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Related Experiment Video

Updated: Feb 2, 2026

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Group equivariant pyramid network for respiratory motion correction on PET image.

Z Wu1, H Zhou2, J Ning1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.

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|January 31, 2026
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Summary

A new Group Equivariant Dual-Pyramid Network (GEPN) effectively reduces respiratory motion artefacts in PET scans. This advanced method enhances lesion clarity and improves diagnostic accuracy for better patient treatment planning.

Keywords:
Dual-stream encodingGroup equivariant convolutionPET image respiratory motion correctionPyramid network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Respiratory motion causes significant blurring in PET images, hindering accurate lesion detection and diagnosis.
  • Current artefact correction techniques lack advanced feature learning, limiting their effectiveness in removing motion-induced blurring.

Purpose of the Study:

  • To develop a novel deep learning framework for robust respiratory motion artefact correction in 3D PET imaging.
  • To enhance the clarity and diagnostic reliability of PET images affected by patient movement during scans.

Main Methods:

  • Development of a Group Equivariant Dual-Pyramid Network (GEPN) utilizing SE(3)-equivariant convolutions for 3D PET data.
  • Integration of attention-enhanced CNNs and SE(3) G-CNNs for efficient organ feature extraction.
  • Implementation of a Lie group-based motion decomposition strategy in the decoder for inter-organ displacement and SE(3) component resolution.

Main Results:

  • GEPN demonstrated superior performance across geometric, lung phantom, and clinical PET datasets, achieving high Dice coefficients (e.g., 81.01% on clinical data).
  • The framework significantly improved artefact correction compared to baseline models, enhancing lesion clarity and motion pattern alignment.
  • Quantitative and perceptual metrics confirmed GEPN's effectiveness in mitigating respiratory motion artefacts.

Conclusions:

  • The GEPN framework successfully addresses respiratory artefacts in PET imaging through a unique combination of group-equivariant architectures and attention mechanisms.
  • GEPN enhances PET image quality, offering a robust solution for improved diagnostic reliability and clinical decision-making.
  • The method provides sharper PET images, aiding in precise tumor delineation for improved radiotherapy planning and treatment assessment.