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

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...

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Vydáno v:	Magnetic resonance in medicine Ročník 94; číslo 3; s. 1301 - 1317
Hlavní autoři:	Wang, Jiawen, Cai, Pei, Wang, Ziyan, Zhang, Huabin, Huang, Jianpan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.09.2025 John Wiley and Sons Inc
Témata:	Adult Algorithms Brain - diagnostic imaging chemical exchange saturation transfer (CEST) Computer Processing and Modeling Correlation analysis Data acquisition Data analysis Feasibility studies Female Healthy Volunteers human brain Humans Image Processing, Computer-Assisted - methods Kolmogorov‐Arnold network (KAN) Lorentzian‐KAN (LKAN) Magnetic resonance imaging Magnetic Resonance Imaging - methods Male multi‐pool Lorentzian fitting (MPLF) Overhauser effect Parameters Reproducibility of Results Spectra Young Adult human brain Kolmogorov‐Arnold network (KAN) multi‐pool Lorentzian fitting (MPLF) Lorentzian‐KAN (LKAN) chemical exchange saturation transfer (CEST)
ISSN:	0740-3194, 1522-2594, 1522-2594
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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.
AbstractList	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. To investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis (CEST-KAN/CEST-LKAN).PURPOSETo investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis (CEST-KAN/CEST-LKAN).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).METHODSCEST 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).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.RESULTSThe 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.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.CONCLUSIONThis 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. To investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis (CEST-KAN/CEST-LKAN). 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 ΔB , water, and multiple CEST contrasts, including amide, relayed nuclear Overhauser effect (rNOE), and magnetization transfer (MT). 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 ΔB 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. 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. 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.
Author	Wang, Jiawen Wang, Ziyan Huang, Jianpan Zhang, Huabin Cai, Pei
AuthorAffiliation	1 Laboratory of Advanced Imaging in Medicine (AIM), Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40468586$$D View this record in MEDLINE/PubMed
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Keywords	human brain Kolmogorov‐Arnold network (KAN) multi‐pool Lorentzian fitting (MPLF) Lorentzian‐KAN (LKAN) chemical exchange saturation transfer (CEST)
Language	English
License	Attribution-NonCommercial-NoDerivs 2025 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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Snippet	Purpose To investigate the potential of using Kolmogorov‐Arnold Network (KAN) and propose Lorentzian‐KAN (LKAN) for CEST MRI data analysis... To investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis (CEST-KAN/CEST-LKAN). CEST... Purpose To investigate the potential of using Kolmogorov‐Arnold Network (KAN) and propose Lorentzian‐KAN (LKAN) for CEST MRI data analysis... To investigate the potential of using Kolmogorov-Arnold Network (KAN) and propose Lorentzian-KAN (LKAN) for CEST MRI data analysis...
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StartPage	1301
SubjectTerms	Adult Algorithms Brain - diagnostic imaging chemical exchange saturation transfer (CEST) Computer Processing and Modeling Correlation analysis Data acquisition Data analysis Feasibility studies Female Healthy Volunteers human brain Humans Image Processing, Computer-Assisted - methods Kolmogorov‐Arnold network (KAN) Lorentzian‐KAN (LKAN) Magnetic resonance imaging Magnetic Resonance Imaging - methods Male multi‐pool Lorentzian fitting (MPLF) Overhauser effect Parameters Reproducibility of Results Spectra Young Adult
Title	CEST MRI data analysis using Kolmogorov‐Arnold network (KAN) and Lorentzian‐KAN (LKAN) models
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmrm.30548 https://www.ncbi.nlm.nih.gov/pubmed/40468586 https://www.proquest.com/docview/3229052694 https://www.proquest.com/docview/3215984218 https://pubmed.ncbi.nlm.nih.gov/PMC12202730
Volume	94
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