title: Pressure mapping from flow imaging: enhancing computation of the viscous term through velocity reconstruction in near-wall regions.
abstract: Although being small compared to inertial acceleration, viscous component of the pressure gradient has recently emerged as a potential biomarker for aortic disease conditions including aortic valve stenosis. However, as it involves the computation of second order derivatives and viscous dissipation is locally higher in the near-wall region of the larger vessels, where the lowest local signal-to-noise ratios are encountered, the estimation process from medical image velocity data through mathematical models is highly challenging. We propose a fully automatic framework to recover the laminar viscous pressure gradient through reconstruction of the velocity vector field in the aortic boundary region. An in-silico study is conducted and the pressure drop is computed solving a Poisson problem on pressure using both a reconstructed and non-reconstructed velocity profile near the vessel walls, showing a global improvement of performance with the enhanced method.
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Donati
authors.forename: David A
authors.initials: DA
authors.lastname: Nordsletten
authors.forename: Nicolas P
authors.initials: NP
authors.lastname: Smith
authors.forename: Pablo
authors.initials: P
authors.lastname: Lamata
mesh_terms: D001011:Aorta / Q000503:physiopathology; D001024:Aortic Valve Stenosis / Q000175:diagnosis*; D001795:Blood Pressure Determination / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D012039:Regional Blood Flow; D059629:Signal-To-Noise Ratio; D014783:Viscosity