The non-invasive measurement of blood oxygen saturation in blood vessels is a promising clinical application of optoacoustic imaging. Nevertheless, precise optoacoustic measurements of blood oxygen saturation are limited because of the complexities of calculating the spatial distribution of the optical fluence. In the paper error in the determination of blood oxygen saturation, associated with the use of approximate methods of optical fluence evaluation within the blood vessel, was investigated for optoacoustic measurements at two wavelengths. The method takes into account both acoustic pressure noise and the error in determined values of the optical scattering and absorption coefficients used for the calculation of the fluence. It is shown that, in conditions of an unknown (or partially known) spatial distribution of fluence at depths of 2 to 8 mm, minimal error in the determination of blood oxygen saturation is achieved at wavelengths of 658 ± 40 nm and 1069 ± 40 nm.

Modern optical imaging techniques demonstrate significant potential for high resolution in vivo angiography. Optoacoustic angiography benefits from higher imaging depth as compared to pure optical modalities. However, strong attenuation of optoacoustic signal with depth provides serious challenges for adequate 3D vessel net mapping, and proper compensation for fluence distribution within biotissues is required. We report on the novel approach allowing to estimate effective in-depth fluence profiles for optoacoustic systems. Calculations are based on Monte Carlo simulation of light transport and account for complex illumination geometry and acoustic detection parameters. The developed fluence compensation algorithm was tested in in vivo angiography of human palm and allowed to overcome significant in-depth attenuation of probing radiation and enhance the contrast of lower dermis plexus while preserving high resolution of upper plexus imaging.

We propose a hybrid approach to image enhancement in acoustic resolution photoacoustic microscopy. The developed technique is based on compensation for nonuniform spatial sensitivity of the optoacoustic (OA) system in both optical and acoustic domains. Spatial distribution of optical fluence is derived from full three-dimensional Monte Carlo simulations accounting for conical geometry of tissue laser illumination at the wavelength of 532 nm. Approximate nonuniform spatial response of acoustic detector with numerical aperture of 0.6 is derived from the two-dimensional k-Wave modeling. Application of the developed technique allows to improve the spatial resolution and to balance in-depth signal-level distribution in OA images of phantom and in-vivo objects.

No abstract

Chronic rhinitis (CR) is among the most frequent inflammatory diseases of ear-nose-throat (ENT) covering up to 30% of the population. Different forms of CR require different treatment tactics, which indicates the need for an efficient tool for differential diagnostics of CR. Optical coherence tomography (OCT) is a promising tool for fast non-invasive evaluation of nasal mucosa, which, however, requires further interpretation of the obtained diagnostic image. In this paper, we provide a comparative analysis of several machine learning approaches that aim at automated differential diagnostics of CR based on diagnostic OCT images of 78 patients aged between 28 and 74 ages. Gradient boosting decision trees (GBT) approach reveals the best classification accuracy (98% and 94% for binary and diagnostic classification, respectively). It shows that proposed approaches have potential for automated classification of CR OCT images.