2019/08/14 by Veronica Corona, Corona, Veronica, Martin Benning +9 · 1 citation
Engineering · #Drilling and Well Engineering #FOS: Electrical engineering #FOS: Mathematics #Hydraulic Fracturing and Reservoir Analysis #Image and Video Processing (eess.IV) #Numerical Analysis (math.NA) #Reservoir Engineering and Simulation Methods #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.05285
openalex publication_date 2019/08/14 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Velocity-encoded MRI is an imaging technique used in different areas to\nassess flow motion. Some applications include medical imaging such as\ncardiovascular blood flow studies, and industrial settings in the areas of\nrheology, pipe flows, and reactor hydrodynamics, where the goal is to\ncharacterise dynamic components of some quantity of interest. The problem of\nestimating velocities from such measurements is a nonlinear dynamic inverse\nproblem. To retrieve time-dependent velocity information, careful mathematical\nmodelling and appropriate regularisation is required. In this work, we propose\nan optimisation algorithm based on non-convex Bregman iteration to jointly\nestimate velocity-, magnitude- and segmentation-information for the application\nof bubbly flow imaging. Furthermore, we demonstrate through numerical\nexperiments on synthetic and real data that the joint model improves velocity,\nmagnitude and segmentation over a classical sequential approach.\n