Validation of Deep Learning-based Augmentation for Reduced ¹⁸F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma.

ABSTRACT:

Purpose

To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/MRI for correct treatment response assessment of children and young adults with lymphoma.

Materials and methods

In this secondary analysis of prospectively collected data (ClinicalTrials.gov identifier: NCT01542879), 20 patients with lymphoma (mean age, 16.4 years ± 6.4 [standard deviation]) underwent ¹⁸F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose ¹⁸F-FDG PET data (3 MBq/kg) were simulated to lower ¹⁸F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25% ¹⁸F-FDG

SUBMITTER: Theruvath AJ 

PROVIDER: S-EPMC8637226 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature