Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network

Chemical exchange saturation transfer (CEST) MRI is a versatile technique that exploits the saturation transfer between exchangeable protons and water for non-invasive detection of diluted metabolites. Although theoretically promising, the practical application of CEST MRI is still challenged by low CEST contrast and low signal-to-noise ratio (SNR) of acquired images. Here, we proposed a deep learning-based method, dubbed denoising CEST network (DCEST-Net), to fully exploit the spatiotemporal correlation prior embedded in the CEST images and restore noise-free images from their noisy observations. Results suggested that DCEST-Net can achieve better performance compared to the state-of-the-art denoising methods.