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

Amirrashedi, Mahsa
Sarkar, Saeed
Mamizadeh, Hojjat
Ghadiri, Hossein
Ghafarian, Pardis
Ay, Mohammad Reza
Published in Computerized medical imaging and graphics. 2021, vol. 94, 102010
Abstract 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.
Keywords PETSmall animal imagingDeep-learningLow-dose imagingDenoising
PMID: 34784505
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Research group Imagerie Médicale (TEP et TEMP) (542)
Swiss National Science Foundation: 320030_176052
Private Foundation of Geneva University Hospitals: RC-06-01
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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 https://archive-ouverte.unige.ch/unige:156547

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Deposited on : 2021-11-18

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