Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging. (December 2021) - Amirrashedi, Mahsa; Sarkar, Saeed; Mamizadeh, Hojjat; Ghadiri, Hossein; Ghafarian, Pardis; Zaidi, Habib; Ay, Mohammad Reza
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. Graphical Abstract: ga1 Highlights: Low-dose imaging is characterized by higher noise level, thus impacting image quality. The feasibility of deep-learning-based image denoising in low-count small animal PET studies has never been reported. We assessed the potential of various deep-learning models to generate full-dose mice PET scans from the undersampled data. Compared to other methods, deep-learning brings a multitude of advantages in terms of quality, implementation, and speed.