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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.

physics in medicine and biology/physics in medicine and biology

Towards automated in vivo tracheal mucociliary transport measurement: Detecting and tracking particle movement in synchrotron phase-contrast x-ray images