Optical coherence tomography (OCT) acquires cross-sectional images of tissue by measuring back-reflected light. Images from in vivo OCT systems typically have a resolution of 10 to 15 mm, and are thus best suited for visualizing structures in the range of tens to hundreds of microns, such as tissue layers or glands. Many normal and abnormal tissues lack visible structures in this size range, so it may appear that OCT is unsuitable for identification of these tissues. However, examination of structure-poor OCT images reveals that they frequently display a characteristic texture that is due to speckle. We evaluated the application of statistical and spectral texture analysis techniques for differentiating tissue types based on the structural and speckle content in OCT images. Excellent correct classification rates were obtained when images had slight visual differences (mouse skin and fat, correct classification rates of 98.5 and 97.3%, respectively), and reasonable rates were obtained with nearly identical-appearing images (normal versus abnormal mouse lung, correct classification rates of 64.0 and 88.6%, respectively). This study shows that texture analysis of OCT images may be capable of differentiating tissue types without reliance on visible structures.

Abstract. Hyperspectral imaging has tremendous potential to detect important molecular biomarkers of early cancer based on their unique spectral signatures. Several drawbacks have limited its use for in vivo screening applications: most notably the poor temporal and spatial resolution, high expense, and low optical throughput of existing hyperspectral imagers. We present the development of a new real-time hyperspectral endoscope (called the image mapping spectroscopy endoscope) based on an image mapping technique capable of addressing these challenges. The parallel high throughput nature of this technique enables the device to operate at frame rates of 5.2 frames per second while collecting a (x, y, λ) datacube of 350 × 350 × 48. We have successfully imaged tissue in vivo, resolving a vasculature pattern of the lower lip while simultaneously detecting oxy-hemoglobin.C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

A high birefringence of over 0.21 for the yttrium vanadate (YVO4) crystal in the middle wavelength infrared (i.e., 3-5 microm) was measured. A Fourier transform infrared spectrometer was employed in the channel spectra technique to obtain the measurements.

A snapshot Image Mapping Spectrometer (IMS) with high sampling density is developed for hyperspectral microscopy, measuring a datacube of dimensions 285 × 285 × 60 (x, y, λ). The spatial resolution is ~0.45 µm with a FOV of 100 × 100 µm2. The measured spectrum is from 450 nm to 650 nm and is sampled by 60 spectral channels with average sampling interval ~3.3 nm. The channel’s spectral resolution is ~8nm. The spectral imaging results demonstrate the potential of the IMS for real-time cellular fluorescence imaging.

An image slicing spectrometer (ISS) for microscopy applications is presented. Its principle is based on the redirecting of image zones by specially organized thin mirrors within a custom fabricated component termed an image slicer. The demonstrated prototype can simultaneously acquire a 140nm spectral range within its 2D field of view from a single image. The spectral resolution of the system is 5.6nm. The FOV and spatial resolution of the ISS depend on the selected microscope objective and for the results presented is 45×45μm 2 and 0.45μm respectively. This proof-of-concept system can be easily improved in the future for higher (both spectral and spatial) resolution imaging. The system requires no scanning and minimal post data processing. In addition, the reflective nature of the image slicer and use of prisms for spectral dispersion make the system light efficient. Both of the above features are highly valuable for real time fluorescent-spectral imaging in biological and diagnostic applications.

We present a snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) for eye imaging applications. The resulting system is capable of simultaneously acquiring 48 spectral channel images in the range 470 nm–650 nm with frame rate at 5.2 fps. The spatial sampling of each measured spectral scene is 350 × 350 pixels. The advantages of this snapshot device are elimination of the eye motion artifacts and pixel misregistration problems in traditional scanning-based hyperspectral retinal cameras, and real-time imaging of oxygen saturation dynamics with sub-second temporal resolution. The spectral imaging performance is demonstrated in a human retinal imaging experiment in vivo. The absorption spectral signatures of oxy-hemoglobin and macular pigments were successfully acquired by using this device.

Microendoscopes allow clinicians to view subcellular features in vivo and in real-time, but their field-of-view is inherently limited by the small size of the probe’s distal end. Video mosaicing has emerged as an effective technique to increase the acquired image size. Current implementations are performed post-procedure, which removes the benefits of live imaging. In this manuscript we present an algorithm for real-time video mosaicing using a low-cost high-resolution microendoscope. We present algorithm execution times and show image results obtained from in vivo tissue.

Image mapping spectrometry (IMS) is a hyperspectral imaging technique that simultaneously captures spatial and spectral information about an object in real-time. We present a new calibration procedure for the IMS as well as the first detailed evaluation of system performance. We correlate optical components and device calibration to performance metrics such as light throughput, scattered light, distortion, spectral image coregistration, and spatial/spectral resolution. Spectral sensitivity and motion artifacts are also evaluated with a dynamic biological experiment. The presented methodology of evaluation is useful in assessment of a variety of hyperspectral and multi-spectral modalities. Results are important to any potential users/developers of an IMS instrument and to anyone who may wish to compare the IMS to other imaging spectrometers.