Dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN): a computed tomography-integrated multimodal network for positron emission tomography denoising.

ABSTRACT:

Background

Positron emission tomography (PET) requires injection of radioactive tracers, which entails concerns regarding radiation exposure. Thus, there is a need for low-dose PET (LDPET) imaging that maintains full-dose PET (FDPET) quality while reducing tracer dosage. However, dose reduction often degrades image quality. Although deep learning methods have shown promise, denoising small lesions remains challenging. To address this, we propose a dual-branch network that incorporates structural information from computed tomography (CT) to enhance LDPET quality, preserve fine details, and improve small-lesion imaging.

Methods

We propose the dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN), a dual-branch multimodal network that leverages structura

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12682464 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature