Axonal loss determines the final disability in
patients with peripheral neuropathies. Consequently, axonal loss results in
intramuscular fat accumulation. Therefore, measuring muscle fat fraction
through Dixon MRI has been a promising biomarker for monitoring disease
progression. However, the responsiveness is yet to be improved, particularly in
the early phase of the disease. In this study, we developed a deep learning-based
method to automate the quantification of individual muscle fat fraction, which mitigates
the laborious manual segmentations and enables the use of individual muscle fat
fraction as outcome measures to track axonal loss in patients with neuropathies.
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