In vivo imaging and modeling of artery blood pressure wave and interaction with light in time domain

Abstract

Artery blood flow driven by pressure wave is essential for human life. The long-term research goal is to establish a foundation for future translation of the imaging photoplethysmography (iPPG) method into applications such as extraction of vital health biomarkers. For this dissertation research, we focus on development of a robust framework of hardware and data processing tools for rapid acquisition and analysis of in vivo iPPG data. Through this project, we have developed a multispectral imaging system for acquisition of time-sequenced iPPG data in vivo from hands of two volunteers with 12 wavelength bands ranging from 445 to 940 nm at a frame rate up to 250 Hz. A fluid dynamic model of blood flow and pressure wave was employed to quantify the effects of the artery wall deformation with different Young's modulus, artery radius and other boundary conditions on flow rate and pressure. A voxel-based Monte Carlo (MC) simulation model has been established for quantitative analysis of the iPPG data based on the radiative transfer theory. The measured waveforms of iPPG signals at multiple locations in palmar artery have been compared to the waveforms of blood pressure waves calculated with the coupled model of MC and the fluid dynamic simulations. We have identified the optimal wavelength bands for iPPG illumination to detect palm artery tree and shown that the spatial and temporal distributions of the iPPG signals have strong potentials to monitor heartbeat rate and conditions of artery. The results of this dissertation study provide convincing evidences that iPPG data contain rich information on blood flow and propagation of pressure wave in artery. Consequently, future development of the iPPG method can yield a powerful and non-contact tool to rapidly acquire vital sign data and quantitatively assess cardiovascular conditions.