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Abstract #2842

Mitigation of noise floor in diffusion MRI using deep learning

Hu Cheng1, Jian Wang2, Sophia Vinci-Booher1, Qiuting Wen3, and Sharlene Newman4
1Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States, 2Shandong Normal University, Jinan, China, 3Indiana University, Indianapolis, IN, United States, 4University of Alabama, Tuscaloosa, AL, United States


Rectified noise floor is a challenging problem for high-resolution diffusion MRI, which cannot be tackled by current denoising methods. We propose a simple deep learning method for correcting noise floor in diffusion MRI. The method is based on 1D CNN model and works on voxel-wise time courses. Therefore, even one dataset of the brain has sufficient number of samples for training, which is a big advantage for practical application. Both simulation and in vivo results show that the method is robust in mitigating the noise floor artifact and restore the true values of diffusion metrics.

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