dMRI Reconstruction

dMRI reconstruction focuses on improving the quality and efficiency of diffusion magnetic resonance imaging data, aiming to extract more accurate and reliable information about brain microstructure and connectivity. Current research emphasizes the use of deep learning, particularly generative models like diffusion probabilistic models and neural networks (including spherical CNNs), along with techniques like low-rank plus sparse decomposition and unsupervised learning for artifact detection and parameter estimation. These advancements are crucial for enhancing the diagnostic capabilities of dMRI in neurological disease research and clinical practice, enabling more accurate and efficient analysis of large datasets.