Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods.
Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods.
复制标题
DOI：
10.1016/j.nicl.2013.08.008
复制
发表时间：
2013
影响因子：
4.2
通讯作者：
Henry, Roland G.
中科院分区：
文献类型：
--
作者：
Bucci, Monica;Mandelli, Maria Luisa;Berman, Jeffrey I.;Amirbekian, Bagrat;Nguyen, Christopher;Berger, Mitchel S.;Henry, Roland G.
关键词：
弥散MRI束状图越来越多地用于描绘体内白质通路，其中主要的临床应用是手术前绘制流利区域。然而，由于缺乏金标准，很少有机会量化这些方法在体内描述白质纤维通路的准确性或灵敏度。术中电刺激（IES）为功能性运动通路的定位和存在提供了黄金标准，可用于确定纤维跟踪算法的准确性和灵敏度。在这项研究中，我们使用脑肿瘤患者的术中刺激作为金标准来评估扩散张量MRI （DTI）和扩散q球模型的敏感性和准确性，并采用确定性和概率纤维跟踪算法来描绘运动通路。我们使用了术前高角分辨率弥散MRI （HARDI）数据（55个方向，b = 2000 s/mm2），这些数据是在临床可行的时间框架内获得的，来自12例接受开颅手术切除脑胶质瘤的患者。使用DTI和q-ball模型采用确定性和概率算法描绘皮质脊髓纤维束。我们使用皮质和白质IES位点作为功能性运动通路存在和定位的金标准。灵敏度定义为基于皮质IES刺激部位描绘纤维通路的真阳性率。为了纤维束运动的准确性和精确性，我们测量了皮层下刺激点与纤维束造影结果之间的距离。通过比较皮质下IES运动功能（上肢、下肢、面部）与运动皮质束图通路的连接来评估所描绘束的阳性预测率。我们通过术中运动通路作图获得了21个皮层和8个皮层下IES位点。与确定性q-ball（50%）、概率DTI（36%）和确定性DTI（10%）相比，从皮质IES中确定的概率q-ball具有最佳的灵敏度（79%）。q-ball算法的敏感性（65%）显著高于DTI算法（23%）（p < 0.001），概率算法（58%）比确定性方法（30%）更敏感（p = 0.003）。概率q球纤维轨迹与皮层下刺激部位的偏移最小。当弥散纤维径迹明显比预期薄时，弥散纤维径迹与皮质下IES位点之间的偏移量显著增加。皮层下IES功能（如手部刺激）与最近的弥散纤维通路（如上肢皮层）的皮层连接有很好的一致性。本研究强调了术中刺激部位的巨大效用，为评估弥散MRI纤维跟踪方法提供了一个金标准，并为评估不同的扩散模型和纤维跟踪方法提供了一个对象标准。概率q球纤维束状图在通过白质的过程的灵敏度和准确性方面明显优于DTI方法。在我们的研究中，常用的DTI纤维跟踪方法在描绘皮质脊髓束外侧方面显示出非常低的灵敏度（对于确定性DTI纤维跟踪低至10%）。肿瘤/水肿的影响导致皮层下IES与术前纤维轨迹之间明显较大的偏移。所提供的数据表明，概率hadi牵引道成像是最客观和可重复的分析，但考虑到样本小和刺激点的数量，对我们的结果进行概括时应谨慎。事实上，我们的研究结果说明了术前弥散纤维跟踪的能力，并表明在做出术前和术中管理决策时应谨慎使用这些数据。弥散性磁共振神经束造影用于手术前脑部制图。我们使用术中电刺激作为金标准。我们用确定性和概率DTI和q球模型描述运动束。概率q球的灵敏度最高（79%）。概率q球纤维轨迹与皮质下IES的偏移最小。
Diffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways. We used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm2) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex. We obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex). This study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions. Diffusion MRI tractography is used for presurgical brain mapping. We use intraoperative electric stimulation as a gold standard. We delineate motor tracts with deterministic and probabilistic DTI and q-ball models. Probabilistic q-ball has the best sensitivity (79%). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical IES.
登录
查看更多内容
影响因子：
3.3
作者：
Hess, Christopher P.;Mukherjee, Pratik;Vigneron, Daniel B.
通讯作者：
Vigneron, Daniel B.
影响因子：
5.7
作者：
Clark, CA;Barrick, TR;Bell, BA
通讯作者：
Bell, BA
影响因子：
5.7
作者：
Stadlbauer, Andreas;Nimsky, Christopher;Ganslandt, Oliver
通讯作者：
Ganslandt, Oliver
影响因子：
3.3
作者：
Jones, DK;Pierpaoli, C
通讯作者：
Pierpaoli, C
影响因子：
5.7
作者：
Bello, Lorenzo;Gambini, Anna;Falini, Andrea
通讯作者：
Falini, Andrea