Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment
Yihuan Lu,
Fei Kang,
Duo Zhang
et al.
Abstract:Purpose
Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment.
Methods
In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Pan… Show more
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