Keywords: Synthetic MR, Neuro, Multi-Contrast, Data Acquisition, Machine Learning, Synthetic MR Neuro
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.
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