Hyperspectral (also known as “multispectral”) spectroscopy is an imaging technique that integrates multiple images of an object resolved at different spectral bands (i.e., ranges of wavelengths) into a single data structure, referred to as a three-dimensional hyperspectral data cube. Data provided by hyperspectral spectroscopy is often used to identify a number of individual components of a complex composition through the recognition of spectral signatures of the individual components of a particular hyperspectral data cube.
Hyperspectral spectroscopy has been used in a variety of applications, ranging from geological and agricultural surveying to military surveillance and industrial evaluation. Hyperspectral spectroscopy has also been used in medical applications to facilitate complex diagnosis and predict treatment outcomes. For example, medical hyperspectral imaging has been used to accurately predict viability and survival of tissue deprived of adequate perfusion, and to differentiate diseased (e.g. tumor) and ischemic tissue from normal tissue.
Hyperspectral/multispectral spectroscopy has also been used in medical applications to assist with complex diagnosis and predict treatment outcomes. For example, medical hyperspectral/multispectral imaging has been used to accurately predict viability and survival of tissue deprived of adequate perfusion, and to differentiate diseased (e.g. tumor) and ischemic tissue from normal tissue. (See, Colarusso P. et al., Appl Spectrosc 1998; 52:106A-120A; Greenman R. I. et al., Lancet 2005; 366:1711-1718; and Zuzak K. J. et al., Circulation 2001; 104(24):2905-10; the contents of which are hereby incorporated herein by reference in their entireties for all purposes.)
However, despite the great potential clinical value of hyperspectral imaging, several drawbacks have limited the use of hyperspectral imaging in the clinic setting. In particular, current medical hyperspectral instruments are costly because of the complex optics and computational requirements currently used to resolve images at a plurality of spectral bands to generate a suitable hyperspectral data cube. Hyperspectral imaging instruments can also suffer from poor temporal and spatial resolution, as well as low optical throughput, due to the complex optics and taxing computational requirements needed for assembling, processing, and analyzing data into a hyperspectral data cube suitable for medical use.
Thus, there is an unmet need in the field for less expensive and more rapid means of hyperspectral/multispectral imaging and data analysis. The present disclosure meets these and other needs by providing methods and systems for co-axial hyperspectral/multispectral imaging.