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Abstract #4209

Assessment of Charcot-Marie-Tooth disease severity using quantitative T2 and fat fraction biomarkers

Maria Holodov1, Shimon Shahar2, Tamar Blumenfeld-Katzir1, David Bendahan3, and Noam Ben-Eliezer1,4,5
1Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Center of AI and Data Science (TAD), Tel Aviv University, Tel Aviv, Israel, 3CNRS, CRMBM, Aix Marseille University, Marseille, France, 4Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 5Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States

Synopsis

Keywords: Muscle, Muscle, Muscular disorder; Neuropathy; Fat water separation

Charcot-Marie-Tooth (CMT) is the most prevalent inherited neurological disease, commonly presenting in the first or second decade of life. Notwithstanding the ongoing advancement of non-invasive imaging, effective and sensitive tools for monitoring CMT and other neuromuscular conditions are lacking. Here we investigated the utility of the extended echo-modulation-curve (EMC) algorithm, for precise mapping of T2 relaxation times and fat fraction (FF) in CMT patients. Results indicate that quantitative T2 and FF biomarkers correlate with clinical scores. These can be useful for precise and objective monitoring of microstructural processes occurring in CMT.

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Keywords