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Kainerstorfer JM, Riley JD, Ehler M, et al. Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors. Biomed Opt Express. 2011;2(5):1040-58doi: 10.1364/BOE.2.001040.
Kainerstorfer, J. M., Riley, J. D., Ehler, M., Najafizadeh, L., Amyot, F., Hassan, M., Pursley, R., Demos, S. G., Chernomordik, V., Pircher, M., Smith, P. D., Hitzenberger, C. K., & Gandjbakhche, A. H. (2011). Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors. Biomedical optics express, 2(5), 1040-58. https://doi.org/10.1364/BOE.2.001040
Kainerstorfer, Jana M, et al. "Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors." Biomedical optics express vol. 2,5 (2011): 1040-58. doi: https://doi.org/10.1364/BOE.2.001040
Kainerstorfer JM, Riley JD, Ehler M, Najafizadeh L, Amyot F, Hassan M, Pursley R, Demos SG, Chernomordik V, Pircher M, Smith PD, Hitzenberger CK, Gandjbakhche AH. Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors. Biomed Opt Express. 2011 Apr 01;2(5):1040-58. doi: 10.1364/BOE.2.001040. PMID: 21559118; PMCID: PMC3087563.
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Biomed Opt Express. 2011 Apr 01;2(5):1040-58. doi: 10.1364/BOE.2.001040.

Quantitative principal component model for skin chromophore mapping using multi-spectral images and spatial priors.

Biomedical optics express

Jana M Kainerstorfer, Jason D Riley, Martin Ehler, Laleh Najafizadeh, Franck Amyot, Moinuddin Hassan, Randall Pursley, Stavros G Demos, Victor Chernomordik, Michael Pircher, Paul D Smith, Christoph K Hitzenberger, Amir H Gandjbakhche

PMID: 21559118 PMCID: PMC3087563 DOI: 10.1364/BOE.2.001040

Abstract

We describe a novel reconstruction algorithm based on Principal Component Analysis (PCA) applied to multi-spectral imaging data. Using numerical phantoms, based on a two layered skin model developed previously, we found analytical expressions, which convert qualitative PCA results into quantitative blood volume and oxygenation values, assuming the epidermal thickness to be known. We also evaluate the limits of accuracy of this method when the value of the epidermal thickness is not known. We show that blood volume can reliably be extracted (less than 6% error) even if the assumed thickness deviates 0.04mm from the actual value, whereas the error in blood oxygenation can be as large as 25% for the same deviation in thickness. This PCA based reconstruction was found to extract blood volume and blood oxygenation with less than 8% error, if the underlying structure is known. We then apply the method to in vivo multi-spectral images from a healthy volunteer's lower forearm, complemented by images of the same area using Optical Coherence Tomography (OCT) for measuring the epidermal thickness. Reconstruction of the imaging results using a two layered analytical skin model was compared to PCA based reconstruction results. A point wise correlation was found, showing the proof of principle of using PCA based reconstruction for blood volume and oxygenation extraction.

Keywords: (100.3010) Image reconstruction techniques; (170.6510) Spectroscopy, tissue diagnostics

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