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Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

Huidong Xie1, Stephanie Thorn2, Xiongchao Chen1

  • 1Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT, 06520, USA.

Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology
|May 4, 2022
PubMed
Summary

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This summary is machine-generated.

Improving cardiac SPECT imaging quality for GE Discovery NM scanners is possible by increasing projection angles. Deep learning enhances image reconstruction, leading to more accurate myocardial perfusion defect detection.

Area of Science:

  • Medical Imaging
  • Nuclear Cardiology
  • Artificial Intelligence in Medicine

Background:

  • GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners.
  • Stationary imaging limits projection angular sampling, impacting reconstruction quality.
  • This study explores enhancing image quality through increased angular sampling and deep learning.

Purpose of the Study:

  • To improve cardiac SPECT reconstruction quality on DNM scanners.
  • To investigate the impact of additional projection angular sampling.
  • To develop a deep learning method for generating synthetic dense-view images.

Main Methods:

  • Acquired four projection angle sets by moving the detector array.
  • Developed a deep neural network to generate synthetic four-angle images from one-angle images.
Keywords:
Cardiac SPECTCardiovascular diseaseDeep learningFew-view imagingGE discovery NM 530c/570c

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  • Trained and evaluated the network using simulated, phantom, pig, and human data.

    • Main Results:

    • Multi-angle reconstructions and deep learning methods yielded higher image resolution and uniformity.
    • Improved accuracy in myocardial perfusion defect quantification was observed.
    • Deep learning results demonstrated enhanced image quality and defect contrast in human studies.

    Conclusions:

    • Increasing angular sampling significantly improves image quality on DNM scanners.
    • Deep learning is a viable method to enhance reconstruction quality for stationary cardiac SPECT imaging.