Abstract
Low-field magnetic resonance (MR) images suffer from inherent low Signal-to-noise ratio (SNR) compared to images acquired using high-field MRI scanners. Denoising these images could help and improve further processing, such as image segmentation. In this paper a Convolutional Neural Network AutoEncoder was designed with a dedicated loss function for noise reduction. A transfer learning approach was employed in which high-field high-SNR MR images, served as targets for learning from their noise-added counterparts. Evaluation of network performances was measured on both noisy high-field and low-field MR images that had not been included in the learning step. The proposed method outperformed major denoising methods applied to MR images. SNR improvements were quantified on low-field MR images.
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