A deep learning-based whole-body solution for PET/MRI attenuation correction.

ABSTRACT:

Background

Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system.

Materials and methods

Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretr

SUBMITTER: Ahangari S 

PROVIDER: S-EPMC9385907 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature