Characterization of bone microstructure using photoacoustic spectrum analysis

Abstract

Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and the deterioration in bone microarchitecture. This study investigates the feasibility of characterizing bone microstructure by analyzing the frequency spectrum of the photoacoustic (PA) signal from the bone. Modeling and numerical simulation of PA signal were performed on trabecular bone simulations and CT scans with different trabecular thicknesses. The resulting quasi-linear photoacoustic spectra were fittted by linear regression, from which the spectral parameter slope was quantified. The simulation based on two different models both demonstrate that bone specimens with thinner trabecular thicknesses have higher slope. Experiment on osteoporotic rat femoral heads with different mineral content was conducted. The finding from the experiment was in good agreement with the simulation, demonstrating that the frequency-domain analysis of PA signals can provide an objective assessment of bone microstructure and deterioration. Considering that PA measurement is non-ionizing, non-invasive, and has sufficient penetration in both calcified and non-calcified tissues, this new bone evaluation method based on photoacoustic spectral analysis holds potential for clinical management of osteoporosis and other bone diseases.

Assessing the development and treatment of rheumatoid arthritis using multiparametric photoacoustic and ultrasound imaging

“…Third, during the progress of arthritis, the intact cartilage surfaces are corroded by the inflammatory, which will cause the change on the bone structure. It is preferred to assess the bone microstructures using spectrum analysis of photoacoustic signal . In addition, the blood viscosity is one of the most valuable mechanical information of microcirculation status.…”

Photoacoustic simulations of microvascular bleeding: spectral analysis and its application for monitoring vascular-targeted treatments

“…However, microvessels deep in tissue cannot be resolved using acoustic-resolution PA imaging. Ultrasound frequency analysis of the PA signals has been shown to detect changes smaller than the system resolution, [11][12][13] and this analysis may permit assessment of microvessel bleeding. The ability to detect and quantify the occurrence of bleeding can have an important role for PA modeling in cancer therapy.…”

Adipocyte Size Evaluation Based on Photoacoustic Spectral Analysis Combined with Deep Learning Method

“…Recently, various methods for analyzing the PA signals in the frequency domain have been developed by several research groups [9][10][11][12], aiming to achieve quantitative evaluation of tissue microstructures. One of the methods termed as photoacoustic spectral analysis (PASA) [13][14][15][16], borrowed the basic concept from ultrasound spectral analysis [17][18][19]. In PASA, by quantifying the key parameters, including slope, intercept, and mid-band fit, of the linear fit to the truncated signal power spectrum within the predetermined frequency range, histological microfeatures of the optically absorbing materials in target tissues were characterized.…”