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Virtual high-count PET image generation using a deep learning method.

Juan Liu1, Sijin Ren1, Rui Wang1,2

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Nuclear Medicine

Background:

  • Deep learning methods can denoise low-count Positron Emission Tomography (PET) images.
  • Standard-count PET scans are common in clinical settings.
  • Virtual-high-count (VHC) PET images aim to improve image quality from standard scans.

Purpose of the Study:

  • To apply a 3D U-Net network to reduce noise in standard-count PET images.
  • To generate virtual-high-count (VHC) PET images from standard-count PET data.
  • To identify potential benefits of VHC PET images for clinical diagnosis.

Main Methods:

  • A 3D U-Net network was trained using 27 dynamic PET datasets.
  • Standard-count PET images were down-sampled and noise levels matched to 195 clinical static PET datasets.
  • Quantitative metrics (NMSE, PSNR, SSIM, SUV bias) and physician evaluations were used for assessment.

Main Results:

  • VHC PET images demonstrated improved quantitative metrics and significantly lower noise (NSTD) compared to standard-count images.
  • No statistically significant difference was found in mean/max Standard Uptake Value (SUV) for lesions between VHC and standard-count images.
  • Physician evaluations consistently ranked VHC images higher in overall image quality than standard-count images.

Conclusions:

  • A deep learning (DL) method effectively converted standard-count PET images to VHC PET images.
  • VHC PET images exhibited reduced noise levels while maintaining lesion SUV accuracy.
  • The VHC PET images offer improved image quality, potentially enhancing lesion detectability and clinical diagnosis.