| dc.description.abstract | PET imaging has an important role in early detection of cancer. However, the data acquired during a scan contains noise, which degrades image quality. One of the major contributing factors is scatter, which can be modeled to correct noisy data. Open-source scatter correction software that models this is available, but only for clinical PET scanners. Meanwhile, the field is shifting towards TB-PET scanners, which have better image quality enabled by the increased axial extent of the detector volumes that cause an increase in the amount of data. This poses additional challenges to the development of scatter correction software for TBPET. In this study, an optimized version of the TOF-aware SSS software OpenSSS is proposed in Python, tailored to the needs of TB-PET. The aim of this study is to investigate whether we can optimize SSS for TB-PET to have good quantitative performance with short running time for clinical implementation. The investigation is done through a parameter study. The results show that for a reference TB-PET dataset, a similar quality of the TB-PET dataset can be maintained while OpenSSS runs 5 times faster. In conclusion, SSS can be optimized to run on TB-PET while maintaining similar quality of the data. | |
