Quantifying Carotid Pulse Waveforms Using Subpixel Image Registration
Cardiovascular diseases are a common cause of death. Symptoms of cardiovascular disease often arise at a stage of the disease where treatments are ineffective. Hence, methods that can help early diagnosis of heart problems are essential for preventing heart failure. Assessing the shape of the carotid artery waveforms is one of the methods that clinicians use to diagnose heart and valvular diseases, such as hypertrophic obstructive cardiomyopathy, aortic stenosis, and aortic regurgitation. The carotid artery waveforms may be estimated using pulsed-Doppler ultrasound devices or quantified using catheterisation. However, both of these solutions have limitations. Currently, among available solutions, there is no inexpensive, non-invasive objective method, or diagnostic tool for estimating or quantifying the carotid waveforms. To address these limitations, we have designed a portable non-contact camera-based device to quantify the carotid arterial waveforms. The proposed device calculates the vessel-induced deformation of skin from videos taken from the neck to estimate the carotid artery pressure waveforms. This device takes advantage of our precise and sensitive subpixel image registration algorithm to measure skin deformations from sequential frames of the videos. The skin deformations obtained using our device were compared against a laser displacement measurement device with a resolution of 0.2 μm, and a correlation score of 0.95 was achieved for five subjects.
The carotid artery waveforms measured using this device can provide beneficial information for early detection of heart disease. Furthermore, the data gathered using this device can be used to develop computational models of the carotid artery and/or the cardiac systolic and diastolic phases.
KeywordsCarotid artery Pressure Deformation Subpixel image registration
- 2.Bickley LS, Szilagyi PG, Hoffman RM (2017) Bates’ guide to physical examination and history-taking. Wolters Kluwer, PhiladelphiaGoogle Scholar
- 6.Kasper D, Fauci A, Hauser S, Longo D, Jameson JL, Loscalzo J (2015) Harrison’s principles of internal medicine. McGraw Hill, New YorkGoogle Scholar
- 7.Maron BJ (2002) Hypertrophic cardiomyopathy: a systematic review. JAMA 287:1308–1320Google Scholar
- 8.Cheng H-M, Chuang S-Y, Wang J-J, Shih Y-T, Wang H-N, Huang C-J, Huang J-T, Sung S-H, Lakatta EG, Yin FCP, Chou P, Yeh C-J, Bai C-H, Pan W-H, Chen C-H (2016) Prognostic significance of mechanical biomarkers derived from pulse wave analysis for predicting long-term cardiovascular mortality in two population-based cohorts. Int J Cardiol 215:388–395CrossRefGoogle Scholar
- 16.HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PM (2017) Subpixel phase-based image registration using Savitzky–Golay differentiators in gradient-correlation. Comput Vis Image Underst. https://doi.org/10.1016/j.cviu.2017.11.003
- 17.HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PMF (2016) Subpixel measurement of living skin deformation using intrinsic features. In: Proceedings of computational biomechanics of medicine XI workshop, MICCAIGoogle Scholar