Keywords: CEST / APT / NOE, Machine Learning/Artificial Intelligence, Synthetic 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.
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