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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network.

Jingzhang Sun1, Qi Zhang1,2, Yu Du1

  • 1Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macao SAR, China.

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|May 8, 2022
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Summary

A new 3D conditional generative adversarial network (cGAN) effectively denoises dual-gated myocardial perfusion single-photon emission computed tomography (MP-SPECT) images. This patient-specific denoising method significantly improves image quality and diagnostic accuracy for MP-SPECT.

Keywords:
conditional generative adversarial networkdenoisingdual gatingmyocardial perfusion SPECT

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Area of Science:

  • Medical Imaging
  • Nuclear Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Dual respiratory-cardiac gating in myocardial perfusion single-photon emission computed tomography (MP-SPECT) reduces motion blur but increases image noise due to reduced counts.
  • Developing advanced denoising techniques is crucial for optimizing MP-SPECT image quality and diagnostic performance.

Purpose of the Study:

  • To develop and evaluate a 3D conditional generative adversarial network (cGAN) for denoising dual-gated (DG) MP-SPECT images.
  • To assess the efficacy of patient-specific (PD) cGAN denoising using cardiac-gated (CG) images as a reference.

Main Methods:

  • Utilized extended cardiac-torso phantoms and clinical SPECT/CT datasets, reconstructing images with multiple respiratory and cardiac gates (48-56 DGs).
  • Trained a 3D cGAN using patient-specific data and CG images as references, comparing its performance against conventional filtering, gating methods, and convolutional neural networks.
  • Evaluated image quality using metrics including normalized mean squared error, noise, spatial blurring, ejection fraction, and defect size.

Main Results:

  • Patient-specific training (PD) significantly outperformed cross-patient training.
  • Using cardiac-gated (CG) images as a reference for cGAN training offered a superior balance between noise reduction and image blur compared to non-gated (NG) data.
  • The proposed cGAN-CG-PD method demonstrated superior performance across all evaluated physical and diagnostic indices in both simulated and clinical studies.

Conclusions:

  • 3D conditional generative adversarial network (cGAN) denoising shows significant promise for improving dual-gated MP-SPECT image quality.
  • The patient-specific approach using cardiac-gated references offers a robust solution for reducing noise and blur in MP-SPECT.