Model-based image analysis of a tethered Brownian fibre for shear stress sensing

Gallagher, M. T. and Neal, C. V. and Arkill, K. P. and Smith, D. J. (2017) Model-based image analysis of a tethered Brownian fibre for shear stress sensing. Journal of the Royal Society Interface, 14 (137). 20170564/1-20170564/13. ISSN 1742-5662

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The measurement of fluid dynamic shear stress acting on a biologically relevant surface is a challenging problem, particularly in the complex environment of, for example, the vasculature. While an experimental method for the direct detection of wall shear stress via the imaging of a synthetic biology nanorod has recently been developed, the data interpretation so far has been limited to phenomeno-logical random walk modelling, small-angle approximation, and image analysis techniques which do not take into account the production of an image from a three-dimensional subject. In this report, we develop a mathematical and statistical framework to estimate shear stress from rapid imaging sequences based firstly on stochastic modelling of the dynamics of a tethered Brownian fibre in shear flow, and secondly on a novel model-based image analysis, which reconstructs fibre positions by solving the inverse problem of image formation. This framework is tested on experimental data, providing the first mechanistically rational analysis of the novel assay. What follows further develops the established theory for an untethered particle in a semi-dilute suspension, which is of relevance to, for example, the study of Brownian nanowires without flow, and presents new ideas in the field of multi-disciplinary image analysis.

Item Type: Article
Keywords: image analysis; wall shear stress; Brownian dynamics; regularized stokeslets; mathematical modelling; viscous fluid dynamics
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Cancer and Stem Cells
Identification Number: 10.1098/rsif.2017.0564
Depositing User: Arkill, Kenton
Date Deposited: 11 Dec 2017 09:13
Last Modified: 13 Dec 2017 05:39

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