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

Cone-beam computed tomography (CBCT) systems, with their portability, presenta promising avenue for direct point-of-care medical imaging, particularly incritical scenarios such as acute stroke assessment. However, the integration ofCBCT into clinical workflows faces challenges, primarily linked to long scanduration resulting in patient motion during scanning and leading to imagequality degradation in the reconstructed volumes. This paper introduces a novelapproach to CBCT motion estimation using a gradient-based optimizationalgorithm, which leverages generalized derivatives of the backprojectionoperator for cone-beam CT geometries. Building on that, a fully differentiabletarget function is formulated which grades the quality of the current motionestimate in reconstruction space. We drastically accelerate motion estimationyielding a 19-fold speed-up compared to existing methods. Additionally, weinvestigate the architecture of networks used for quality metric regression andpropose predicting voxel-wise quality maps, favoring autoencoder-likearchitectures over contracting ones. This modification improves gradient flow,leading to more accurate motion estimation. The presented method is evaluatedthrough realistic experiments on head anatomy. It achieves a reduction inreprojection error from an initial average of 3mm to 0.61mm after motioncompensation and consistently demonstrates superior performance compared toexisting approaches. The analytic Jacobian for the backprojection operation,which is at the core of the proposed method, is made publicly available. Insummary, this paper contributes to the advancement of CBCT integration intoclinical workflows by proposing a robust motion estimation approach thatenhances efficiency and accuracy, addressing critical challenges intime-sensitive scenarios.

A gradient-based approach to fast and accurate head motion compensation in cone-beam CT