Diffusion Tensor Estimation Using Model-Based Deep Learning

Diffusion tensor imaging (DTI) is widely used in clinical applications and neuroscience. Its practical utility is limited by the need for multiple scans. Here, we integrate deep learning and model-based optimization methods to estimate diffusion tensor using only one non-diffusion-weighted images and six diffusion-weighted images. The data fidelity term is the weighted linear least squares fitting (WLLS) and the regularization term is Regularization by Denoising (RED). The Alternating Direction Method of Multiplier (ADMM) is adopted to iteratively optimize the model. Experiment results demonstrate that the proposed model-based strategy has great potential to improve the accuracy of diffusion tensor estimation.