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Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.

Yang Lei1,2, Xue Dong1,2, Tonghe Wang1,3

  • 1Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.

Physics in Medicine and Biology
|September 28, 2019
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Summary
This summary is machine-generated.

A novel Cycle GAN deep learning model enhances low-count PET images, reducing radiation exposure and improving diagnostic quality. This method significantly reduces noise and improves image quantification for better patient care.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiochemistry

Background:

  • Positron Emission Tomography (PET) imaging aims to reduce radiation dose and scan times for patient benefit.
  • Lowering photon counts in PET increases noise, degrading image quality and impacting diagnostic accuracy.
  • Current methods struggle to maintain diagnostic quality with significantly reduced PET data.

Purpose of the Study:

  • To develop a deep learning model for synthesizing diagnostic-quality PET images from low-count data.
  • To address the challenges of noise and image degradation in low-count PET imaging.
  • To improve the signal-to-noise ratio and quantitative accuracy of PET scans with reduced data.

Main Methods:

  • A Cycle-consistent Generative Adversarial Network (Cycle GAN) was employed to learn image transformations.
  • Residual blocks were incorporated into the generator to better capture noise and differences in low-count PET data.
  • The model was trained to synthesize high-quality PET images from data with one-eighth of standard photon counts.

Main Results:

  • The Cycle GAN model achieved average mean error of -0.14% ± 1.43% and normalized mean square error of 0.52% ± 0.19%.
  • Compared to original low-count images (5.59% ± 2.11% and 3.51% ± 4.14%), the model significantly improved quantitative accuracy.
  • Normalized cross-correlation increased from 0.970 to 0.996, and peak signal-to-noise ratio improved from 39.4 dB to 46.0 dB.

Conclusions:

  • The proposed Cycle GAN deep learning approach effectively estimates diagnostic-quality PET images from low-count data.
  • This method holds significant potential for improving low-count PET image quality to clinical diagnostic standards.
  • The technique offers a promising solution for reducing patient radiation burden while maintaining diagnostic efficacy in PET imaging.