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Abstract #4337

Efficient six-direction DTI tensor estimation using model-based deep learning

Jialong Li1, Qiqi Lu1, Qiang Liu1, Yanqiu Feng1, and Xinyuan Zhang1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China


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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