Review

Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis?

¹ Institute of Myology, NMR laboratory, Paris, France
² CEA, I2BM, MIRCen, NMR laboratory, Paris, France
³ INRA, UMR 703 PAnTher, Oniris, Nantes, France
⁴ Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
⁵ PRISM-BioSCANs, University of Rennes, Rennes, France
⁶ Instituto de Biomedicina, CSCIC Universidad de Sevilla, Sevilla, Spain
⁷ University da Beira Interior, Covilha, Portugal
⁸ LIST, Southeast University, Nanjing, China
⁹ LTSI, INSERM, University of Rennes, Rennes, France
¹⁰ Lab. Of Mathematical Cybernetics, UIIP, Belorussian Academy of Science, Minsk, Belarus
¹¹ Ninewells Hosp. and Medical School, Dundee, Scotland, United Kingdom

^* e-mail: jacques.de-certaines@orange.fr

Received: 12 December 2014
Accepted: 18 February 2015
Published online: 19 March 2015

Abstract

Several previous clinical or preclinical studies using computerized texture analysis of MR Images have demonstrated much more clinical discrimination than visual image analysis by the radiologist. In muscular dystrophy, a discriminating power has been already demonstrated with various methods of texture analysis of magnetic resonance images (MRI-TA). Unfortunately, a scale gap exists between the spatial resolutions of histological and MR images making a direct correlation impossible. Furthermore, the effect of the various histological modifications on the grey level of each pixel is complex and cannot be easily analyzed. Consequently, clinicians will not accept the use of MRI-TA in routine practice if TA remains a “black box” without clinical correspondence at a tissue level. A goal therefore of the multicenter European COST action MYO-MRI is to optimize MRI-TA methods in muscular dystrophy and to elucidate the histological meaning of MRI textures.