Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

ABSTRACT: Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach ar

SUBMITTER: Guo R 

PROVIDER: S-EPMC9537165 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature