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

Breast X-ray CT imaging is being considered in screening as an extension tomammography. As a large fraction of the population will be exposed toradiation, low-dose imaging is essential. Iterative image reconstruction basedon solving an optimization problem, such as Total-Variation minimization, showspotential for reconstruction from sparse-view data. For iterative methods it isimportant to ensure convergence to an accurate solution, since important imagefeatures, such as presence of microcalcifications indicating breast cancer, maynot be visible in a non-converged reconstruction, and this can have clinicalsignificance. To prevent excessively long computational times, which is apractical concern for the large image arrays in CT, it is desirable to keep thenumber of iterations low, while still ensuring a sufficiently accuratereconstruction for the specific imaging task. This motivates the study ofaccurate convergence criteria for iterative image reconstruction. In simulationstudies with a realistic breast phantom with microcalcifications we comparedifferent convergence criteria for reliable reconstruction. Our results showthat it can be challenging to ensure a sufficiently accurate microcalcificationreconstruction, when using standard convergence criteria. In particular, thegray level of the small microcalcifications may not have converged long afterthe background tissue is reconstructed uniformly. We propose the use of theindividual objective function gradient components to better monitor possibleregions of non-converged variables. For microcalcifications we find empiricallya large correlation between nonzero gradient components and non-convergedvariables, which occur precisely within the microcalcifications. This supportsour claim that gradient components can be used to ensure convergence to asufficiently accurate reconstruction.

Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT