Scholarlink

Many approaches have been taken to the task of fitting Proton Magnetic Resonance Spectroscopy ( \(^1\) H-MRS) data, but the challenge remains unsolved. Traditional neural networks for example perform poorly in the fitting of low quantity metabolites and require large datasets. Recently, Physics Informed Neural Networks (PINN) has emmerged as a promising solution for fitting \(^1\) H-MRS data by integrating physical laws with artificial intelligence. This paper proposes a PINN framework for fitting \(^1\) H-MRS 7T data that outperforms current methods with a speed of 0.030 ms for the fitting, robust to pathological variations, providing accurate and rapid fitting of brain metabolites relevant in the diagnosis of the central nervous system diseases and disorders. Source code is available at https://github.com/lbonnin1/MRS-PINN-GUI.git .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MR Spectroscopy Spectrum Fitting Using a Physically Informed Neural Network

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Landoline Bonnin,
  • Wissal Ramchi,
  • Pascal Bourdon,
  • Carole Guillevin,
  • Remy Guillevin,
  • Clement Giraud,
  • Christine Fernandez-Maloigne

摘要

Many approaches have been taken to the task of fitting Proton Magnetic Resonance Spectroscopy ( \(^1\) H-MRS) data, but the challenge remains unsolved. Traditional neural networks for example perform poorly in the fitting of low quantity metabolites and require large datasets. Recently, Physics Informed Neural Networks (PINN) has emmerged as a promising solution for fitting \(^1\) H-MRS data by integrating physical laws with artificial intelligence. This paper proposes a PINN framework for fitting \(^1\) H-MRS 7T data that outperforms current methods with a speed of 0.030 ms for the fitting, robust to pathological variations, providing accurate and rapid fitting of brain metabolites relevant in the diagnosis of the central nervous system diseases and disorders. Source code is available at https://github.com/lbonnin1/MRS-PINN-GUI.git .