2018/03/31 by Ranjan Maitra
Computer Science · Mathematics · Medicine · Physics and Astronomy · #Algorithm #Artificial intelligence #Bandwidth (computing) #Circulant matrix #Computer science #Computer vision #Eigendecomposition of a matrix #Eigenvalues and eigenvectors #Filter (signal processing) #Filter design #Iterative reconstruction #Mathematics #Medical Imaging Techniques and Applications #Physics #Radiation Detection and Scintillator Technologies #Reconstruction filter #Root-raised-cosine filter #Signal processing #Signal reconstruction #Target Tracking and Data Fusion in Sensor Networks #Telecommunications #stat.AP #stat.CO #stat.ME
paper · pdf · doi:10.1109/tip.2019.2919428
12 pages, 7 figures, submitted to IEEE Transactions on Image Processing
arxiv created 2019/04/21 · openalex publication_date 2019/06/04 · arxiv updated 2019/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
A generalized cross-validation approach to estimate the reconstruction filter bandwidth in 2D filtered backprojection is presented. The method writes the reconstruction equation in equivalent backprojected filtering form, derives results on eigendecomposition of symmetric 2D circulant matrices, and applies them to make bandwidth estimation a computationally efficient operation within the context of standard backprojected filtering reconstruction. Performance evaluations on a range of simulated emission tomography experiments give promising results. The superior performance holds at both low and high total expected counts, pointing to the method's applicability even in weak signal-to-noise-ratio situations. The approach also applies to the more general class of elliptically symmetric filters, with the reconstructed estimate's performance often better than even that obtained with the true optimal radially symmetric filter.