Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

ABSTRACT: PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using ¹⁸ F-FDG, ¹⁸ F-DOPA, ¹⁸ F-Flortaucipir (targeting tau pathology), and ¹⁸ F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT

SUBMITTER: Arabi H 

PROVIDER: S-EPMC7416024 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature