Photoacoustic remote sensing elastography.

The mechanical properties of organisms are important indicators for clinical disputes and disease monitoring, yet most existing elastography techniques are based on contact measurements, which are limited in many application scenarios. Photoacoustic remote sensing elastography (PARSE) is the first, to the best of our knowledge, elastography modality based on acoustic pressure monitoring, where elastic contrast information is obtained by using an all-optical non-contact and non-coherent intensity monitoring method through the time-response properties of laser-induced photoacoustic pressure. To validate PARSE, sections of different elastic organs were measured and this modality was applied to differentiate between bronchial cartilage and soft tissue to confirm the validity of the elasticity evaluation. PARSE, through a mathematical derivation process, has a 9.5-times greater distinction detection capability than photoacoustic remote sensing (PARS) imaging in stained bronchial sections, expands the scope of conventional PARS imaging, and has potential to become an important complementary imaging modality.

Two-dimensional correlation (2D) method for improving the accuracy of OCT-based noninvasive blood glucose concentration (BGC) monitoring.

The optical scattering coefficient (µs) in the dermis layer of human skin obtained with optical coherence tomography (OCT) has shown to have a strong correlation with the blood glucose concentration (BGC), which can be used for noninvasive BGC monitoring. Unfortunately, the nonhomogeneity in the skin may cause inaccuracies for the BGC analysis. In this paper, we propose a 2D correlation analysis method to identify 2D regions in the skin with µs sensitive to BGC variations and only use data in these regions to calculate µs for minimizing the inaccuracy induced by nonhomogeneity and therefore improving the accuracy of OCT-based BGC monitoring. We demonstrate the effectiveness of the 2D method with OCT data obtained with in vivo human forearm skins of nine different human subjects. In particular, we present a 3D OCT data set in a two-dimensional (2D) map of depth vs. a lateral dimension and calculate the correlation coefficient R between the µs and the BGC in each region of the 2D map with the BGC data measured with a glucose meter using finger blood. We filter out the µs data from regions with low R values and only keep the µs data with R values sufficiently high (R-filter). The filtered µs data in all the regions are then averaged to produce an average µs data. We define a term called overall relevancy (OR) to quantify the degree of correlation between the filtered/averaged µs data and the finger-blood BGC data to determine the optimal R value for such an R-filter with the highest obtained OR. We found that the optimal R for such an R-filter has an absolute value (|R|) of 0.6 or 0.65. We further show that the R-filter obtained with the 2D correlation method yields better OR between µs and the BGC than that obtained with the previously reported 1D correlation method. We believe that the method demonstrated in this paper is important for understanding the influence of BGC on µs in human skins and therefore for improving the accuracy of OCT-based noninvasive BGC monitoring, although further studies are required to validate its effectiveness.