Regressive cross-correlation of pressure signals in the region of stenosis: Insights from particle image velocimetry experimentation

P. D. Docherty*, P. H. Geoghegan, L. Huetter, M. Jermy, M. Sellier

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Various anomalies in arterial geometry can cause serious hemodynamic dysfunction. In particular, stenosed arteries can cause reduced blood flow, excess stress on the heart, and elements can shear off causing blockage, which in the brain leads to stroke. This research assesses whether pressure signals obtained close to a stenosis are distinct from signals observed in other areas of the artery. Particle image velocimetry was used to determine the fluid velocity field within a compliant phantom that mimicked a stenosis in the carotid artery during physiological pulsatile pressure waves. The Navier-Stokes representation of the velocity fields were used to determine the pressure responses across the domain. A three-parameter regressive cross-correlation was used to calibrate the output pressure responses against the pressure input signal. The transform between the input-output pressure signals allowed detection of the region immediately downstream of the stenosis. In particular, if the cross correlative parameter that relates the instantaneous transfer across the input-output signals was greater than the delayed transfer parameter a stenosis is present. In contrast, the delayed transfer parameter was larger for the region upstream of the stenosis. This outcome is particularly valuable as it does not require calibration of the absolute pressure, which can be difficult to determine physiologically due to factors such as arterial geometry and intrathoracic pressure. However, the outcomes need to be validated in more geometries prior to clinical validation.

Original languageEnglish
Pages (from-to)143-149
Number of pages7
JournalBiomedical Signal Processing and Control
Early online date4 Oct 2016
Publication statusPublished - 1 Feb 2017


  • Autoregressive correlation modelling
  • Experimental fluids
  • Fluid dynamics
  • Hemodynamics
  • Particle image velocimetry


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