Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.
Sci Rep. 2017 Sep 11;7(1):11141. doi: 10.1038/s41598-017-11747-3.
We assessed the test-retest reliability of high spatial resolution diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Diffusion MRI was acquired using a Siemens 3 Tesla Prisma scanner with 80 mT/m gradients and a 32-channel head coil from each of 3 concussive traumatic brain injury (cTBI) patients and 4 controls twice 0 to 24 days apart. Coefficients of variation (CoV) for DTI parameters were calculated in each DTI Studio parcellated white matter tract at 1.25 mm and 1.75 mm isotropic voxel resolution, as well as DKI parameters at 1.75 mm isotropic. Overall, fractional anisotropy had the best reliability, with mean CoV at 5% for 1.25 mm and 3.5% for 1.75 mm isotropic voxels. Mean CoV for the other DTI metrics were <7.0% for both 1.25 and 1.75 mm isotropic voxels. The mean CoV was ≤4.5% across the DKI metrics. In the commonly injured orbitofrontal and temporal pole regions CoV was <3.5% for all parameters. Thus, with appropriate processing, high spatial resolution advanced diffusion MRI has good to excellent test-retest reproducibility in both human cTBI patients and controls. However, further technical improvements will be needed to reliably discern the most subtle diffusion abnormalities, especially at high spatial resolution.
我们评估了高空间分辨率弥散张量成像(DTI)和弥散峰度成像(DKI)的测试-重测可靠性。使用西门子 3 特斯拉 Prisma 扫描仪,对 3 例震荡性创伤性脑损伤(cTBI)患者和 4 例对照者进行两次扩散 MRI 采集,两次采集间隔 0 至 24 天。在每个 DTI Studio 分割的白质束中,计算了 DTI 参数的变异系数(CoV),在 1.25mm 和 1.75mm 各向同性体素分辨率下,以及在 1.75mm 各向同性下计算了 DKI 参数的 CoV。总的来说,各向异性分数(FA)的可靠性最好,1.25mm 和 1.75mm 各向同性体素的平均 CoV 分别为 5%和 3.5%。在 1.25mm 和 1.75mm 各向同性体素中,所有其他 DTI 指标的平均 CoV 均<7.0%。DKI 指标的平均 CoV 均≤4.5%。在常见损伤的眶额和颞极区域,所有参数的 CoV 均<3.5%。因此,通过适当的处理,高空间分辨率的高级弥散 MRI 在人类 cTBI 患者和对照者中具有良好到极好的测试-重测可重复性。然而,需要进一步的技术改进来可靠地区分最细微的弥散异常,特别是在高空间分辨率下。