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Groupwise image registration with edge-based loss for low-SNR cardiac MRI.

Xuan Lei1, Philip Schniter1, Chong Chen2

  • 1Electrical & Computer Engineering, The Ohio State University, Columbus, Ohio.

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|May 12, 2025
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Summary

A new deep learning method, Averaging Morph with Edge Detection (AiM-ED), improves cardiac image quality by registering and averaging low-SNR free-breathing images. This technique enhances signal-to-noise ratio (SNR) and image quality for cardiac magnetic resonance (CMR) applications.

Keywords:
LGEVoxelMorphcardiovascular MRIedge detectionimage registration

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Imaging

Background:

  • Single-shot cardiac imaging often suffers from low signal-to-noise ratio (SNR), particularly at lower magnetic field strengths.
  • Improving image quality in free-breathing cardiac scans is crucial for accurate diagnosis and monitoring.

Purpose of the Study:

  • To develop and validate a novel deep learning-based image registration method for averaging multiple free-breathing single-shot cardiac images with low SNR.
  • To address the challenge of low SNR in cardiac magnetic resonance (CMR) imaging.

Main Methods:

  • Averaging Morph with Edge Detection (AiM-ED), a fast deep learning (DL) method, jointly registers multiple noisy source images to a target image.
  • AiM-ED employs a noise-robust edge detector to enhance training loss and is validated on synthetic and real free-breathing single-shot late gadolinium-enhanced (LGE) images.

Main Results:

  • AiM-ED demonstrated superior performance compared to traditional and VoxelMorph-based registration methods, yielding higher recovery SNR and perceptual image quality metrics.
  • Ablation studies confirmed the benefits of jointly processing multiple images and incorporating edge maps within the AiM-ED framework.

Conclusions:

  • AiM-ED significantly enhances image quality for single-shot LGE imaging, outperforming existing registration techniques.
  • The method's fast inference, minimal training data needs, and robustness to noise position it as a valuable tool for single-shot CMR applications.