Advanced Analysis of the Water/Fat Distribution in Skeletal Muscle Tissue Using Magnetic Resonance Imaging in Patients With Neuromuscular Disease - Nasel, Christian; Klickovic, Uros; Cetin, Hakan; Struhal, Walter; Moser, Ewald (Frontiers in Physics, 2020)
Purpose: Neuromuscular diseases (NMDs) frequently cause severe disabilities. Magnetic resonance imaging (MRI)–based calculation of the so-called fat fraction (FF) in affected muscles was recently described as a reliable biomarker for monitoring progression of NMDs. This is of high interest as newly available modern gene therapies, currently subject to intensive investigations, may provide at least palliation of these severely disabling diseases. In this retrospective study feasibility of advanced image analysis, potentially extending the application of FF in lower limbs in patients suffering various NMDs was investigated. Methods: Patients receiving MRI due to manifestation of proven NMDs (amyotrophic lateral sclerosis [n = 6], spinobulbar muscular atrophy [n = 4], limb girdle muscular dystrophy [n = 5], metabolic myopathy [n = 2]) in lower limbs were compared to patients without NMD [n = 9]. FF and new parameters derived from an advanced image analysis with generation of standardized MRI feature–based matrices were correlated with clinical grades of strength obtained using the MRC scale (Medical Research Council for Muscle Strength). While FF displays the fat partition in muscles only, the advanced image analysis considers the full MR-image information. Here, principal (PCA) and independent component analyses (ICA) were employed to derive parameters describing the full data obtained in more detail. Results: PCA- and ICA-based full-image parameters remained strongly correlated with FF (Spearman coefficient 0.96–0.59), but generally showed stronger correlations with the MRC score in lower limbs (Spearman coefficient; FF = −0.71; PCA & ICA parameters = −0.76–0.78). So far, age was no significant confounder in full-image assessment. Conclusion: The proposed advanced image analysis in NMDs is technically feasible and seems to effectively extend the information of FF.