3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

OBJECTIVES

Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.

METHODS

This retrospective study was performed on 456 participants respectively scanned by three different PET scanners with two different tracers. A DL method called spatially aware noise reduction network (SANR) was proposed to recover 3D full-dose (FD) PET volumes from LD data. The performance of SANR was compared with a 2D DL method taking regular FD PET volumes as the reference. Wilcoxon signed-rank test was conducted to compare the image quality metrics across different DL denoising methods. For clinical evaluation, two nuclear medicine physicians examined the recovered FD PET volumes using a 5-point grading scheme (5 = excellent) and gave a binary decision (negative or positive) for diagnostic quality assessment.

RESULTS

Statistically significant differences (p < 0.05) were found in terms of image quality metrics when SANR was compared with the 2D DL method. For clinical evaluation, SANR achieved a lesion detection accuracy of 95.3% (95% CI: 90.1%, 100%), while the reference full-dose PET volumes obtained a lesion detection accuracy of 98.4% (95% CI: 95.4%, 100%). In Alzheimer's disease diagnosis, both the reference FD PET volumes and the FD PET volumes recovered by SANR exhibited the same accuracy.

CONCLUSION

Compared with reference FD PET, LD PET denoised by the proposed approach significantly reduced radiotracer dosage and showed noninferior diagnostic performance in brain lesion detection and Alzheimer's disease diagnosis.

KEY POINTS

Question The current trend in PET imaging is to reduce injected dosage, which leads to low-quality PET images and reduces diagnostic efficacy. Findings The proposed deep learning method could recover diagnostic quality PET images from data acquired with a markedly reduced radiotracer dosage. Clinical relevance The proposed method would enhance the utility of PET scanning at lower radiotracer dosage and inform future workflows for brain lesion detection and Alzheimer's disease diagnosis, especially for those patients who need multiple examinations.