用Transformer和CNN融合路径与局部信息，提升脑白质纤维追踪精度。
TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks
- 结合Transformer建模纤维路径序列，融合上下文与当前测量。
- 利用CNN提取每个体素邻域的微结构特征，增强空间感知。
- 在真实数据上表现优异，适合神经影像与脑网络研究者使用。
白质纤维追踪是基于扩散MRI重建大脑白质三维通路的先进神经影像技术，可视为从噪声大且模糊的测量中推断神经纤维轨迹的路径规划问题，面临交叉、汇聚和扇形等复杂结构挑战。本文提出一种新型追踪方法，利用Transformer建模白质纤维流线的序列特性，通过整合轨迹上下文与当前扩散MRI测量，预测纤维方向。为引入空间信息，采用卷积神经网络（CNN）从每个体素周围的局部邻域提取微结构特征。通过融合这两种互补信息，本方法相比传统模型显著提升了神经通路映射的精确性与完整性。我们在Tractometer工具包上评估了该方法，在性能上达到当前最优水平，并在TractoInferno数据集上展示了对真实数据的强大泛化能力。
原文摘要 · Abstract (English)
White matter tractography is an advanced neuroimaging technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data. It can be framed as a pathfinding problem aiming to infer neural fiber trajectories from noisy and ambiguous measurements, facing challenges such as crossing, merging, and fanning white-matter configurations. In this paper, we propose a novel tractography method that leverages Transformers to model the sequential nature of white matter streamlines, enabling the prediction of fiber directions by integrating both the trajectory context and current diffusion MRI measurements. To incorporate spatial information, we utilize CNNs that extract microstructural features from local neighborhoods around each voxel. By combining these complementary sources of information, our approach improves the precision and completeness of neural pathway mapping compared to traditional tractography models. We evaluate our method with the Tractometer toolkit, achieving competitive performance against state-of-the-art approaches, and present qualitative results on the TractoInferno dataset, demonstrating strong generalization to real-world data.
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