High Resolution Respiratory Motion Artefacts Free Image Reconstruction Algorithm in Pet

PET is a scanning technique in medical imaging based research. It allows us, for the first time, to measure in detail the functioning of distinct areas of the human brain while the patient is comfortable, conscious and alert. This work presents new super-resolution algorithm for improving the resolution in clinical positron emission tomography (PET) scanners. Super-resolution images were obtained by combining a couple of data sets with non-rigid motion between acquisitions and applying the generalized back-projection algorithm [1]. I extended this approach to consider also internal organ motion. The problem of motion is well known in positron emission tomography (PET) studies. The PET images are being formed 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. The algorithm improvements resulted in lower approximation error, absence of motion artefacts and higher convergence speed. All the data is being gathered one small lesion which has been performed using axial shifts without increasing the patient radiation exposure. The resulting, high resolution images have better resolution than the original images The new technique for improving the resolution of PET images has passed the preliminary tests and has been confirmed by multiple lines of empirical evidence.