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 need for tomographic reconstruction from sparse measurements arises whenthe measurement process is potentially harmful, needs to be rapid, or isuneconomical. In such cases, information from previous longitudinal scans ofthe same object helps to reconstruct the current object while requiringsignificantly fewer updating measurements. Our work is based on longitudinaldata acquisition scenarios where we wish to study new changes that evolvewithin an object over time, such as in repeated scanning for diseasemonitoring, or in tomography-guided surgical procedures. While this is easilyfeasible when measurements are acquired from a large number of projectionviews, it is challenging when the number of views is limited. If the goal is totrack the changes while simultaneously reducing sub-sampling artefacts, wepropose (1) acquiring measurements from a small number of views and using aglobal unweighted prior-based reconstruction. If the goal is to observe detailsof new changes, we propose (2) acquiring measurements from a moderate number ofviews and using a more involved reconstruction routine. We show that in thelatter case, a weighted technique is necessary in order to prevent the priorfrom adversely affecting the reconstruction of new structures that are absentin any of the earlier scans. The reconstruction of new regions is safeguardedfrom the bias of the prior by computing regional weights that moderate thelocal influence of the priors. We are thus able to effectively reconstruct boththe old and the new structures in the test. In addition to testing on simulateddata, we have validated the efficacy of our method on real tomographic data.The results demonstrate the use of both unweighted and weighted priors indifferent scenarios.

Tomographic reconstruction to detect evolving structures