Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.

ABSTRACT:

Purpose

Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.

Methods

Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-

SUBMITTER: Shiri I 

PROVIDER: S-EPMC9742659 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature