Deep learning for undersampled MRI reconstruction. (25th June 2018)

• Record Type: Journal Article
• Authors: Hyun, Chang Min; Kim, Hwa Pyung; Lee, Sung Min; Lee, Sungchul; Seo, Jin Keun
• Is Part Of: Physics in medicine & biology. Volume 63:Number 13(2018:Jul.)
• Publication Date: 2018-06-25
• Subjects: magnetic resonance imaging -- undersampling -- deep learning -- fast MRI; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aac71a
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k -space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k -space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k -space data. Our experiments show the remarkable performance of the proposed method; only 29 of the k -space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.