使用科尔莫戈罗夫-阿诺德网络 (KAN) 和洛伦茨-KAN (LKAN) 模型进行CEST MRI数据分析
Jiawen Wang1, Pei Cai1, Ziyan Wang1
1Laboratory of Advanced Imaging in Medicine (AIM), Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Magnetic resonance in medicine
|June 5, 2025
概括
与传统方法相比,科尔莫戈罗夫-阿诺德网络 (KAN) 和洛伦齐安-KAN (LKAN) 在化学交换和转移 (CEST) MRI 数据分析方面表现出卓越的准确性和稳定性. 这些新的方法在CESTMRI中提供了可靠的临床应用的潜力.
科学领域:
- 磁共振成像技术 磁共振成像技术
- 医疗成像中的人工智能
- 量化MRI是指数量化的MRI.
背景情况:
- 化学交换和转移 (CEST) MRI 是评估组织组成的一个有价值的技术.
- 对于CEST MRI数据的传统分析方法,如多池罗伦兹拟合 (MPLF),可能是计算密集的,可能缺乏准确性.
- 开发先进的计算模型对于提高CESTMRI数据分析的效率和可靠性至关重要.
研究的目的:
- 调查科尔摩戈罗夫-阿诺德网络 (KAN) 在CEST MRI数据分析方面的潜力.
- 提出和评估一种新的洛伦兹-KAN (LKAN) 模型,用于增强CESTMRI数据处理.
- 将KAN和LKAN的性能与多层感知子 (MLP) 和MPLF等传统方法进行比较.
主要方法:
- 获取了27名健康志愿者的3个特斯拉CEST磁力共振成像数据.
- 利用了548,865个Z光谱的大数据集进行培训和验证,以及51,977个Z光谱进行测试.
- 评估了MLP,KAN和LKAN模型,用于对MPLF预测ΔB0,水和各种CEST对比 (胺基,rNOE,MT).
主要成果:
- 在预测CEST参数方面,KAN和LKAN的准确性高于MLP,测试损失显著减少 (KAN为28.37%,LKAN为32.17%).
- 与MLP相比,Voxel智能相关性分析显示KAN和LKAN在预测ΔB0和其他CEST参数方面的表现优于KAN和LKAN.
- 与KAN相比,LKAN实现了较快的平均训练时间 (37.26%的减少) 和较低的平均测试损失 (5.29%的减少),而这两种模型对噪音数据的稳定性都比MLP更强大.
结论:
- 科尔莫戈罗夫-阿诺德网络 (KAN) 和洛伦齐安-KAN (LKAN) 是可行的和有效的CESTMRI数据分析.
- 这些新型神经网络架构在准确性和稳定性方面明显优于传统的MLP模型.
- CEST-KAN和CEST-LKAN显示出作为临床CESTMRI应用的可靠后分析工具的前景.
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