NDLI: Pressure mapping from flow imaging: Enhancing computation of the viscous term through velocity reconstruction in near-wall regions

Content Provider	IEEE Xplore Digital Library
Author	Donati, F. Nordsletten, D.A. Smith, N.P. Lamata, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Biomed. Eng., King's Coll. London, London, UK (Donati, F.; Nordsletten, D.A.; Smith, N.P.; Lamata, P.)
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.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	5097
Ending Page	5100
File Size	1180326
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944771
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Signal to noise ratio Estimation Biomedical imaging Accuracy Mathematical model Boundary conditions
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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