FastCod：弥散成像中的快速脑连接
FastCod: Fast Brain Connectivity in Diffusion Imaging
摘要：
磁共振弥散加权成像（DW-MRI）的连通性信息在研究大脑皮层下灰质结构中起着重要作用。然而，由于计算每个体素到每个其他体素（或多个ROI）的连接性的$O(N^2)$复杂性，提取连接性信息的当前实践是非常低效的。这使得高分辨率图像的处理和人口一级的分析在计算上要求很高。为了解决这个问题，我们提出了一种更有效的方法来提取连接信息;简单地说，我们认为如果白质纤维流线通过它们，则两个区域/体素将被连接-无论流线起源于何处。我们认为丘脑parcellation任务的演示目的，我们的实验表明，我们的方法带来了30至120倍的速度比传统的方法具有可比的定性parcellation结果。我们还证明了在我们的框架中，高分辨率连接特征可以从低分辨率DW-MRI中超分辨。这两项创新共同实现了DW-MRI的更高分辨率连接分析。我们的源代码可以在jasonbian97.github.io/fastcod上找到。
英文摘要：
Connectivity information derived from diffusion-weighted magnetic resonance images~(DW-MRIs) plays an important role in studying human subcortical gray matter structures. However, due to the $O(N^2)$ complexity of computing the connectivity of each voxel to every other voxel (or multiple ROIs), the current practice of extracting connectivity information is highly inefficient. This makes the processing of high-resolution images and population-level analyses very computationally demanding. To address this issue, we propose a more efficient way to extract connectivity information; briefly, we consider two regions/voxels to be connected if a white matter fiber streamline passes through them -- no matter where the streamline originates. We consider the thalamus parcellation task for demonstration purposes; our experiments show that our approach brings a 30 to 120 times speedup over traditional approaches with comparable qualitative parcellation results. We also demonstrate high-resolution connectivity features can be super-resolved from low-resolution DW-MRI in our framework. Together, these two innovations enable higher resolution connectivity analysis from DW-MRI. Our source code is availible at jasonbian97.github.io/fastcod.
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