Finas; Daniel Fagret; Frank Kober; Gilbert Habib; Monique Bernard; Alain Lalande; Lucile Miquerol; Alexis Jacquier
Journal:  J Cardiovasc Magn Reson       Date:  2018-10-25       Impact factor: 5.364

  6 in total

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