Dispersion multiplexing with broadband filtering for miniature spectrometers

Real-time in vivo cancer diagnosis using Raman spectroscopy

Raman spectroscopy has becoming a practical tool for rapid in vivo tissue diagnosis. This paper provides an overview on the latest development of real-time in vivo Raman systems for cancer detection. Instrumentation, data handling, as well as oncology applications of Raman techniques were covered. Optic fiber probes designs for Raman spectroscopy were discussed. Spectral data pre-processing, feature extraction, and classification between normal/benign and malignant tissues were surveyed. Applications of Raman techniques for clinical diagnosis for different types of cancers, including skin cancer, lung cancer, stomach cancer, oesophageal cancer, colorectal cancer, cervical cancer, and breast cancer, were summarized. Schematic of a real-time Raman spectrometer for skin cancer detection. Without correction, the image captured on CCD camera for a straight entrance slit has a curvature. By arranging the optic fiber array in reverse orientation, the curvature could be effectively corrected.

Very-high-resolution tandem Fabry-Perot etalon cylindrical beam volume hologram spectrometer for diffuse source spectroscopy

We demonstrate a compact and slitless spectrometer with high resolution formed by cascading a Fabry-Perot etalon (FPE) and a cylindrical beam volume hologram (CBVH). The most significant advantage of this combined spectrometer is that we can independently encode spectral information of a diffuse beam in a 2D plane. Also, we show that in this slitless configuration we can simultaneously benefit from the advantages of both elements: the high resolution of the FPE and the large spectral range of the CBVH. Here, we report on the experimental demonstration of a spectrometer with better than 0.2 nm resolution.

Single-shot compressive spectral imaging with a dual-disperser architecture

This paper describes a single-shot spectral imaging approach based on the concept of compressive sensing. The primary features of the system design are two dispersive elements, arranged in opposition and surrounding a binary-valued aperture code. In contrast to thin-film approaches to spectral filtering, this structure results in easily-controllable, spatially-varying, spectral filter functions with narrow features. Measurement of the input scene through these filters is equivalent to projective measurement in the spectral domain, and hence can be treated with the compressive sensing frameworks recently developed by a number of groups. We present a reconstruction framework and demonstrate its application to experimental data.