MOTION CORRECTED PET SIGNALS COMPRESSING

摘要

This article delivers the new compression sensing based super-resolution algorithm for enhancing the image resolution in clinical positron emission tomography (PET) scanners. The concerns of this technique are motion artifacts. The PET measurements are being gathered over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artifacts are the typical side effect. These may lead to unreadable scans. It is exposed that the presented algorithm improves PET spatial resolution in cases when Compressed Sensing (CS) sequences are applied. Compressed sensing is able to reconstruct signals from significantly fewer measurements than were traditionally thought necessary. The application of CS to PET has the value for significant scan time reductions, with visible benefits for patients and health care economics. In this work the objective is to combine Super-Resolution image enhancement algorithm with CS framework to achieve high resolution PET output keeping the scans free of motion artifacts. Both methods focus on maximizing image sparsity on known sparse transform domain and minimizing fidelity.