Systems and methods for deep learning regularized reconstruction of under sampled magnetic resonance imaging data

Abstract

Embodiments of the present disclosure relate to systems and methods for reconstructing magnetic resonance imaging (MRI) images from under sampled k-space data. The method includes acquiring multi-coil under sampled k-space data with a fully-sampled calibration region, determining a k-space convolution kernel, and producing an initial multi-coil MRI image estimate via inverse Fourier transform. The estimate is regularized using a trained deep learning regularizer, transformed to synthetic fully-sampled k-space data, and convolved with the kernel to produce data-consistent synthetic k-space data. This is combined with the original under sampled data to produce an estimated fully-sampled k-space data, which is then inverse transformed to update the multi-coil MRI image estimate. The process iteratively improves image quality, reduces noise, and preserves features, enhancing MRI reconstruction from under sampled data.