2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-BSIP.2
Paper Title IMPROVING IMAGE QUALITY IN LOW-FIELD MRI WITH DEEP LEARNING
Authors Armando Garcia Hernandez, AIx-Marseille université, France; Pierre Fau, Institut Paoli-Calmettes, France; Stanislas Rapacchi, Julien Wojak, Aix-Marseille Université, France; Hugues Mailleux, Mohamed Benkreira, Institut Paoli-Calmettes, France; Mouloud Adel, Aix-Marseille Université, France
SessionMLR-APPL-BSIP: Machine learning for biomedical signal and image processing
LocationArea C
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for biomedical signal and image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.