ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.

ABSTRACT:

Background

The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework i

SUBMITTER: Vashistha R 

PROVIDER: S-EPMC11374951 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature