一种用于结合dMRI和fMRI数据的新型双流框架用于dMRI束追踪分类
A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data
AI总结：
本文提出一种结合dMRI和fMRI的双流框架，用于提升白质束分区的功能一致性，通过预训练网络和辅助网络实现更精确的束分类。
摘要：
束分类对于从扩散磁共振成像（dMRI）束追踪中识别具有解剖学意义的白质束至关重要。然而，当前的束分类方法主要依赖于束轨迹的几何特征，无法区分具有相似路径的功能性不同的纤维束。为了解决这个问题，我们引入了一种新的双流束分类框架，联合分析dMRI和功能磁共振成像（fMRI）数据以增强束分区的功能一致性。我们设计了一种新的网络，利用预训练的骨干网络对完整的束轨迹进行束分类，同时辅以一个辅助网络，处理来自纤维端点区域的fMRI信号。我们通过将皮质脊髓束（CST）划分为其四个体表征子区域来展示我们的方法。通过消融研究和与最先进方法的比较，实验结果证明了我们方法的优越性能。
英文摘要：
Streamline classification is essential to identify anatomically meaningful white matter tracts from diffusion MRI (dMRI) tractography. However, current streamline classification methods rely primarily on the geometric features of the streamline trajectory, failing to distinguish between functionally distinct fiber tracts with similar pathways. To address this, we introduce a novel dual-stream streamline classification framework that jointly analyzes dMRI and functional MRI (fMRI) data to enhance the functional coherence of tract parcellation. We design a novel network that performs streamline classification using a pretrained backbone model for full streamline trajectories, while augmenting with an auxiliary network that processes fMRI signals from fiber endpoint regions. We demonstrate our method by parcellating the corticospinal tract (CST) into its four somatotopic subdivisions. Experimental results from ablation studies and comparisons with state-of-the-art methods demonstrate our approach's superior performance.
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