A Deep Diffusion Model Approach for Diffusion MRI White Matter Fiber Tractography

Motivation: Traditional tractography methods rely on complex physical models and tracking algorithms, making them computationally intensive and sensitive to data quality. Machine learning-based methods have gained interest but they often fail to estimate local fiber orientations accurately. Goal(s): We propose a novel tractography method using a deep diffusion model to enhance fiber orientation estimation accuracy while improving computational efficiency. Approach: Our method contains convolutional networks to capture local spatial features, recurrent networks to model high-order dependencies along the streamline points, and a diffusion model to estimate the local fiber orientation. Results: We demonstrate our method's superior performance on phantom and in-vivo datasets. Impact: The proposed diffusion model-based method, integrating local neighborhood information and sequential dependencies, directly achieves local fiber orientation estimation and tractography. Tested on in silico and in vivo datasets, we demonstrate competitive performance against current methods.