Accelerated CEST Imaging with Deep Learning Priors and Synthetic Brain Tumor Datasets

Motivation: The clinical application of CEST MRI is constrained by its relatively long scan time. Goal(s): We aim to develop a deep learning reconstruction method for accelerating CEST imaging in the absence of true experimental data. Approach: Here, we propose a model-based deep learning framework, in conjunction with the Channel-wise Attention mechanism and Total variation regularization, dubbed as MoDL-CAT. Moreover, we propose a new workflow to synthesize CEST data from the BraTS and fastMRI repositories. Results: We demonstrate that the BraTS-CEST dataset can improve the performance of all deep learning networks tested, and the MoDL-CAT method achieves superior reconstruction quality to the state-of-the-art methods. Impact: The proposed deep learning framework with channel-wise attention may offer a better prior for reconstruction. And our novel workflow to synthesize high-quality brain tumor CEST datasets might help researchers with limited data to explore various methods for accelerating CEST imaging.