Standard-Dose PET Reconstruction from Low-Dose Preclinical Images Using an Adopted All Convolutional U-Net

摘要

As a mainstay of metabolic studies, Positron Emission Tomography (PET) has aroused remarkable attention in the clinical arena and the translational realm. The amount of radiotracer dosage is amongst the major problems in PET imaging, creating ongoing challenges for both the clinical community and the preclinical researchers. In quest of generating diagnostic quality PET images in extremely low-dose conditions, several deep-learning(DL)-inspired methods have sprung up in human imaging over the past few years. Propelled by the successful application of DL techniques in human studies and the unique advantages of deep neural networks in learning specific features, we have investigated a fully 3D U-Net-like model which enables reconstructing standard-dose PET dataset directly from its low-dose equivalent. We verified the performance of the method both in mice and rat PET scans through calculating image evaluation metrics such as RMSE, PSNR, and SSIM. Our measurements revealed that the proposed method could provide high-quality PET scans with improved noise properties in low-dose rodent studies.