title: Semiautomatic detection of myocardial contours in order to investigate normal values of the left ventricular trabeculated mass using MRI.
abstract: 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).
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Le2i, UMR 6306, CNRS, Arts et Métiers, Université de Bourgogne Franche-Comté, Dijon, France.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Bricq
authors.affiliation: Service de radiologie, CHU, Grenoble, France.
authors.forename: Julien
authors.initials: J
authors.lastname: Frandon
authors.affiliation: Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.
authors.forename: Monique
authors.initials: M
authors.lastname: Bernard
authors.affiliation: Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.
authors.forename: Maxime
authors.initials: M
authors.lastname: Guye
authors.affiliation: Service de radiologie, CHU, Grenoble, France.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Finas
authors.affiliation: Aix-Marseille Université, CNRS, IBDM UMR 7288, Marseille, France.
authors.forename: Laetitia
authors.initials: L
authors.lastname: Marcadet
authors.affiliation: Aix-Marseille Université, CNRS, IBDM UMR 7288, Marseille, France.
authors.forename: Lucile
authors.initials: L
authors.lastname: Miquerol
authors.affiliation: Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.
authors.forename: Frank
authors.initials: F
authors.lastname: Kober
authors.affiliation: Département de Cardiologie, Hôpital de La Timone, AP-HM, Aix-Marseille Université, Marseille, France.
authors.forename: Gilbert
authors.initials: G
authors.lastname: Habib
authors.affiliation: Service de Médecine Nucléaire, CHU, Grenoble, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fagret
authors.affiliation: Aix-Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France.|Département de Radiologie, Aix Marseille Université, Hôpital Universitaire La Timone, Marseille, France.
authors.forename: Alexis
authors.initials: A
authors.lastname: Jacquier
authors.affiliation: Le2i, UMR 6306, CNRS, Arts et Métiers, Université de Bourgogne Franche-Comté, Dijon, France.|Service d'IRM, CHU, Dijon, France.
authors.forename: Alain
authors.initials: A
authors.lastname: Lalande
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000818:Animals; D005260:Female; D006330:Heart Defects, Congenital / Q000000981:diagnostic imaging* / Q000473:pathology*; D006352:Heart Ventricles / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D051379:Mice; D008822:Mice, Transgenic; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: cardiovascular magnetic resonance imaging; left ventricle; noncompaction; papillary muscles; trabeculae