<p>In this thesis, a two-dimensional pixel-wise deconvolution method for partial volume correction (PVC) for combined Positron Emission Tomography and Computer Tomography (PET/CT) imaging has been developed. The method is based on Van Cittert's deconvolution algorithm and includes a noise reduction method based on adaptive smoothing and median filters. Furthermore, a technique to take into account the position dependent PET point spread function (PSF) and to reduce ringing artifacts is also described. The quantitative and qualitative performance of the proposed PVC algorithm was evaluated using phantom experiments with varying object size, background and noise level. PVC results in an increased activity recovery as well as image contrast enhancement. However, the quantitative performance of the algorithm is impaired by the presence of background activity and image noise. When applying the correction on clinical PET images, the result was an increase in standardized uptake values, up to 98% for small tumors in the lung. These results suggest that the PVC described in this work significantly improves activity recovery without producing excessive amount of ringing artifacts and noise amplification. The main limitations of the algorithm are the restriction to two dimensions and the lack of regularization constraints based on anatomical information from the co-registered CT images.</p>

In this thesis, a two-dimensional pixel-wise deconvolution method for partial volume correction (PVC) for combined Positron Emission Tomography and Computer Tomography (PET/CT) imaging has been developed. The method is based on Van Cittert's deconvolution algorithm and includes a noise reduction method based on adaptive smoothing and median filters. Furthermore, a technique to take into account the position dependent PET point spread function (PSF) and to reduce ringing artifacts is also described. The quantitative and qualitative performance of the proposed PVC algorithm was evaluated using phantom experiments with varying object size, background and noise level. PVC results in an increased activity recovery as well as image contrast enhancement. However, the quantitative performance of the algorithm is impaired by the presence of background activity and image noise. When applying the correction on clinical PET images, the result was an increase in standardized uptake values, up to 98% for small tumors in the lung. These results suggest that the PVC described in this work significantly improves activity recovery without producing excessive amount of ringing artifacts and noise amplification. The main limitations of the algorithm are the restriction to two dimensions and the lack of regularization constraints based on anatomical information from the co-registered CT images.