Quantification and optimization techniques for dynamic brian imaging in high resolution positron emission tomography

Modern positron emission tomography (PET) scanners have a large number of detector crystals which corresponds to an even larger number of lines of response (LOR) addressing the coincidence events. This increasing complexity makes the image reconstruction much more challenging especially in dynamic imaging where the acquired number of counts per frame or per LOR is much less as compared to that in the static case. The low statistical quality of the data thus degrades the quantitative accuracy of the images. Moreover, the increasing number of LORs and dynamic frames requires a longer computational time and larger data storage for the image reconstruction task. A dual reconstruction scheme, a novel scatter calibration, and a practical scatter and randoms approximation methods were developed in this work. These methods have been validated using phantom, non-human primate, and human studies and have been demonstrated to improve the quantification accuracy of the images, to accelerate the image formation task, and to reduce the data storage requirement for dynamic brain imaging in high resolution PET. In conclusion, these studies contribute to increasing the accuracy and to decreasing the computational burden for dynamic high resolution quantitative PET imaging. The proposed methods are modular and can be applied to any PET scanners with the exception of the dual reconstruction scheme which requires list-mode acquisition capability. These methods are particularly beneficial for high resolution scanners which have a large number of LORs, such as the high resolution research tomograph (HRRT).Science, Faculty ofPhysics and Astronomy, Department ofGraduat