Keywords: Analysis/Processing, Low-Field MRI, Unsupervised, Denoising
Motivation: The low signal-to-noise ratio (SNR) of low-field magnetic resonance images affects the diagnosis of relevant diseases and limits the widespread adoption of low-field MRI systems.
Goal(s): We seek to improve the SNR of low field images using unsupervised learning without reference images.
Approach: Using an improved ResNet model as the framework, we designed a trainable Sobel convolutional block for shallow edge feature extraction and introduced a convolutional attention module to extract deep edge features from the shallow edge feature map.
Results: This network was tested for denoising on 0.4T veterinary images to demonstrate effective denoising while preserving the underlying image structure.
Impact: In the absence of reference images, our method preserves the structural features of images while effectively reducing noise. It achieve noise reduction for low-field images in a shorter time and has great potential for improving low-field image quality improvement.
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