Scientific article
Open access

Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging

Published inComputerized medical imaging and graphics, vol. 94, 102010
Publication date2021-12
First online date2021-11-07

The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imaging is characterized by a higher level of noise, the quality of the reconstructed images leaves much to be desired. Being the most ubiquitous techniques in denoising applications, edge-aware denoising filters, and reconstruction-based techniques have drawn significant attention in low-count applications. However, for the last few years, much of the credit has gone to deep-learning (DL) methods, which provide more robust solutions to handle various conditions. Albeit being extensively explored in clinical studies, to the best of our knowledge, there is a lack of studies exploring the feasibility of DL-based image denoising in low-count small animal PET imaging. Therefore, herein, we investigated different DL frameworks to map low-dose small animal PET images to their full-dose equivalent with quality and visual similarity on a par with those of standard acquisition. The performance of the DL model was also compared to other well-established filters, including Gaussian smoothing, nonlocal means, and anisotropic diffusion. Visual inspection and quantitative assessment based on quality metrics proved the superior performance of the DL methods in low-count small animal PET studies, paving the way for a more detailed exploration of DL-assisted algorithms in this domain.

  • PET
  • Small animal imaging
  • Deep-learning
  • Low-dose imaging
  • Denoising
Citation (ISO format)
AMIRRASHEDI, Mahsa et al. Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging. In: Computerized medical imaging and graphics, 2021, vol. 94, p. 102010. doi: 10.1016/j.compmedimag.2021.102010
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Article (Published version)
ISSN of the journal0895-6111

Technical informations

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