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Clinical validation of a deep learning model for low-count PET image enhancement
Qigang Long1, Yan Tian2, Boyang Pan3
1School of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China.
Summary
The RaDynPET deep learning model significantly improves image quality in reduced-count 18F-FDG PET/CT scans. This method maintains diagnostic accuracy, allowing for faster whole-body PET examinations.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Nuclear Medicine
Background:
- Whole-body positron emission tomography (PET) imaging, particularly with 18F-FDG, is crucial for staging and monitoring various cancers.
- Standard PET acquisition times can be lengthy, impacting patient comfort and throughput.
- Reducing PET scan duration while preserving diagnostic image quality is a significant clinical challenge.
Purpose of the Study:
- To evaluate the efficacy of the RaDynPET deep learning model in enhancing image quality for whole-body PET examinations with a fourfold reduction in acquisition time.
- To assess the impact of RaDynPET on quantitative metrics, including Standardized Uptake Values (SUV), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).
- To validate the performance of RaDynPET in lesion detection and diagnostic accuracy using both internal and external patient cohorts.
Main Methods:
- 120 patients undergoing 18F-FDG PET/CT were included, with data reconstructed using OSEM from 120-s (G120) and 30-s (G30) list-mode data.
- The RaDynPET model generated enhanced images (R30) from G30 data, which were then compared to G30 and G120.
- Image quality was assessed by nuclear medicine physicians using a 5-point Likert scale, alongside quantitative analysis of SUV, SNR, lesion tumor-to-background ratio (TBR), and contrast-to-noise ratio (CNR).
Main Results:
- RaDynPET (R30) significantly improved subjective image quality compared to both G30 and G120 in internal cohorts.
- R30 demonstrated excellent agreement with G120 for SUV values and superior liver SNR and CNR.
- In external cohorts, R30 maintained strong SUV agreement and achieved high lesion detection sensitivity (95.45%) and specificity (98.41%), comparable to G120.
Conclusions:
- The RaDynPET model successfully restores high image quality in 18F-FDG PET scans acquired with 25% of the standard time.
- RaDynPET maintains quantitative accuracy (SUV agreement) essential for clinical interpretation.
- This deep learning approach enables significantly faster whole-body PET imaging without compromising diagnostic performance.
