Department of Neurosurgery, Goet-he University Hospital, Frankfurt am Main, Germany.
Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany.
Oper Neurosurg (Hagerstown). 2019 Feb 1;16(2):197-210. doi: 10.1093/ons/opy062.
Tractography is a popular tool for visualizing the corticospinal tract (CST). However, results may be influenced by numerous variables, eg, the selection of seeding regions of interests (ROIs) or the chosen tracking algorithm.
To compare different variable sets by correlating tractography results with intraoperative subcortical stimulation of the CST, correcting intraoperative brain shift by the use of intraoperative MRI.
Seeding ROIs were created by means of motor cortex segmentation, functional MRI (fMRI), and navigated transcranial magnetic stimulation (nTMS). Based on these ROIs, tractography was run for each patient using a deterministic and a probabilistic algorithm. Tractographies were processed on pre- and postoperatively acquired data.
Using a linear mixed effects statistical model, best correlation between subcortical stimulation intensity and the distance between tractography and stimulation sites was achieved by using the segmented motor cortex as seeding ROI and applying the probabilistic algorithm on preoperatively acquired imaging sequences. Tractographies based on fMRI or nTMS results differed very little, but with enlargement of positive nTMS sites the stimulation-distance correlation of nTMS-based tractography improved.
Our results underline that the use of tractography demands for careful interpretation of its virtual results by considering all influencing variables.
示踪技术是可视化皮质脊髓束(CST)的常用工具。然而,结果可能受到许多变量的影响,例如,感兴趣区(ROI)种子区域的选择或所选择的跟踪算法。
通过将示踪技术结果与 CST 的术中皮质下刺激相关联,使用术中 MRI 校正术中脑移位,比较不同变量集。
通过运动皮层分割、功能磁共振成像(fMRI)和导航经颅磁刺激(nTMS)创建种子 ROI。基于这些 ROI,使用确定性和概率算法为每位患者运行示踪技术。对术前和术后获得的数据进行示踪技术处理。
使用线性混合效应统计模型,使用分割的运动皮层作为种子 ROI 并应用于术前获得的成像序列上的概率算法,可实现皮质下刺激强度与示踪技术和刺激部位之间距离之间的最佳相关性。基于 fMRI 或 nTMS 结果的示踪技术差异非常小,但随着阳性 nTMS 部位的扩大,基于 nTMS 的示踪技术的刺激距离相关性得到改善。
我们的结果强调,在考虑所有影响变量的情况下,对示踪技术的虚拟结果进行仔细解释是非常重要的。