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.

目的：神经肌肉疾病（NMD）经常导致严重的残疾。基于磁共振成像（MRI）的受累肌肉中所谓的脂肪分数（FF）的计算最近被描述为监测NMD进展的可靠生物标记。由于当前正在接受深入研究的新近获得的现代基因疗法可能至少减轻了这些严重致残的疾病，因此，这引起了人们的极大兴趣。在这项高级图像分析的回顾性研究中，研究了FF在可能患有各种NMD的患者下肢中的应用扩展潜力。

方法： 由于已证实的NMD（肌萎缩性侧索硬化[ñ = 6]，脊髓球肌萎缩[ñ = 4]，肢带肌营养不良[ñ = 5]，代谢性肌病[ñ = 2]）与没有NMD的患者比较ñ= 9]。FF和从高级图像分析中产生的新参数以及基于MRI特征的标准化矩阵的生成与使用MRC量表（肌肉强度医学研究委员会）获得的临床强度等级相关。FF仅显示肌肉中的脂肪分配，而高级图像分析则考虑了完整的MR图像信息。在这里，采用主成分分析（PCA）和独立成分分析（ICA）来导出描述更详细获得的完整数据的参数。

结果：基于PCA和ICA的全图像参数仍与FF密切相关（Spearman系数0.96-0.59），但通常显示与下肢的MRC评分更强的相关性（Spearman系数； FF = −0.71； PCA和ICA参数= − 0.76-0.78）。到目前为止，年龄在全图像评估中没有明显的混杂因素。

结论： 在NMD中提出的高级图像分析在技术上是可行的，并且似乎有效地扩展了FF的信息。