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/

Zendy Plus

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

Zendy Tools

Zendy Open

Velocity-encoded MRI is an imaging technique used in different areas toassess flow motion. Some applications include medical imaging such ascardiovascular blood flow studies, and industrial settings in the areas ofrheology, pipe flows, and reactor hydrodynamics, where the goal is tocharacterise dynamic components of some quantity of interest. The problem ofestimating velocities from such measurements is a nonlinear dynamic inverseproblem. To retrieve time-dependent velocity information, careful mathematicalmodelling and appropriate regularisation is required. In this work, we proposean optimisation algorithm based on non-convex Bregman iteration to jointlyestimate velocity-, magnitude- and segmentation-information for the applicationof bubbly flow imaging. Furthermore, we demonstrate through numericalexperiments on synthetic and real data that the joint model improves velocity,magnitude and segmentation over a classical sequential approach.

Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data