English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The generalized minimal residual (GMRES) algorithm is applied to imagereconstruction using linear computed tomography (CT) models. The GMRESalgorithm iteratively solves square, non-symmetric linear systems and it haspractical application to CT when using unmatched back-projector/projector pairsand when applying preconditioning. The GMRES algorithm is demonstrated on a 3DCT image reconstruction problem where it is seen that use of unmatchedprojection matrices does not prevent convergence, while using an unmatched pairin the related conjugate gradients for least-squares (CGLS) algorithm leads todivergent iteration. Implementation of preconditioning using GMRES is alsodemonstrated.

Iterative image reconstruction for CT with unmatched projection matrices using the generalized minimal residual algorithm