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基于机器学习的多池Voigt在Z频谱中适应CEST,rNOE和MTC

Sajad Mohammed Ali¹, Peter C M van Zijl^2,3, Jannik Prasuhn^3,4,5,6,7

¹Department of Medical Radiation Physics, Lund University, Lund, Sweden.

Magnetic resonance in medicine

|February 18, 2025

概括

使用Voigt模型对Z-spectra进行机器学习,大大减少了临床应用的处理时间. 这种Four-pool Voigt (FPV) -ML方法为大型研究提供了更快,更高质量的分析.

科学领域:

磁共振成像是一种磁共振成像技术.
计算生物学计算生物学
生物物理学的生物物理.

背景情况:

在定量磁共振光谱学 (MRS) 中,Z光谱分析至关重要.
传统的装配方法可能耗时,限制了临床可行性和大规模研究.
开发更快,更准确的装配模型对于推进MRS应用至关重要.

研究的目的:

开发和评估基于机器学习 (ML) 的Z-spectra适配方法,使用四池Voigt (FPV) 模型.
显著减少临床扫描仪分析和大型队列研究的安装时间.
将FPV-ML模型与四池罗伦斯 (FPL) -ML模型进行比较,以验证Voigt模型的优势.

主要方法:

沃伊格特和洛伦兹模型与人类3TZ光谱数据相匹配,使用最小平方 (LS) 来生成训练数据.
使用渐变增强决策树来训练FPV-ML和FPL-ML模型.
模型准确性,合适时间 (ML与LS) 和合适度都被严格评估.

主要成果:

两种FPV-ML和FPL-ML模型都取得了极好的准确性,训练时间不到1分钟.
ML配件平均为20μs/频谱,比LS配件 (0.270.82 s/频谱) 快得多.
与FPL-ML相比,FPV-ML在所有测试数据中显著改善了适合性 (p < 0.005).

结论:

与LS方法相比,渐变增强决策树的拟合显著加快了Z光谱分析.
开发的ML模型适用于临床环境和大型队列研究中的快速数据处理.
FPV-ML方法提供了卓越的适配精度,使其有利于先进的MRS应用.

关键词:

在CEST中,CEST是CEST.洛伦齐安适配器洛伦齐安适配器在MTC中,MTC是MTC.沃伊格特的合适适应.机器学习是机器学习.在诺伊尔.

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