Evaluation of muscle diseases using artificial neural network analysis of 31P MR spectroscopy data

✍ Scribed by Suresh Kari; Nancy J. Olsen; Jane H. Park

• Publisher: John Wiley and Sons
• Year: 1995
• Tongue: English
• Weight: 822 KB
• Volume: 34
• Category: Article
• ISSN: 0740-3194
• DOI: 10.1002/mrm.1910340504

✦ Synopsis

Abstract

Dermatomyositis is an autoimmune disease characterized by an erythematous rash and severe muscle weakness. ^31^P Magnetic resonance spectroscopy (MRS) provides quantitative data for longitudinal monitoring of disease status and responses to immunosuppressive therapy. A disease variant, amyopathic dermatomyositis, presents with a typical rash but no clinical muscle weakness. However, metabolic abnormalities in the oxidative capacity of muscles of amyopathic patients during exercise were detected with ^31^P MRS. Because MRS provided the best quantitative data for evaluating dermatomyositis, the ^31^P metabolic parameters derived from the MR spectra were further processed using an artificial neural network (XERION). The neural network analyses provided additional clinical information from the weighted correlations of multiple ^31^P parameters, namely, inorganic phosphate, phos‐phocreatine, ATP, phosphodiesters, and selected ratios. This investigation analyzes the relative importance of the various metabolic parameters for accurate patient characterization and provides insights into the pathogenesis of the disease.