Rapid High-resolution Whole-brain 3D Multi-parametric and Multi-contrast MRI with Deep Learning-based Acquisition & Reconstruction

Motivation: As the public demand for MRI grows exponentially, there is an increasing need for a one-click 3D MR exam that can generate multiple image contrasts and parametric maps as an effective way to improve patient throughput. Goal(s): Our goal was to implement an acquisition and reconstruction method that makes high-resolution whole brain multi contrast examination possible in less than 3 minutes. Approach: We implemented a deep learning-guided vast undersampled MR acquisition and a time efficient recon algorithm that uses a densely connected unrolled neural network. Results: Our proposed method preserved image quality and quantitative accuracy of the multicontrast and multiparametric images. Impact: This study demonstrates that with highly undersampled 3D QALAS acquisition combined with the DL recon algorithm, a 3-minute one-click exam is feasible that generates whole-brain high-resolution brain volumes of multiple contrasts and quantitative maps, which can enhance patient workflow in a busy clinical practice.