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

从商业可用协议中学习扩散磁共振：将先进的纤维束成像引入常规神经外科实践

• 批准号: 2125385
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2125385
• 关键词: Learning; diffusion; MR; commercially; available

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.

弥散磁共振成像（dMRI）纤维束成像的准确定位可以通过识别应保留以避免功能缺陷的白色物质（WM）束来辅助手术计划。然而，仍然存在限制临床环境中的最佳纤维束成像的缺点。首先，信号建模取决于采集参数，包括信噪比（SNR）、信号幅度（b值）和用于高质量局部纤维取向建模的扩散加权梯度（b-vec）的最小数量。然而，采集时间有限，商业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的不同复杂纤维配置方面具有固有的未解决的挑战，其中轴突可以交叉、接触、弯曲或散开。第三，在纤维追踪算法中存在关于纤维束位置的可变性，这通常不被考虑在内。大多数最先进的纤维束成像方法不估计WM路径的不确定性，并且通常执行后处理用户引导的滤波以消除伪流线。在这个博士项目，我的目标是开发一种端到端的方法，以确保临床获得的dMRI的最佳纤维束成像，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束不确定性测量来实现这一点。为此，我将：1）开发卷积神经网络（CNN）方法，以改善商用dMRI模型拟合; 2）计算纤维束成像不确定性量化; 3）结合纤维束成像特定功能，以改善模型拟合并验证神经胶质瘤患者的管道。