Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.
Department of Electrical and Computer Engineering, Texas A&M University, Doha, Qatar.
Muscle Nerve. 2019 Nov;60(5):621-628. doi: 10.1002/mus.26657. Epub 2019 Aug 28.
Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.
To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients).
The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.
MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.
金毛寻回犬肌肉萎缩症（GRMD）是一种自发性 X 连锁犬类杜氏肌肉萎缩症模型，与人类疾病状况相似。肌肉百分比指数（MPI）被提议作为 GRMD 疾病严重程度的成像生物标志物。
为了评估 MPI，我们使用一台 4.7T 小口径扫描仪从 9 个 GRMD 样本中获取 MRI 数据。我们使用机器学习方法，使用八种原始定量 MRI 数据图像（T1m、T2m、两个 Dixon 图和四个扩散张量成像图）、三种纹理描述符（局部二值模式、灰度共生矩阵、灰度游程长度矩阵）和一个梯度描述符（方向梯度直方图）。
混淆矩阵显示，所有样本中，93.5%的肌肉像素被正确分类。在留一交叉验证中进行的分类，提供了平均 80%的准确率，对于年轻（8%）和年老（20%）的狗存在高估的差异。
MPI 可能有助于量化 GRMD 的严重程度，但对于严重病例需要谨慎解释。
