  Purpose: This study aims to propose and investigate the feasibility of using
Kolmogorov-Arnold Network (KAN) for CEST MRI data analysis (CEST-KAN). Methods:
CEST MRI data were acquired from twelve healthy volunteers at 3T. Data from ten
subjects were used for training, while the remaining two were reserved for
testing. The performance of multi-layer perceptron (MLP) and KAN models with
the same network settings were evaluated and compared to the conventional
multi-pool Lorentzian fitting (MPLF) method in generating water and multiple
CEST contrasts, including amide, relayed nuclear Overhauser effect (rNOE), and
magnetization transfer (MT). Results: The water and CEST maps generated by both
MLP and KAN were visually comparable to the MPLF results. However, the KAN
model demonstrated higher accuracy in extrapolating the CEST fitting metrics,
as evidenced by the smaller validation loss during training and smaller
absolute error during testing. Voxel-wise correlation analysis showed that all
four CEST fitting metrics generated by KAN consistently exhibited higher
Pearson coefficients than the MLP results, indicating superior performance.
Moreover, the KAN models consistently outperformed the MLP models in varying
hidden layer numbers despite longer training time. Conclusion: In this study,
we demonstrated for the first time the feasibility of utilizing KAN for CEST
MRI data analysis, highlighting its superiority over MLP in this task. The
findings suggest that CEST-KAN has the potential to be a robust and reliable
post-analysis tool for CEST MRI in clinical settings.
