Clinical monitoring of blood oxygen saturation (sO2) is traditionally performed using optical techniques, such as pulse oximetry and diffuse reflectance spectroscopy (DRS), which lack spatial resolution. Photoacoustic imaging (PAI) is a rapidly developing biomedical imaging technique that is superior to previous techniques in that it combines optical excitation and acoustic detection, providing a map of chromophore distribution in the tissue. Hitherto, PAI has primarily been used in preclinical studies, and only a few studies have been performed in patients. Its ability to measure sO2 with spatial resolution during local vasoconstriction after adrenaline injection has not yet been investigated. Using PAI and spectral unmixing we characterize the heterogeneous change in sO2 after injecting a local anesthetic containing adrenaline into the dermis on the forearm of seven healthy subjects. In comparison to results obtained using DRS, we highlight contrasting results obtained between the two methods arising due to the so-called ‘window effect’ caused by a reduced blood flow in the superficial vascular plexus. The results demonstrate the importance of spatially resolving sO2 and the ability of PAI to assess the tissue composition in different layers of the skin.
Parabolic sub-sample interpolation for 2D block-matching motion estimation is computationally efficient. However, it is well known that the parabolic interpolation gives a biased motion estimate for displacements greater than |y.2| samples (y = 0, 1, …). Grid slope sub-sample interpolation is less biased, but it shows large variability for displacements close to y.0. We therefore propose to combine these sub-sample methods into one method (GS15PI) using a threshold to determine when to use which method. The proposed method was evaluated on simulated, phantom, and in vivo ultrasound cine loops and was compared to three sub-sample interpolation methods. On average, GS15PI reduced the absolute sub-sample estimation errors in the simulated and phantom cine loops by 14, 8, and 24% compared to sub-sample interpolation of the image, parabolic sub-sample interpolation, and grid slope sub-sample interpolation, respectively. The limited in vivo evaluation of estimations of the longitudinal movement of the common carotid artery using parabolic and grid slope sub-sample interpolation and GS15PI resulted in coefficient of variation (CV) values of 6.9, 7.5, and 6.8%, respectively. The proposed method is computationally efficient and has low bias and variance. The method is another step toward a fast and reliable method for clinical investigations of longitudinal movement of the arterial wall.
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