Leveraging Deep Neural Networks to Improve Numerical and Perceptual Image Quality in Low-Dose Preclinical Pet Imaging Publisher Pubmed

Summary: Deep-learning (DL) methods may overcome imaging noise in low-dose small animal PET studies. Could this enhance future translational research? Findings suggest DL significantly improves image quality. #MedicalImaging #DeepLearning

Amirrashedi M^{1, 2} ; Sarkar S^{1, 2} ; Mamizadeh H^{1, 2} ; Ghadiri H^{1, 2} ; Ghafarian P^{3, 4} ; Zaidi H^{5, 6, 7, 8} ; Ay MR^{1, 2}
Source: Computerized Medical Imaging and Graphics Published:2021

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. © 2021 The Authors