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The scattering and absorption of light within biological tissue severely limits the penetration depth of optical imaging techniques. Recently, it has been found that water-soluble, strongly absorbing dye molecules, such as tartrazine, can achievein vivotissue transparency by increasing the refractive index of aqueous components in tissue, as predicted by the Lorentz oscillator model and Kramers–Kronig relations. In this study, we topically applied absorbing dye molecules to the abdominal skin of pigmented and nonpigmented mice to enhance the penetration depth of optical coherence tomography (OCT) and photoacoustic microscopy (PAM). In both types of mice, the penetration depth of OCT was significantly improved using tartrazine and 4-aminoantipyrine. As predicted by the Kramers–Kronig relations and absorption spectra of the dyes, mice treated with 4-aminoantipyrine showed significantly improved penetration depth compared to mice treated with tartrazine for the PAM system with 532 nm excitation. These findings further demonstrate the use of absorbing dye molecules for achieving tissue transparency to enhance the penetration depth of depth-resolved optical imaging modalities in skin, thus accelerating the translation of these technologies in clinical areas, such as dermatology. 

We present a machine learning method for detecting and staging cervical dysplastic tissue using light scattering data based on a convolutional neural network (CNN) architecture. Depth-resolved angular scattering measurements from two clinical trials were used to generate independent training and validation sets as input of our model. We report 90.3% sensitivity, 85.7% specificity, and 87.5% accuracy in classifying cervical dysplasia, showing the uniformity of classification of a/LCI scans across different instruments. Further, our deep learning approach significantly improved processing speeds over the traditional Mie theory inverse light scattering analysis (ILSA) method, with a hundredfold reduction in processing time, offering a promising approach for a/LCI in the clinic for assessing cervical dysplasia. 

For many clinical applications, such as dermatology, optical coherence tomography (OCT) suffers from limited penetration depth due primarily to the highly scattering nature of biological tissues. Here, we present a novel implementation of dual-axis optical coherence tomography (DA-OCT) that offers improved depth penetration in skin imaging at 1.3 µm compared to conventional OCT. Several unique aspects of DA-OCT are examined here, including the requirements for scattering properties to realize the improvement and the limited depth of focus (DOF) inherent to the technique. To overcome this limitation, our approach uses a tunable lens to coordinate focal plane selection with image acquisition to create an enhanced DOF for DA-OCT. This improvement in penetration depth is quantified experimentally against conventional on-axis OCT using tissue phantoms and mouse skin. The results presented here suggest the potential use of DA-OCT in situations where a high degree of scattering limits depth penetration in OCT imaging.