Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.

Abstract

PURPOSE Efforts to reduce the radiation burden of PET/CT have driven the increasing development of AI-based CT-less PET imaging techniques. However, comprehensive clinical evaluations of these approaches remain limited. This study aimed to rigorously assess whether deep learning (DL)-based PET reconstruction can eliminate the need for CT-derived attenuation and scatter correction while maintaining image quality sufficient for reliable clinical diagnosis. METHODS In this dual-center retrospective analysis, raw PET/CT data from 359 patients were evaluated across 4 scanners and 4 tracers. Each dataset underwent four reconstruction approaches: (1) CT-based attenuation and scatter correction (CT-ASC, reference standard); (2) conventional 2D-DL; (3) conventional 3D-DL; and (4) our novel Decomposition-based DL algorithm. Diagnostic quality of reconstructed images was systematically assessed via visual scoring (5-point Likert scale), diagnostic accuracy (lesion-based false-positive/negative rates), and semi-quantitative metrics (SUVmax consistency). RESULTS Visual analysis demonstrated the superior performance of Decomposition-based DL compared to conventional 2D-DL and 3D-DL (p < 0.001 for all comparisons). Furthermore, the proposed method exhibited the lowest false-negative and false-positive rates (0.56% false positives with SIEMENS Vision 600; zero rates in other cases). Semi-quantitative analysis showed that although Decomposition-based DL did not consistently yield the lowest mean absolute percentage error values compared to controls, it maintained strong agreement with CT-ASC in most cases. CONCLUSION This dual-center study demonstrates that decomposition-based DL CT-free PET imaging outperforms conventional DL methods, achieving diagnostic accuracy comparable to CT-based attenuation correction in most cases. This clinical evaluation provides valuable insights to guide further methodological development and support clinical translation of CT-free PET imaging.