Motivation: For B0 correction of GluCEST MRI, some deep learning-based algorithms have been developed to significantly accelerate the Z-spectrum calibration process. Goal(s): When applied to CEST imaging involving other metabolites, and to Nuclear Overhauser Effect (NOE) MRI, the performance of the model declined substantially. Our goal is to develop a new model that can handle different metabolites. Approach: To address this issue, we proposed a Swin-Transformer-based model designed to handle both NOE and Glutamate-weighted CEST MRI separately. Results: Preliminary results demonstrate strong performance on both GluCEST and NOE datasets, indicating the potential for a generalizable model applicable to other CEST agents. Impact: This success of the proposed method suggests that the Swin Transformer could potentially serve as a general model for B0 correction across various metabolites in a single model if sufficient data is available.
Li et al. (Tue,) studied this question.