Hyperspectral Imaging and Multivariate Curve Resolution
Researchers at Sandia have designed and constructed a hyperspectral confocal fluorescence microscope. Hyperspectral microscopes image hundreds of spectral wavelengths when obtaining spectral images. Included in the hyperspectral imaging system are software programs for controlling the microscope and its data collection, as well as spectral image viewing software for viewing both the raw image data and the spectral and image results from the multivariate curve resolution (MCR) analyses.
The hyperspectral microscope allows for rapid detection of all emitting fluorescence species in an image and determines their relative concentrations throughout the image without any prior information. The microscope is combined with Sandia’s unique and proprietary multivariate algorithms and software to form a complete system for the extraction of quantitative image information from the hyperspectral images at diffraction-limited spatial resolutions. Sandia’s MCR software employs new algorithmic approaches to accomplish dramatically faster computation of the rigorous, constrained alternating least-squares MCR analysis. This microscope system can reveal new fluorescent species that may not have been known to exist. It also allows an expansion of the structural stains and molecular fluorophores that biologists can introduce into biological samples simultaneously. This microscope and analysis system can accurately multiplex and recover the individual composition maps of each fluorophore, even if they are highly overlapped spectrally and/or spatially.
- Accurate detection and quantification of unexpected or unknown fluorescence species in imaged samples
- Changes in the pure emission spectra can be used to indirectly monitor the local environment of the fluorescent molecules in biological sample
- Hyperspectral imaging in a purely discovery mode for all those samples where the set of emission components is either not known or where the emission component spectra are dependent on the local environment of the sample
- Relative concentration maps of each of the emission components in sample can be obtained without fear of spectral cross talk from overlapping spectral components
- Imaging of any sample that can be placed under the microscope objective and that has fluorescence species that can be excited by the laser, including biological samples such as living plant, animal, and bacterial cells; thin animal and plant tissue samples; and biofilm on water purification membranes
- Live monitoring of the synthesis of quantum dots in microfluidic platforms to better understand the kinetic reaction mechanisms and rate constants involve in their synthesis
- Monitoring of cell viability or the metabolic state of the cell to obtain unprecedented image contrast
- Gene expression microarrays for studying genetic markers for leukemia and treatment outcomes
- Material durability diagnostics (e.g., aging characteristics and viability materials used in airplanes and nuclear reactors)
- Michael B Sinclair, David M. Haaland, Jerilyn A. Timlin, and Howland D.T. Jones. (2006).”Hyperspectral Confocal Microscope.” Applied Optics. 45(24), pp 6283-6291. [online]. Available: http://www.opticsinfobase.org/view_article.cfm?gotourl=http%3A%2F%2Fwww%2Eopticsinfobase%2Eorg%2FDirectPDFAccess%2FCF7746EC%2DAA88%2D18FA%2D8DD45DEA81003270%5F96177%2Epdf%3Fda%3D1%26id%3D96177%26seq%3D0%26mobile%3Dno&org=Sandia%20National%20Labs%20Albuquerque%20Technical%20Lib
- Willem F. J. Vermaas, Jerilyn A. Timlin, Howland D. T. Jones, Michael B. Sinclair, Linda T. Nieman, Sawsan Hamad, David K. Melgaard, and David M. Haaland. (2008, March). “In vivo Hyperspectral Confocal Fluorescence Imaging to Determine Pigment Localization and Distribution in Cyanobacterial Cells.” Proceedings of the National Academies of Sciences. 105 (10), pp 4050-4055. [online]. Available: http://www.pnas.org/content/105/10/4050.full.pdf+html
- P. G. Kotula, M. R. Keenan, and J. R. Michael. (2003). “Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool.” Microsc. Microanal. 9, pp 1-17. [online]. Available: http://www.geology.wisc.edu/~johnf/g777/MM/Kotula-2003.pdf
- Mark H. Van Benthem, Michael R. Keenan, and David M. Haaland. (2002). “Application of Equality Constraints on Variables during Alternating Least Squares Procedures.” Journal of Chemometrics. 16, pp 613-622. [online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cem.761/pdf
- M. H. Van Benthem and M. R. Keenan. (2004). “Fast algorithm for the solution of large-scale non-negativity-constrained least squares problems.” Journal of Chemometrics. 18, pp 441-450. [online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cem.889/pdf
| Patent Title | Patent Number | Grant Date |
|---|---|---|
| Methods for spectral image analysis by exploiting spatial simplicity | 7,840,626 | 11/23/2010 |
| Method to analyze remotely sensed spectral data | 7,491,944 | 02/17/2009 |
| Fast combinatorial algorithm for the solution of linearly constrained least squares problems | 7,451,173 | 11/11/2008 |
| Spectral compression algorithms for the analysis of very large multivariate images | 7,283,684 | 10/16/2007 |
| Augmented classical least squares multivariate spectral analysis | 6,922,645 | 07/26/2005 |
| Augmented classical least squares multivariate spectral analysis | 6,842,702 | 01/11/2005 |
| Augmented classical least squares multivariate spectral analysis | 6,687,620 | 02/03/2004 |
SD#6728
Published11/14/2011
Last Updated2/6/2013