Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Zero-shot learning
Motivation: Low-field MRI is constrained by the physical factors of detection equipment, facing issues such as noise that degrade image quality and affect disease diagnosis.
Goal(s): This study aims to denoise low-field magnetic resonance images using deep learning techniques.
Approach: This paper proposes a dual-stage denoising method based on zero-shot learning: the first stage uses a supervised deep learning method for denoising, while the second stage employs a zero-shot denoising method.
Results: Results demonstrate that the dual-stage denoising method outperforms both the supervised method and the zero-shot denoising method when applied individually, effectively achieving an improvement in the quality of low-field magnetic resonance images.
Impact: The framework of our proposed dual-stage denoising method is plug-and-play for various existing denoising models and generally enhances their performance.
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