融合dMRI与fMRI数据，提升白质纤维束功能分区精度
A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data
- 双流架构：分别处理纤维轨迹几何特征与末端功能信号
- 在皮质脊髓束分型中实现更优的功能一致性划分
- 适合神经解剖与脑功能研究者参考
纤维束分类对从扩散磁共振成像（dMRI）轨迹重建中识别解剖上有意义的白质通路至关重要。然而，现有方法主要依赖轨迹的几何特征，难以区分路径相似但功能不同的纤维束。为此，我们提出一种新型双流分类框架，联合分析dMRI与功能磁共振成像（fMRI）数据，以增强纤维束分割的功能一致性。设计了一种基于预训练主干网络的全轨迹分类模型，并引入辅助网络处理纤维末端区域的fMRI信号。通过将皮质脊髓束（CST）划分为四个体感拓扑亚区，实验表明该方法在消融研究和与前沿方法的对比中均表现更优。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。