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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Magnetic resonance imaging pattern variability in dysferlinopathy.

Sergey N Bardakov¹, Vadim A Tsargush¹, Pierre G Carlier²

¹S.M. Kirov Military Medical Academy, Petersburg, Russia.

Acta Myologica : Myopathies and Cardiomyopathies : Official Journal of the Mediterranean Society of Myology

|January 20, 2022

Summary

Magnetic resonance imaging (MRI) reveals varied patterns in dysferlinopathy, particularly in thigh muscles. Recognizing these diverse MRI findings aids in diagnosing this myopathy and identifying genetic causes.

Keywords:

LGMD2B LGMDR2 MRI pattern Miyoshi myopathy T2-MSME dysferlinopathy

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Area of Science:

Neurology
Radiology
Genetics

Background:

Magnetic resonance imaging (MRI) is crucial for diagnosing myopathies.
Typical MRI patterns of dysferlinopathy are established, but variability is underrecognized.

Purpose of the Study:

To investigate the variability of MRI patterns in dysferlinopathy.
To correlate MRI findings with clinical and genetic data.

Main Methods:

Analyzed MRI scans (T2, T1, STIR sequences) from 25 dysferlinopathy patients.
Performed quantitative and semi-quantitative assessments of muscle fatty replacement and edema.
Included creatine phosphokinase levels, molecular genetics, and muscle biopsy in two cases.

Main Results:

MRI pattern variability was lowest in pelvis/leg muscles and highest in thigh muscles.
Three main patterns observed: posterior-dominant (80%), anterior-dominant (16%), and diffuse (4%).
Anterior-dominant patterns included collagen-like, proximal, and pseudo-myositis variants.

Conclusions:

Awareness of atypical MRI patterns in dysferlinopathy is essential for efficient diagnostics.
Recognizing MRI variability can optimize the search for causative gene mutations.
Standardized MRI analysis can improve diagnostic accuracy for dysferlinopathy.