Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA,
Int J Comput Assist Radiol Surg. 2013 Sep;8(5):763-74. doi: 10.1007/s11548-012-0810-6. Epub 2013 Jan 9.
Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD.
Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features.
The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation.
The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.
金毛寻回犬肌肉萎缩症（GRMD）是杜氏肌营养不良症（DMD）的一种广泛应用的犬模型。最近的研究表明，磁共振成像（MRI）可用于非侵入性地检测 DMD 和 GRMD 中的一致变化。在本文中，我们提出了一种用于量化 GRMD 的 MRI 生物标志物的半自动系统。
我们的系统应用于一个包含 8 只正常犬和 10 只 GRMD 犬的 45 个 MRI 扫描的数据库，这些犬参与了一项纵向自然史研究。我们首先使用半自动的全肌肉分割方法对 6 个近端骨盆肢肌肉进行分割。然后，我们进行了预处理，包括不均匀性校正、不同图像序列的空间配准、T2 加权和 T2 加权脂肪抑制图像的强度校准以及 MRI 生物标志物图的计算。最后，对于每个分割的肌肉，我们自动测量了肌肉体积、MRI 生物标志物图上的强度统计以及统计图像纹理特征的 MRI 生物标志物。
正常犬和 GRMD 犬之间的肌肉体积和 T2 值、脂肪和水图上的平均强度存在组间差异。对于统计纹理生物标志物，正常犬和 GRMD 犬之间的直方图和游程长度矩阵特征均存在明显的组间差异。与标准的、有限的肌肉范围分割相比，全肌肉分割在提出的生物标志物中显示出明显更小的误差和变异性。
实验结果表明，该量化工具可可靠地量化 GRMD 犬的 MRI 生物标志物，表明它也可用于量化 DMD 患者的疾病进展和测量治疗效果。
