Deep learning for undersampled MRI reconstruction

TL;DR

This paper introduces a deep learning approach for accelerated MRI reconstruction using sub-Nyquist sampling, achieving high-quality images with only 29% of the original k-space data by combining uniform subsampling and low-frequency data addition.

Contribution

The study proposes a novel deep learning framework that effectively reconstructs high-quality MRI images from significantly undersampled k-space data, improving speed without sacrificing image quality.

Findings

High-quality images from 29% of k-space data

Deep learning outperforms traditional reconstruction methods

Effective handling of image folding via low-frequency data

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, 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.