PET image reconstruction using compressed sensing

PET is a scanning procedure in medical imaging based research. It provides measurements of functioning in distinct areas of the human brain while the patient is comfortable, conscious and alert. This work presents new compression sensing based super-resolution algorithm for improving the resolution in clinical positron emission tomography (PET) scanners. The problem of motion artifacts is well known in positron emission tomography (PET) studies. The PET images are being acquired over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artefacts are the usual result. These may lead to wrong diagnosis. It is shown that the approach improves PET spatial resolution in cases Compressed Sensing (CS) sequences are used. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. The use of CS to PET has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the goal is to combine Super-Resolution image enhancement algorithm with CS framework to achieve high resolution PET output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity.