Scholarlink

This study explores a physics-informed convolutional neural network (CNN) ensemble for Intravoxel Incoherent Motion (IVIM) parameters estimation in diffusion-weighted MRI (DW-MRI). A U-Net was trained on simulated phantoms under different noise conditions and evaluated using Median Absolute Error and Robust Coefficient of Variation. The ensemble approach improved robustness and enabled uncertainty quantification through variance maps. When tested on in-vivo mouse brain images, the CNN estimates aligned well with Bayesian results, particularly for D and D* parameters. The findings suggest that ensembling improves IVIM accuracy and reliability while also enabling uncertainty quantification.

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

Physics-Informed CNN Ensemble for Improved Tissue Perfusion and Diffusion Estimation with Uncertainty Quantification in IVIM MRI

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Nicola Casali,
  • Alessandro Brusaferri,
  • Giuseppe Baselli,
  • Stefano Fumagalli,
  • Edoardo Micotti,
  • Gianluigi Forloni,
  • Riaz Hussein,
  • Giovanna Rizzo,
  • Alfonso Mastropietro

摘要

This study explores a physics-informed convolutional neural network (CNN) ensemble for Intravoxel Incoherent Motion (IVIM) parameters estimation in diffusion-weighted MRI (DW-MRI). A U-Net was trained on simulated phantoms under different noise conditions and evaluated using Median Absolute Error and Robust Coefficient of Variation. The ensemble approach improved robustness and enabled uncertainty quantification through variance maps. When tested on in-vivo mouse brain images, the CNN estimates aligned well with Bayesian results, particularly for D and D* parameters. The findings suggest that ensembling improves IVIM accuracy and reliability while also enabling uncertainty quantification.