Noise Estimation-based Method for MRI Denoising with Discriminative Perceptual Architecture

In this paper, we investigate the case for the discriminative perceptual architecture in magnetic resonance imaging Rician noise removal. While the existing denoising methods based on noise estimation have achieved the superior performance under the synthetic situation, it fails to remove certain re...

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Bibliographic Details
Published in2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) pp. 469 - 473
Main Authors Xu, Xiaorui, Li, Siyue, Zhao, Shutian, Yan, Taiyu, Ki, Chun, Au, Franklin, Chen, Weitian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2020
Subjects
Discriminative Perceptual Architecture
Feature extraction
Generators
Magnetic resonance imaging
MRI Denoising
Noise measurement
Noise reduction
Rician channels
Rician Noise
Training
WGAN
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Summary:In this paper, we investigate the case for the discriminative perceptual architecture in magnetic resonance imaging Rician noise removal. While the existing denoising methods based on noise estimation have achieved the superior performance under the synthetic situation, it fails to remove certain real noise and lost some texture details as the overfitting noise estimation. In order to improve the noise estimation based methods for MRI denoising, we propose to use a perceptual feature extractor to calculate the perceptual loss and the discriminator structure of WGAN to reduce the noise distribution divergence. The experiment demonstrates superiority of our proposed method in removing MRI noise and reserving good texture details.
DOI:10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00087