纹理作为金贵犬肌肉营养不良疾病严重程度的成像生物标志物。
Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy.
机构信息
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar.
出版信息
Muscle Nerve. 2019 Mar;59(3):380-386. doi: 10.1002/mus.26386. Epub 2019 Jan 13.
INTRODUCTION
Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD.
METHODS
To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines.
RESULTS
A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort.
CONCLUSIONS
The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380-386, 2019.
简介
金毛猎犬肌营养不良症（GRMD）是一种 X 连锁隐性疾病，其表型特征与杜氏肌营养不良症（DMD）相似。目前需要一种定量且可重复的方法来监测 GRMD 和 DMD 的疾病进展。
方法
为了评估 GRMD 的严重程度，我们使用 5 种特征提取方法从多参数 MRI（T1w、T2w、T1m、T2m 和 Dixon 图像）中提取纹理特征，并使用支持向量机进行分类。
结果
定性图像中的单个特征可提供 89%的最大准确性。此外，T1w、T2m 或 Dixon 图像中的 2 个特征可提供最高的准确性。在考虑扫描时间和计算复杂度之间的折衷时，T2m 图像在较低的采集和处理时间和精力下提供了良好的准确性。
结论
MRI 纹理特征的组合提高了 GRMD 疾病进展评估的分类准确性，并评估了骨骼肌的异质性，反映了组织病理学变化。肌肉神经 59:380-386, 2019。
Zotero 插件