Dynamic positron emission tomography image reconstruction using spatiotemporal kernel method with deep image prior.

ABSTRACT:

Background

The inherently ill-posed nature of tomographic reconstruction, combined with the low photon counts in short acquisition frames, poses significant challenges for achieving high-quality dynamic positron emission tomography (PET) images. Recent studies have shown that deep learning methods, particularly the deep image prior (DIP) framework, can effectively enhance PET image reconstruction without requiring external training data. This study aimed to develop a novel DIP-based reconstruction algorithm integrated with a spatiotemporal kernel and to validate its effectiveness in improving the quality of reconstructed dynamic PET images.

Methods

We constructed a novel dynamic PET reconstruction algorithm that integrates a spatiotemporal kernel with a DIP framework. Specif

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC12682561 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature