GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Journal: Magnetic resonance imaging
PMID: 39561859

Abstract

PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural detail, leading to suboptimal image quality. GraFMRI leverages Graph Neural Networks (GNNs) to transform multi-modal MRI data (T1, T2, PD) into a graph-based representation, enabling the model to capture intricate spatial relationships and inter-modality dependencies.

Authors

  • Shahzad Ahmed
    Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Feng Jinchao
    Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Javed Ferzund
    Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan.
  • Muhammad Usman Ali
    Department of Computer Science, COMSATS University Islamabad Sahiwal Campus Sahiwal 57000, Pakistan.
  • Muhammad Yaqub
    Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Malik Abdul Manan
    Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Atif Mehmood
    Department of Computer Science, National University of Modern Language, NUML, Islamabad, Pakistan.

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