基于深度学习的无监督磁共振图像去噪方法
An Unsupervised Deep Learning Method for MRI Image Denoising
DOI: 10.12677/CSA.2021.115129, PDF,    国家科技经费支持
作者: 唐 凡：成都信息工程大学，四川 成都；符 颖*：成都信息工程大学，四川 成都;四川省图形图像与空间信息2011协同创新中心，四川 成都；李 燕：重庆中烟工业有限责任公司重庆卷烟厂，重庆
关键词: MRI去噪；莱斯噪声；无监督深度学习；解缠表示；MRI Denoising； Rician Noise； Unsupervised Deep Learning； Disentangled Representations
摘要: 近年来，基于深度学习的方法在医学图像去噪方面取得了很好的表现。然而，大多数基于深度学习的方法都需要成对的训练数据，这将影响如新型冠状病毒肺炎等病症的临床诊断。本文提出了一种用于磁共振成像(magnetic resonance image，简称MRI)去噪的无监督学习方法。首先，我们通过内容编码器和随机噪声编码器分离受噪声影响的低质MRI图像的内容信息和噪声信息。其次，利用Kullback-Leibler (KL)散度损失对噪声的分布进行正则化。第三，向模型加入对抗损失，使生成的去噪图像看起来更加真实。最后，我们增加了循环一致损失和感知损失来确保带噪图像和去噪图像内容信息的一致性。实验结果表明，我们提出的方法取得了良好的视觉效果。
Abstract: Recently, medical image denoising methods based on deep learning have performed well. However, one challenge for most of these methods needs paired synthetic training data, which will affect clinic diagnosis such as COVID-19. In this paper, we proposed an unsupervised learning method for Magnetic Resonance Imaging (MRI) denoising. Firstly, we separated the content and noise of low-quality MRI images affected by noise through the content encoder and random noise encoder. Secondly, we used Kullback-Leibler (KL) loss to regularize the distribution of noise. Thirdly, we apply the adversarial loss on the model to make the denoising images look more realistic. Finally, we added cycle-consistency loss and perception loss to ensure the consistency of the noisy image and the denoised image. Experimental results showed the method we proposed achieved good visual results.
文章引用：唐凡, 符颖, 李燕. 基于深度学习的无监督磁共振图像去噪方法[J]. 计算机科学与应用, 2021, 11(5): 1268-1280. https://doi.org/10.12677/CSA.2021.115129

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