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Abstract #3857

A Dual-Stage Denoising Method Based on Zero-Shot Learning for Low-Field MRI

Yi Li1,2, Shaojun Liu1,2, Yilong Liu3, and Mengye Lyu1,2
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, 2College of Applied Sciences, Shenzhen University, Shenzhen, China, 3Guangdong-Hongkong-Macau Institute of CNS Regeneration, Key Laboratory of CNS Regeneration (Ministry of Education), Jinan University, Guangzhou, China

Synopsis

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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Keywords