PDF(Pubmed)
Abstract：
Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?
摘要：
弥散MRI（dMRI）流线型纤维束成像是体内评估大脑白质（WM）通路的金标准。然而,能够适应纤维束造影所需的微结构模型的高角度分辨率dMRI采集通常是耗时的，并且不是临床上常规采集的，限制纤维束造影分析的范围。为了解决这个限制，我们基于深度学习的最新进展，这些进展表明，流线传播可以直接从dMRI学习，而无需传统的模型拟合。具体来说,我们建议从T1wMRI学习流线传播器,以便在dMRI不可用时进行任意纤维束成像分析.要做到这一点，我们提出了一种在师生框架中训练的新型卷积递归神经网络（CoRNN），该框架利用T1wMRI，相关的解剖学背景，并从HumanConnectome项目获得的数据中简化内存。我们在两种常见的纤维束成像范例下描述了我们的方法，WM束分析和结构连接组学，并发现从我们的方法生成的流线计算的测量值与使用传统的dMRI纤维束成像生成的测量值之间大约有5-15％的差异。当放在文献中时，这些结果表明，用我们的方法从T1wMRI计算的WM测量的准确性是在扫描-再扫描dMRI变异性的水平上，并提出了一个重要的问题：纤维束成像是真正的微观结构现象，还是dMRI仅仅促进了它的发现和实施？
