Enhancing 18F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm

Enhancing 18F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm

Abstract Background As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting lesion diagnostics. It is crucial to ide...

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Bibliographic Details
Main Authors: Zhihao Chen, Hongxing Yang, Ming Qi, Wen Chen, Fei Liu, Shaoli Song, Jianping Zhang
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Cancer Imaging
Subjects:
PET image quality
Deep progressive learning (DPL)
Ordered subset expectation maximization (OSEM)
BMI
Lesion diagnostic performance
Online Access:https://doi.org/10.1186/s40644-025-00877-x
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https://doi.org/10.1186/s40644-025-00877-x

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