RT Journal Article
T1 Deep-learning based positron range correction of PET images
A1 López Herraiz, Joaquín
A1 Bembibre, Adrián
A1 López Montes, Alejandro
AB Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.
PB MDPI
YR 2020
FD 2020-12-29
LK https://hdl.handle.net/20.500.14352/101228
UL https://hdl.handle.net/20.500.14352/101228
LA eng
NO Herraiz, J.L.; Bembibre, A.; López-Montes, A. Deep-Learning Based Positron Range Correction of PET Images. Appl. Sci. 2021, 11, 266. https://doi.org/10.3390/app11010266
NO National Institutes of Health (United States of America)
NO Gobierno de España
NO Comunidad Autónoma de Madrid
DS Docta Complutense
RD 7 abr 2025