A Self-Guided Deep Learning Technique for MRI Image Noise Reduction
DOI:
https://doi.org/10.53469/jtpes.2024.04(01).15Keywords:
MRI Denoising, Rician Noise, Unsupervised Deep Learning, Disentangled RepresentationsAbstract
In recent years, methods founded on deep learning have exhibited notable efficacy within the field of medical image denoising. However, the majority of deep learning approaches typically require paired training data, which poses challenges for clinical diagnoses of conditions such as novel coronavirus pneumonia. This paper introduces an unsupervised learning methodology for denoising magnetic resonance images (MRI). Firstly, we employ content encoders and random noise encoders to separate the content information and noise artifacts from low-quality MRI images affected by noise. Secondly, we regularize the noise distribution through Kullback-Leibler (KL) divergence loss. Thirdly, an adversarial loss is incorporated into the model to augment the realism of the generated denoised images. Finally, we incorporate cycle consistency and perceptual losses to ensure the coherence of content information between noisy input and denoised output images. The effectiveness of the proposed approach is substantiated by experimental results, showcasing significant enhancements in visual quality.
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Copyright (c) 2024 Xu Yan, MingXuan Xiao, Weimin Wang, Yufeng Li, Fei Zhang
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