Deep learning for undersampled MRI reconstruction
This addresses the problem of long scan times in MRI for medical imaging, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles faster MRI by using deep learning to reconstruct high-quality images from undersampled k-space data, achieving results comparable to standard reconstruction with only 29% of the data.
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, very few low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of Fourier transforms of the subsampled and fully sampled k-space data. Numerous experiments show the remarkable performance of the proposed method; only 29% of k-space data can generate images of high quality as effectively as standard MRI reconstruction with fully sampled data.