Unsupervised deep learning framework for data-driven gating in positron emission tomography.

ABSTRACT:

Background

Physiological motion, such as respiratory motion, has become a limiting factor in the spatial resolution of positron emission tomography (PET) imaging as the resolution of PET detectors continue to improve. Motion-induced misregistration between PET and CT images can also cause attenuation correction artifacts. Respiratory gating can be used to freeze the motion and to reduce motion induced artifacts.

Purpose

In this study, we propose a robust data-driven approach using an unsupervised deep clustering network that employs an autoencoder (AE) to extract latent features for respiratory gating.

Methods

We first divide list-mode PET data into short-time frames. The short-time frame images are reconstructed without attenuation, scatter, or randoms correction to

SUBMITTER: Li T 

PROVIDER: S-EPMC10592231 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature