The biomechanical properties of the human skin are intrinsically correlated with changes associated with pathological conditions, aging, and hydration. Quantitative measurements can improve diagnostic tools, treatments, and cosmetic product evaluation. Using optical coherence elastography (OCE), an emerging imaging modality combining optical coherence tomography (OCT) with a localized excitation source to induce mechanical disturbances, a quantitative evaluation of tissue biomechanics can be achieved. OCE complements the structural information with elasticity data to attain a complete overview of skin status.
In this study, we employed a home-built OCE system, combining a swept-source OCT system with a piezoelectric actuator for tissue displacement, to evaluate changes to the skin biomechanical properties due to the application of an anti-aging cream. Skin elasticity was monitored for a total of five weeks. Anti-aging cream was applied daily for four weeks. OCE measurements continued for one additional week to assess the effect of cream application interruption. Three female volunteers were included in this proof-of-principle investigation. Their counter-arm was used as control. Although no statistical significance was reached, a decrease in skin Young’s modulus was observed with the cream application, indicating an increase in skin elasticity.
We reconstruct the three-dimensional shape and location of the retinal vascular network from commercial spectral-domain (SD) optical coherence tomography (OCT) data. The two-dimensional location of retinal vascular network on the eye fundus is obtained through support vector machines classification of properly defined fundus images from OCT data, taking advantage of the fact that on standard SD-OCT, the incident light beam is absorbed by hemoglobin, creating a shadow on the OCT signal below each perfused vessel. The depth-wise location of the vessel is obtained as the beginning of the shadow. The classification of crossovers and bifurcations within the vascular network is also addressed. We illustrate the feasibility of the method in terms of vessel caliber estimation and the accuracy of bifurcations and crossovers classification.
The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.
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