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

Suresh Kari, Nancy Olsen, Jane H. Park

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Dermatomyositis is an autoimmune disease characterized by an erythematous rash and severe muscle weakness. 31P 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 31P MRS. Because MRS provided the best quantitative data for evaluating dermatomyositis, the 31P 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 31P 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.

Original languageEnglish (US)
Pages (from-to)664-672
Number of pages9
JournalMagnetic Resonance in Medicine
Volume34
Issue number5
DOIs
StatePublished - Jan 1 1995

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Dermatomyositis
Magnetic Resonance Spectroscopy
Muscle Weakness
Exanthema
Muscles
Phosphocreatine
Immunosuppressive Agents
Autoimmune Diseases
Adenosine Triphosphate
Phosphates
Exercise
Therapeutics
Amyopathic dermatomyositis

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

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Evaluation of muscle diseases using artificial neural network analysis of 31P MR spectroscopy data. / Kari, Suresh; Olsen, Nancy; Park, Jane H.

In: Magnetic Resonance in Medicine, Vol. 34, No. 5, 01.01.1995, p. 664-672.

Research output: Contribution to journalArticle

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