Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126080
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Type: Journal article
Title: Towards automated <i>in vivo</i> tracheal mucociliary transport measurement: detecting and tracking particle movement in synchrotron phase-contrast X-ray images
Other Titles: Towards automated in vivo tracheal mucociliary transport measurement: detecting and tracking particle movement in synchrotron phase-contrast X-ray images
Author: Gardner, M.
Parsons, D.
Morgan, K.S.
McCarron, A.
Cmielewski, P.
Gradl, R.
Donnelley, M.
Citation: Physics in Medicine and Biology, 2020; 65(14):145012-1-145012-17
Publisher: IOP Publishing
Issue Date: 2020
ISSN: 0031-9155
1361-6560
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Responsibility: 
Mark Gardner, David Parsons, Kaye Morgan, Alexandra McCarron, Patricia Cmielewski, Regine Gradl, and Martin Donnelley
Abstract: Accurate in vivo quantification of airway mucociliary transport (MCT) in animal models is important for understanding diseases such as cystic fibrosis, as well as for developing therapies. A non-invasive method of measuring MCT behaviour, based on tracking the position of micron sized particles using Synchrotron X-ray imaging, has previously been described. In previous studies, the location (and path) of each particle was tracked manually, which is a time consuming and subjective process. Here we describe particle tracking methods that were developed to reduce the need for manual particle tracking. The MCT marker particles were detected in the Synchrotron X-ray images using cascade classifiers. The particle trajectories along the airway surface were generated by linking the detected locations between frames using a modified particle linking algorithm. The developed methods were compared with the manual tracking method on simulated X-ray images, as well as on in vivo images of rat airways acquired at the SPring-8 Synchrotron. The results for the simulated and in vivo images showed that the semi-automatic algorithm reduced the time required for particle tracking when compared with the manual tracking method, and was able to detect MCT marker particle locations and measure particle speeds more accurately than the manual tracking method. Future work will examine the modification of methods to improve particle detection and particle linking algorithms to allow for more accurate fully-automatic particle tracking.
Keywords: Automation; Cystic Fibrosis; Mucociliary Transport; Particle Tracking; Phase-Contrast X-Ray; Sychrotron
Rights: © 2020 Institute of Physics and Engineering in Medicine. Original Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
RMID: 1000014189
DOI: 10.1088/1361-6560/ab7509
Grant ID: http://purl.org/au-research/grants/nhmrc/GNT1079712
http://purl.org/au-research/grants/arc/FT180100374
Appears in Collections:Medicine publications

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