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Supervised learning with cyclegan for low-dose FDG PET image denoising.

Long Zhou1, Joshua D Schaefferkoetter2, Ivan W K Tham3

  • 1Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China; ZheJiang Minfound Intelligent Healthcare Technology Co., Ltd., West Wenyi road, Hangzhou, China.

Medical Image Analysis
|July 17, 2020
PubMed
Summary

This study introduces CycleWGANs, a deep learning model to improve low-dose PET imaging quality. The model enhances image accuracy and SUV values, crucial for detecting lung cancer, while minimizing radiation exposure risks.

Keywords:
Cycle consistentGenerative adversarial networksLow-dosePET

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence

Background:

  • Positron Emission Tomography (PET) imaging uses radiotracers, posing radiation exposure risks.
  • Reducing radiotracer doses can compromise PET image quality, hindering diagnostic accuracy.
  • Developing methods to enhance low-dose PET images is critical for patient safety and effective diagnosis.

Purpose of the Study:

  • To propose and validate a supervised deep learning model, CycleWGANs, for boosting low-dose PET image quality.
  • To compare the performance of CycleWGANs against traditional denoising methods and other deep learning models.
  • To assess the model's ability to maintain diagnostic accuracy, specifically Standardized Uptake Value (SUV) metrics, from low-dose PET data.

Main Methods:

  • A supervised deep learning model, CycleWGANs, was developed to reconstruct high-quality PET images from low-dose data.
  • Low-dose PET datasets were simulated from a real dataset of lung cancer patients by reducing event counts.
  • Performance was evaluated against Non-Local Mean (NLM), BM3D, RED-CNN, and 3D-cGAN using SUV bias, SSIM, PSNR, and NRMSE metrics.

Main Results:

  • CycleWGANs demonstrated superior performance in estimating full-dose PET images from low-dose inputs, outperforming other methods in SUV bias for lesions and normal tissues.
  • While RED-CNN excelled in traditional metrics like SSIM and PSNR, CycleWGANs showed better preservation of edge details and SUV values.
  • Correlation and profile analyses confirmed CycleWGANs' effectiveness in maintaining diagnostic information despite a slight increase in noise.

Conclusions:

  • CycleWGANs effectively enhances low-dose PET image quality, offering a promising solution for reducing radiation exposure in medical imaging.
  • The model's ability to preserve critical SUV values and lesion characteristics is vital for accurate lung cancer diagnosis.
  • CycleWGANs represents a significant advancement in deep learning applications for medical imaging, balancing image quality with patient safety.