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Abstract #3833

Unsupervised Denoising Method for Multi-sequence Low-field MRI in Veterinary Imaging

Jinglei Tang1, Weitao Zu1, Jie Liu2, Cheng Jin1, and Zhiyong Zhang1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Ningbo Chuanshanjia Electrical and Mechanical Company, Ningbo, China

Synopsis

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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