Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation

Qigang Long; Yan Tian; Yun Hu; Zhenchun Xu; Pu Liao; Shanshan Xu; Wei Liu; Jingzheng Jin; Yunsong Peng

doi:10.1186/s40658-026-00841-z

January 31, 2026EJNMMI PhysicsOpen Access

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation

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Key Points

This evaluation aims to compare scanner-integrated reconstruction and post-processing deep learning methods for improving image quality in 18F-FDG PET/CT scans.
Compared two imaging techniques: Scanner-Integrated Reconstruction (DPR) and Post-Processing Deep Learning (POST).
Assessed improvements in image quality and scan-time reduction.
Evaluated agreement with clinical reference standards.
Both techniques improved or preserved image quality during scans.
POST was effective at 1/4 acquisition time, while DPR was better for 1/2 time or more.

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Implication

Comparative clinical evaluation demonstrates image quality improvement in low-count 18F-FDG PET/CT scans, suggesting effective time-saving techniques.

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Long et al. (2026) studied this question.

synapsesocial.com/papers/6980ff26c1c9540dea811ee1 https://doi.org/10.1186/s40658-026-00841-z

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Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation

Key Points

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Implication

Abstract

Discussion

Cite This Study

Scanner-integrated reconstruction versus post-processing deep learning for low-count 18F-FDG PET/CT: a comparative clinical evaluation

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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