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Cross noise level PET denoising with continuous adversarial domain generalization.

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This study introduces a novel domain generalization technique for positron emission tomography (PET) denoising, improving image quality across different noise levels. The method enhances accuracy and consistency in Alzheimer's research imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiochemistry

Background:

  • Positron emission tomography (PET) image denoising is crucial for reducing variance.
  • Deep learning models struggle with generalization across varying noise levels, leading to biased results.
  • Domain generalization offers a potential solution to improve model robustness.

Purpose of the Study:

  • To develop a domain generalization technique for robust PET image denoising across different noise levels.
  • To minimize bias and improve generalization in deep learning-based PET denoising models.
  • To create a model capable of handling arbitrary noise levels in PET imaging.

Main Methods:

  • Utilized domain generalization with a novel continuous discriminator (CD) for adversarial training.
  • Employed fraction of events as a continuous domain label to enforce noise-level invariant features.
  • Trained and tested a 3D UNet model with CD on 18F-MK6240 tau PET datasets across varied noise levels.

Main Results:

  • The proposed CD method significantly improved denoising performance, reducing bias and standard deviation.
  • Enhanced structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) in Alzheimer-related regions and whole brain images.
  • Demonstrated consistent performance improvements when tested on noise levels outside the training distribution.

Conclusions:

  • This study presents the first domain generalization approach for cross-noise level PET denoising.
  • The continuous domain generalization technique effectively addresses performance degradation in varying noise conditions.
  • The developed method shows promise for improving the reliability of PET imaging in clinical research.