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Direct low-field MRI super-resolution using undersampled k-space

Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly

TL;DR

This work proposes a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space that employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content.

Abstract

Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.

Direct low-field MRI super-resolution using undersampled k-space

TL;DR

This work proposes a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space that employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content.

Abstract

Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.
Paper Structure (10 sections, 2 equations, 4 figures, 1 table)

This paper contains 10 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Deep learning architecture configuration
  4. Experiments and Results
  5. Datasets
  6. Training and Testing
  7. Quantitative analysis
  8. Qualitative analysis
  9. Conclusion
  10. COMPLIANCE WITH ETHICAL STANDARDS

Figures (4)

  • Figure 1: K-space dual channel U-Net for LF-MRI SR/IQT using undersampled k-space.
  • Figure 2: HF-MR test images, and pseudo-radial and cartesian undersampling binary masks at 50% and 30% sampling rates.
  • Figure 3: Reconstruction results of the LF images, and IQT in the spatial and k-space domains, at different pseudo-radial under-sampling rates using two test images.
  • Figure 4: Absolute error maps between HF images in Fig. \ref{['fig:PRS_HFslices']}, and the reconstructions in Fig. \ref{['fig:PRS_IQTslices']} at different pseudo-radial under-sampling rates: $|\text{HF} - \text{LF}|$ in row 1, $|\text{HF} - \text{IQT-spatial}|$ in rows 2 and $|\text{HF} - \text{IQT-k-space}|$ in row 3.