A multi-scale ResNet and Swin Transformer fusion network with channel attention predicts PET/CT image quality scores from a new 2,700-image radiologist-labeled dataset and outperforms existing IQA methods.
An artificial intelligence-driven image quality assessment system for whole-body [18F] FDG PET/CT
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment
A multi-scale ResNet and Swin Transformer fusion network with channel attention predicts PET/CT image quality scores from a new 2,700-image radiologist-labeled dataset and outperforms existing IQA methods.