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Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks.

Rajat Vashistha1,2, Viktor Vegh1,2, Hamed Moradi1,2,3

  • 1Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.

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|September 27, 2024
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Summary

This study introduces a novel generative adversarial network (GAN) framework for Positron Emission Tomography (PET) image reconstruction. The method effectively corrects common PET imaging artifacts, improving image quality without patient-specific data.

Keywords:
PET image reconstructiondeep learninggenerative adversarial networknoise and motion correctionnon-clinical training data

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Area of Science:

  • Medical Imaging
  • Machine Learning in Healthcare
  • Radiological Sciences

Background:

  • Positron Emission Tomography (PET) image reconstruction is crucial for diagnostic accuracy but is affected by noise and artifacts.
  • Existing correction methods address individual issues, while machine learning, particularly Generative Adversarial Networks (GANs), shows potential for complex data mapping.
  • This study explores GANs for PET image reconstruction, focusing on training data properties and artifact correction without patient-specific anatomical information.

Purpose of the Study:

  • To investigate the impact of training image properties on GAN performance for PET image reconstruction.
  • To develop and evaluate a novel GAN-based method for correcting common PET imaging artifacts.
  • To assess the proposed method's effectiveness compared to traditional techniques like Filtered Backprojection (FBP) and Ordered Subset Expectation Maximization (OSEM).

Main Methods:

  • A modular GAN framework comprising two modules was developed, with the first module trained on non-clinical sinogram-image pairs and optimized based on image metrics.
  • The second module employed adaptive instance normalization and style embedding to refine image quality, incorporating perceptual and patch-based loss functions.
  • Performance was evaluated against FBP and OSEM (with and without point spread function correction) using simulated, NEMA phantom, and human imaging data, with metrics including SSIM, PSNR, rRMSE, and CNR.

Main Results:

  • The proposed GAN framework demonstrated superior qualitative and quantitative performance compared to FBP and OSEM for simulated data.
  • Module 2 refinement significantly improved image quality, achieving over 22% higher SSIM than OSEM in noisy conditions.
  • The method proved robust against noise and motion, yielding higher CNR values for phantom and human brain imaging data compared to OSEM and FBP.

Conclusions:

  • The developed GAN-based image reconstruction method effectively corrects common PET imaging artifacts.
  • The framework offers a promising alternative to traditional methods, enhancing image quality and quantitative accuracy.
  • This approach advances PET imaging by providing robust artifact correction without requiring patient-specific anatomical data.