Learning diffusion MR from commercially available protocols: bringing advanced tractography into routine neurosurgical practice

扩散磁共振成像（dMRI）的精确定位可以通过识别应保留的白质束（WM）来帮助手术计划，以避免功能缺陷。然而，仍有缺点限制了临床上最佳的肛管造影。首先，信号建模依赖于采集参数，包括信噪比（SNR）、信号幅度（b值）和最小扩散加权梯度（b-vecs），以实现高质量的局部光纤定向建模。然而，采集时间有限，商用扫描仪可能无法提供强大的最先进的dMRI采集。其次，纤维跟踪方法在如何区分体素内不同的复杂纤维结构方面存在固有的未解决的挑战，其中轴突可以交叉，亲吻，弯曲或扇形。第三，光纤跟踪算法在通常不考虑的信道位置方面存在可变性。大多数最先进的轨迹成像方法不估计WM路径的不确定性，并且通常执行后处理用户引导滤波以消除虚假流线。在这个博士项目中，我的目标是开发一种端到端的方法，以确保临床获得性dMRI的最佳束状图，从而在神经外科手术中提供更准确的指导。我将通过提高从商业dMRI采集中恢复复杂局部纤维方向的能力，并使用深度学习为术前指导提供一个不确定性测量来实现这一目标。为了实现这一目标，我将：1)开发卷积神经网络（CNN）方法来改进商业mri模型拟合；2)计算神经束造影不确定度量化；3)结合神经束造影特异性特征，改进模型拟合，验证胶质瘤患者的管道。

Accurate localization of diffusion magnetic resonance imaging (dMRI) tractography can aid surgical planning by identifying white matter (WM) tracts that should be preserved to avoid functional deficits. However, there are still shortcomings that limit optimal tractography in a clinical setting. Firstly, signal modeling depends on acquisition parameters including signal-to-noise ratio (SNR), signal magnitude (b-values), and the minimum number of diffusion-weighting gradients (b-vecs) for high-quality local fiber orientation modeling. However, acquisition time is limited, and commercial scanners may not provide robust state-of-the-art dMRI acquisitions. Secondly, fiber tracking methods have intrinsic unresolved challenges in how to distinguish between different complex fiber configurations within a voxel, where axons can cross, kiss, bend, or fan out. Thirdly, there is variability in the fiber tracking algorithms with regards to tracts locations that usually are not taken into account. Most of the state-of-the-art tractography approaches do not estimate the uncertainty of WM pathways, and post-processing user-guided filtering is often performed to eliminate spurious streamlines. In this Ph.D. project, I aim to develop an end-to-end approach to ensure optimal tractography for clinically acquired dMRI and thereby provide more accurate guidance during neurosurgery. I will do that by improving the ability to recover complex local fiber orientations from commercial dMRI acquisitions and providing a tract uncertainty measure for preoperative guidance using deep learning. To achieve this I will: 1) Develop a convolutional neural network (CNN) approach to improve commercialdMRI model fitting; 2) Compute tractography uncertainty quantification, 3) Incorporate tractography specific features to improve model fitting and validate pipeline on patients with gliomas.