TractoTransformer：使用CNN和Transformer网络的扩散MRI流线纤维束成像
TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks
AI总结：
本文提出TractoTransformer，结合CNN提取局部微结构特征与Transformer建模白质流线序列，提升扩散MRI纤维束成像的精度和泛化能力，并在多个数据集上验证了其有效性。
摘要：
白质纤维束成像是一种先进的神经影像技术，可从扩散MRI数据中重建大脑的三维白质通路。它可以被表述为一个寻路问题，旨在从噪声和模糊的测量中推断神经纤维轨迹，面临交叉、合并和扇形白质构型等挑战。在本文中，我们提出了一种新颖的纤维束成像方法，利用Transformer对白质流线的序列性质进行建模，通过整合轨迹上下文和当前扩散MRI测量来预测纤维方向。为了融入空间信息，我们利用CNN从每个体素周围的局部邻域中提取微结构特征。通过结合这些互补的信息来源，与传统纤维束成像模型相比，我们的方法提高了神经通路映射的精度和完整性。我们使用Tractometer工具包评估了我们的方法，取得了与最先进方法相比具有竞争力的性能，并在TractoInferno数据集上展示了定性结果，证明了对真实世界数据的强大泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
