Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks.

ABSTRACT: BACKGROUND:Attenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however-when CT- or MR-derived attenuation maps are corrupted or CT acquisition solely for the purpose of AC shall be avoided-it would be of value to have the possibility of obtaining attenuation maps only based on PET information. The purpose of this study was to thus develop, implement, and evaluate a deep learning-based method for whole body [18F]FDG-PET AC which is independent of other imaging modalities for acquiring the attenuation map. METHODS:The proposed method is investigated on whole body [18F]FDG-PET data using a Generative Adversarial Networks (GAN) deep learning framework. It is trained to generate pseudo CT images (CTGAN

SUBMITTER: Armanious K 

PROVIDER: S-EPMC7246235 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature