CEST MRI data analysis using Kolmogorov-Arnold network (KAN) and Lorentzian-KAN (LKAN) models

Abstract

Purpose: To investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis (CEST-KAN/CEST-LKAN).

Methods: CEST MRI data acquired from 27 healthy volunteers at 3 T were used in this study. Data from 25 subjects were used for training and validation (548 865 Z-spectra), whereas the remaining two were reserved for testing (51 977 Z-spectra). The performance of multi-layer perceptron (MLP), KAN, and LKAN models was evaluated and compared to conventional multi-pool Lorentzian fitting (MPLF) method in generating ΔB₀, water, and multiple CEST contrasts, including amide, relayed nuclear Overhauser effect (rNOE), and magnetization transfer (MT).

Results: The KAN and LKAN showed higher accuracy in predicting CEST parameters compared to MLP, with average reductions in test loss of 28.37% and 32.17%, respectively. Voxel-wise correlation analysis also revealed that ΔB₀ and four other CEST parameters from the KAN and LKAN had higher average Pearson coefficients than MLP by 1.57% and 2.84%, indicating superior performance. LKAN exhibited a shorter average training time by 37.26% and a smaller average test loss by 5.29% compared to the KAN. Furthermore, our results demonstrated that even smaller KAN and LKAN could achieve better accuracy than MLPs, with both KAN and LKAN showing greater robustness to noisy data compared to MLP.

Conclusion: This study demonstrates the feasibility of KAN and LKAN for CEST MRI data analysis, highlighting their superiority over MLP. The findings suggest that CEST-KAN and CEST-LKAN have the potential to be robust and reliable post-analysis tools for CEST MRI in clinical settings.

Keywords: Kolmogorov‐Arnold network (KAN); Lorentzian‐KAN (LKAN); chemical exchange saturation transfer (CEST); human brain; multi‐pool Lorentzian fitting (MPLF).