Combination of Wiener filtering and singular value decomposition filtering for volume imaging PET

Although the 3D multi-slice rebinning (MSRB) algorithm in PET is fast and practical, and provides image quality close to that of a 3D reprojection algorithm, the MSRB image, in general, suffers from the noise amplified by its singular value decomposition (SVD) filtering technique in the axial direction. The authors´ aim in this study is to combine the use of the Wiener filter (WF) with the SVD to decrease the noise and improve the image quality. Since the SVD filtering can “deconvolve” the spatially variant response function while the WF can suppress the noise and reduce the blurring caused by the physical processes not modeled by the axial SVD filter, the synthesis of these two techniques combine the advantages of both filters. The authors applied this approach to the volume imaging HEAD PENN-PET brain scanner with an axial extent of 256 mm. This combined filter was evaluated in terms of EWHM, image contrast, signal-to-noise, etc. With several phantoms, such as cold sphere and 3D brain phantoms. Specifically, the authors studied both the SVD filter with an axial Wiener filter and the SVD filter with a 3D Wiener filter, and compared the filtered images to those from the 3D reconstruction projection (3DRP) algorithm. The authors´ results indicated that the Wiener filter not only increases the signal/noise ratio but also improves the contrast. For the 3D brain phantom both the gray/white and ventricle/gray ratios were improved from 1.8 to 2.8 and 0.47 to 0.25, respectively. The overall performance is close to that of the 3DRP algorithm