Joint Phase Reconstruction and Magnitude Segmentation from Velocity-Encoded MRI Data

Veronica Corona (), Martin Benning (), Lynn F. Gladden (), Andi Reci (), Andrew J. Sederman () and Carola-Bibiane Schönlieb ()
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Veronica Corona: University of Cambridge, Department of Applied Mathematics and Theoretical Physics
Martin Benning: Queen Mary University of London, School of Mathematical Sciences
Lynn F. Gladden: University of Cambridge, Department of Chemical Engineering and Biotechnology
Andi Reci: University of Cambridge, Department of Chemical Engineering and Biotechnology
Andrew J. Sederman: University of Cambridge, Department of Chemical Engineering and Biotechnology
Carola-Bibiane Schönlieb: University of Cambridge, Department of Applied Mathematics and Theoretical Physics

A chapter in Time-dependent Problems in Imaging and Parameter Identification, 2021, pp 1-24 from Springer

Abstract: Abstract Velocity-encoded MRI is an imaging technique used in different areas to assess flow motion. Some applications include medical imaging such as cardiovascular blood flow studies, and industrial settings in the areas of rheology, pipe flows, and reactor hydrodynamics, where the goal is to characterise dynamic components of some quantity of interest. The problem of estimating velocities from such measurements is a nonlinear dynamic inverse problem. To retrieve time-dependent velocity information, careful mathematical modelling and appropriate regularisation is required. In this work, we use an optimisation algorithm based on non-convex Bregman iteration to jointly estimate velocity-, magnitude- and segmentation-information for the application of bubbly flow imaging. Furthermore, we demonstrate through numerical experiments on synthetic and real data that the joint model improves velocity, magnitude and segmentation over a classical sequential approach.

Date: 2021
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DOI: 10.1007/978-3-030-57784-1_1

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