GPU‐based list‐mode TOF PET image reconstruction with complete correction techniques

Background Positron emission tomography (PET) is essential for the early diagnosis of cancer, neurological disorders, and cardiovascular diseases. However, achieving high‐quality PET images remains challenging due to the complex physical factors involved and the trade‐off between reconstruction accuracy and computational efficiency. Purpose This study aimed to develop QuanTOF, a GPU‐accelerated PET reconstruction framework integrating comprehensive physical corrections and advanced modeling to enhance image quality while maintaining clinical practicality. Methods QuanTOF employs a GPU‐accelerated Bayesian penalized‐likelihood reconstruction algorithm with time‐of‐flight (TOF) and point spread function (PSF) modeling. It incorporates full corrections for attenuation, normalization, random coincidences, and scatter coincidences. A memory‐efficient TOF single scatter simulation (SSS) algorithm enabled on‐the‐fly scatter correction without storing full TOF sinograms. Validation included Monte Carlo simulations, clinical phantom experiments, and blinded reader studies using a Siemens Biograph Vision PET/CT scanner. Results In phantom studies, QuanTOF achieved uniformity comparable to commercial systems and resolved 2.4 mm hot rods in Derenzo phantoms, with peak‐to‐valley ratios of 2.61 (3.2 mm rods) and 1.29 (2.4 mm rods). Scatter correction time was reduced to s, two orders of magnitude faster than existing methods. Clinicians rated QuanTOF images significantly higher (average score: 3.90 vs. 2.15 for clinical images) in reader studies. Reconstruction times remained clinically acceptable (e.g., 2.78 s for NEMA phantom scatter correction) Conclusions QuanTOF balances accuracy and efficiency through GPU‐optimized physics modeling and memory‐efficient algorithms. It delivers high‐resolution PET images with diagnostic confidence, demonstrating potential for clinical oncology, neurology, and cardiology applications.