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Abstract #3676

Perceptual Noise-Estimation based Method for MRI denoising with deep learning

Xiaorui Xu1, Siyue Li1, Shutian Zhao1, Chun Ki Franklin Au1, and Weitian Chen1
1CUHK lab of AI in radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong

Most of the methods in MRI denoising derive denoised images from corrupted images directly. DnCNN is a network that is used to remove Gaussian noise from natural images. The noise distribution in MRI images are often non-Gaussian due to the latest development of reconstruction algorithm and MRI hardware. In this work, we investigated the case when the noise follows Racian distribution. We utilized the idea of DnCNN and combine it with a perceptual architecture to remove Rician noise of MRI images. We demonstrated this method can generate ideal clean images.

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