  Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability
in significantly enhancing the detection of proteins and metabolites with low
concentrations through exchangeable protons. The clinical application of CEST,
however, is constrained by its low contrast and low signal-to-noise ratio (SNR)
in the acquired data. Denoising, as one of the post-processing stages for CEST
data, can effectively improve the accuracy of CEST quantification. In this
work, by modeling spatial variant z-spectrums into low-dimensional subspace, we
introduce Implicit Regression in Subspace (IRIS), which is an unsupervised
denoising algorithm utilizing the excellent property of implicit neural
representation for continuous mapping. Experiments conducted on both synthetic
and in-vivo data demonstrate that our proposed method surpasses other CEST
denoising methods regarding both qualitative and quantitative performance.
