Implicit Regression in Subspace for High-Sensitivity CEST Imaging
This work addresses a domain-specific problem for clinical CEST MRI applications by improving denoising accuracy, though it appears incremental as it builds on existing denoising approaches with a novel method.
The paper tackles the problem of low contrast and low signal-to-noise ratio in CEST MRI by introducing IRIS, an unsupervised denoising algorithm that models spatial variant z-spectrums into a low-dimensional subspace, resulting in superior qualitative and quantitative performance compared to other methods.
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.