Motivation: Endogenous CEST contrast is relatively small and vulnerable to imaging noise. Goal(s): To develop a retrospective denoising method to enhance SNR of acquired CEST images. Approach: A denoising neural network was trained using pairs of noisy CEST images through a noise-to-noise deep learning approach, distinct from conventional approaches that use clean or simulated CEST images. A data consistency layer was introduced to preserve center k-space of original CEST images to improve fidelity. A transformer module was used to exploit spatiotemporal correlations among different frequency offsets. Results: Multipool Lorentzian fitting was performed. Compared to clean images, our method achieved mean correlation coefficient of 0.90. Impact: Our method can considerably increase SNR of CEST images without sacrificing image fidelity. After denoising, the derived CEST maps could more reliably represent molecular changes in brain regions.
Liu et al. (Tue,) studied this question.