Deep learning for undersampled MRI reconstruction. (25th June 2018) - Hyun, Chang Min; Kim, Hwa Pyung; Lee, Sung Min; Lee, Sungchul; Seo, Jin Keun
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.