TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks
This work addresses the challenge of noisy and ambiguous measurements in neuroimaging for researchers and clinicians, representing an incremental improvement over existing methods.
The paper tackles the problem of reconstructing white matter pathways from diffusion MRI data by proposing a tractography method that combines Transformers for sequential modeling and CNNs for spatial feature extraction, achieving competitive performance on the Tractometer toolkit and strong generalization on the TractoInferno dataset.
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.