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

Although the three-dimensional (3D) multi-slice rebinning (MSRB) algorithm in PET is fast and practical, and provides an accurate reconstruction, the MSRB image, in general, suffers from the noise amplified by its singular value decomposition (SVD) filtering operation in the axial direction. Our 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. The SVD filtering “deconvolves” the spatially variant axial response function while the WF suppresses the noise and reduces the blurring not modeled by the axial SVD filter but included in the system modulation transfer function. Therefore, the synthesis of these two techniques combines the advantages of both filters. We 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 spatial resolution, image contrast, and signal-to-noise ratio with several phantoms, such as a cold sphere phantom and 3D brain phantom. Specifically, we 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 reprojection (3DRP) reconstruction algorithm. Our results indicate that the Wiener filter increases the signal to noise ratio and also improves the contrast. For the MSRB images of the 3D brain phantom, after 3D WF, both the Gray/White and Gray/Ventricle ratios were improved from 1.8 to 2.8 and 2.1 to 4.1, respectively. In addition, the image quality with the MSRB algorithm is close to that of the 3DRP algorithm with 3D WF applied to both image reconstructions