Diffusion tensor imaging (DTI) can noninvasively probe the tissue microstructure and characterize its anisotropic nature. The images carried with heavy diffusion-sensitizing gradients suffer from low SNR, and thus more than six diffusion-weighted images are required to improve the accuracy of parameter estimation against noise effect. We propose an efficient DTI model-based 3D-Unet (DTI-Unet) to predict high-quality diffusion tensor field and non-diffusion-weighted image from the noisy input. In our model, the input contains only six diffusion-weighted volumes and one b0 volume. Compared with the state-of-the-art denoising algorithms (MPPCA, GLHOSVD), our model performs better in image denoising and parameter estimation.
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