QUANTOF: multi-GPU-based list-mode fully quantitative TOF PET image reconstruction

Positron Emission Tomography (PET) plays a pivotal role in the early detection and precise diagnosis of various diseases, including cancer, neurological disorders, and heart conditions. Ordered subset expectation maximization (OSEM) algorithm is the most commonly used image reconstruction algorithm. To address the high computational costs associated with this algorithm, numerous studies have explored the effectiveness of GPU acceleration. However, these studies have predominantly focused on the forward and backward projection components of iterative algorithms, neglecting correction techniques which are essential for PET image reconstruction, such as scatter correction. This work introduces a multi-GPU-based list-mode fully quantitative Time-of-Flight (TOF) PET image reconstruction technique, named QUANTOF, that integrates all corrections during PET image iterative reconstruction on GPUs. Additionally, optimizations have been applied to the backprojection algorithm, resulting in a two-fold increase in speed compared to the pre-optimized version. Validation using experimental datasets from a Siemens Biograph Vision PET/CT scanner demonstrates that this technique significantly reduces reconstruction time costs while achieving exceptional quantitative accuracy.