Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with ¹⁸F-FDG, ⁶⁸ Ga-DOTATATE, and ¹⁸F-Fluciclovine.

ABSTRACT: A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using ¹⁸F-FDG, ⁶⁸ Ga-DOTATATE, and ¹⁸F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT).

Methods

Clinical whole-body PET/CT datasets of ¹⁸F-FDG (N = 113), ⁶⁸ Ga-DOTATATE (N = 76), and ¹⁸F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural netw

SUBMITTER: Toyonaga T 

PROVIDER: S-EPMC10725742 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature