Multigrid Reconstruction in Tomographic Imaging

• Published in: IEEE transactions on radiation and plasma medical sciences 2020-05, Vol.4 (3), p.300-310
• Main Authors: Marlevi, David, Kohr, Holger, Buurlage, Jan-Willem, Gao, Bo, Batenburg, K. Joost, Colarieti-Tosti, Massimiliano
• Format: Article
• Language: English
• Subjects: Algorithms; Biomedical imaging; Computed tomography; Computer applications; Computer simulation; Computing costs; Discretization; Domains; Error analysis; Field of view; Image processing; Image reconstruction; Imaging; Inverse problems; Iterative methods; multigrid; Radio frequency; reconstruction; Regularization; tomography
• ISSN: 2469-7311; 2469-7303; 2469-7303
• DOI: 10.1109/TRPMS.2019.2942186

In this article, we present an efficient methodology for multigrid tomographic image reconstruction from nontruncated projection data. By partitioning the reconstruction domain and adapting the forward and backward operators, an image can be reconstructed accurately within multiple domains of varying discretization or regularization. We demonstrate the efficacy of the multigrid reconstruction principle using simulated data for quantitative assessment and experimental measurements from a \mu -CT scanner for a clinically relevant use case scenario. A major advantage of using multiple reconstruction grids is the possibility to drastically reduce the number of unknowns in the inverse problem, and thereby the associated computational cost. This cost reduction helps to enlarge the class of available algorithms in applications with strict limitations on computation time or resources, and it enables full system resolution reconstruction of subregions that would otherwise be infeasible for the full field of view. The numerical experiments, along with a brief error analysis, show that the expected artefacts from coarse discretization outside the region of interest become noticeable only for large differences in discretization between subregions.