High-sensitivity CEST Image Denoising

The clinical application of CEST is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data.

• By modeling spatial variant z-spectrums into low-dimensional subspace, we developed Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping.
• Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification.

• Our unsupervised denoising algorithm (IRIS) improves the SNR and sensitivity of CEST MR imaging.
• It leverages an extremely lightweight neural network while achieving high performance in representing continuous signals.
• Qualitative and quantitative evaluations on both synthetic and in-vivo datasets demonstrate the outperformance of IRIS over other methods in terms of noise reduction while maintaining the integrity of the underlying CEST signal.

• MRI
• the detection of proteins and metabolites with low concentrations