The exquisite sensitivity and frequency discrimination of mammalian hearing underlie the ability to understand complex speech in noise. This requires force generation by cochlear outer hair cells (OHCs) to amplify the basilar membrane traveling wave; however, it is unclear how amplification is achieved with sharp frequency tuning. Here we investigated the origin of tuning by measuring sound-induced 2-D vibrations within the mouse organ of Corti in vivo. Our goal was to determine the transfer function relating the radial shear between the structures that deflect the OHC bundle, the tectorial membrane and reticular lamina, to the transverse motion of the basilar membrane. We found that, after normalizing their responses to the vibration of the basilar membrane, the radial vibrations of the tectorial membrane and reticular lamina were tuned. The radial tuning peaked at a higher frequency than transverse basilar membrane tuning in the passive, postmortem condition. The radial tuning was similar in dead mice, indicating that this reflected passive, not active, mechanics. These findings were exaggerated in Tecta^{C1509G/C1509G} mice, where the tectorial membrane is detached from OHC stereocilia, arguing that the tuning of radial vibrations within the hair cell epithelium is distinct from tectorial membrane tuning. Together, these results reveal a passive, frequency-dependent contribution to cochlear filtering that is independent of basilar membrane filtering. These data argue that passive mechanics within the organ of Corti sharpen frequency selectivity by defining which OHCs enhance the vibration of the basilar membrane, thereby tuning the gain of cochlear amplification. SIGNIFICANCE STATEMENT Outer hair cells amplify the traveling wave within the mammalian cochlea. The resultant gain and frequency sharpening are necessary for speech discrimination, particularly in the presence of background noise. Here we measured the 2-D motion of the organ of Corti in mice and found that the structures that stimulate the outer hair cell stereocilia, the tectorial membrane and reticular lamina, were sharply tuned in the radial direction. Radial tuning was similar in dead mice and in mice lacking a tectorial membrane. This suggests that radial tuning comes from passive mechanics within the hair cell epithelium, and that these mechanics, at least in part, may tune the gain of cochlear amplification.

Optical coherence tomography (OCT) is an established technique that provides noninvasive, volumetric, and real-time in vivo images of tissue microstructure.¹ One longstanding criticism of traditional OCT images (i.e., B-scans), however, is that they only provide morphological information about the tissue.^2,3 Quantitative analysis of tissue properties would facilitate more accurate differentiation between diseased tissue and normal tissue. Several researchers have proposed that the optical attenuation coefficient (AC), which measures how quickly incident light is attenuated when passing through a medium, may enable such analysis. Indeed, analysis of the AC has been used for quantitative assessment and differentiation of several tissue types:⁴ for example, quantification of the AC from OCT data enables in vivo diagnostic applications, such as imaging of atherosclerotic plaques,³ assessment of glaucoma,⁵ identification of axillary lymph nodes,⁶ differentiation between normal and cancerous tissue in the bladder⁷ and colon,⁸ and imaging of the cerebral cortex after stroke.⁹ In addition, measurement of the AC also serves to assess vascularity in human burn scars¹⁰ and development of acne scars,¹¹ as well as to monitor the effect of photodynamic therapy in skin lesions.¹²

We present a method for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) data. In contrast to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and the attenuation coefficient while considering the confocal function and sensitivity fall-off, which are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We also show that further improvement of the estimated attenuation coefficient is possible by formulating image denoising as a convex optimization problem that we term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated data, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a novel way to improve OCT contrast for better tissue characterization through quantitative imaging.

We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95.4% sensitivity and specificity, validated by leave-one-patient-out cross validation (LOPOCV), in detecting BCC in human skin samples collected from 42 patients. Moreover, we show the superiority of our classifier over the best possible classifier based on features extracted from intensity-only data, which demonstrates the significance of PR data in detecting BCC.

White light endoscopy is widely used for diagnostic imaging of the interior of organs and body cavities, but the inability to correlate individual 2D images with 3D organ morphology limits its utility for quantitative or longitudinal studies of disease physiology or cancer surveillance. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a computational method to reconstruct and visualize a 3D model of organs from an endoscopic video that captures the shape and surface appearance of the organ. A key aspect of our strategy is the use of advanced computer vision techniques and unmodified, clinical-grade endoscopy hardware with few constraints on the image acquisition protocol, which presents a low barrier to clinical translation. We validate the accuracy and robustness of our reconstruction and co-registration method using cystoscopy videos from tissue-mimicking bladder phantoms and show clinical utility during cystoscopy in the operating room for bladder cancer evaluation. As our method can powerfully augment the visual medical record of the appearance of internal organs, it is broadly applicable to endoscopy and represents a significant advance in cancer surveillance opportunities for big-data cancer research.

Rapid advancements in smartphone technology have enabled the integration of many optical detection techniques that leverage the embedded functional components and software platform of these sophisticated devices. Over the past few years, several research groups have developed high-resolution smartphone-based optical spectroscopic platforms and demonstrated their usability in different biomedical applications. Such platforms provide unprecedented opportunity to develop point-of-care diagnostics systems, especially for resource-constrained environments. In this review, we discuss the development of smartphone systems for optical spectroscopy and highlight current challenges and potential solutions to improve the scope for their future adaptability.