OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 8 — Aug. 1, 2011
  • pp: 2110–2116
« Show journal navigation

Chemoselective imaging of mouse brain tissue via multiplex CARS microscopy

Christoph Pohling, Tiago Buckup, Axel Pagenstecher, and Marcus Motzkus  »View Author Affiliations


Biomedical Optics Express, Vol. 2, Issue 8, pp. 2110-2116 (2011)
http://dx.doi.org/10.1364/BOE.2.002110


View Full Text Article

Acrobat PDF (1094 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The fast and reliable characterization of pathological tissue is a debated topic in the application of vibrational spectroscopy in medicine. In the present work we apply multiplex coherent anti-Stokes Raman scattering (MCARS) to the investigation of fresh mouse brain tissue. The combination of imaginary part extraction followed by principal component analysis led to color contrast between grey and white matter as well as layers of granule and Purkinje cells. Additional quantitative information was obtained by using a decomposition algorithm. The results perfectly agree with HE stained references slides prepared separately making multiplex CARS an ideal approach for chemoselective imaging.

© 2011 OSA

1. Introduction

Coherent anti-Stokes Raman scattering (CARS) is a widespread technique in nonlinear microscopy of biological structures [1

1. A. Zumbusch, G. R. Holtom, and X. S. Xie, “Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering,” Phys. Rev. Lett. 82(20), 4142–4145 (1999). [CrossRef]

,2

2. A. Volkmer, J. X. Cheng, L. D. Book, and X. S. Xie, “New advances in Coherent anti-Stokes Raman scattering (CARS) microscopy and spectroscopy of biological systems,” Biophys. J. 80, 164a (2001).

]. CARS has been applied in a large field of medical applications ranging from the identification of microbial cells, high resolution imaging of human aorta-tissue, time resolved observations of HELA cells, up to real time monitoring of blood oxygenation level [3

3. M. Harz, P. Rösch, and J. Popp, “Vibrational spectroscopy--a powerful tool for the rapid identification of microbial cells at the single-cell level,” Cytometry A 75A(2), 104–113 (2009). [CrossRef] [PubMed]

7

7. H. A. Rinia, M. Bonn, E. M. Vartiainen, C. B. Schaffer, and M. Müller, “Spectroscopic analysis of the oxygenation state of hemoglobin using coherent anti-Stokes Raman scattering,” J. Biomed. Opt. 11(5), 050502 (2006). [CrossRef] [PubMed]

]. In spite of its success, CARS has been used only rarely for the identification of pathologic tissue alterations [8

8. C. L. Evans, X. Y. Xu, S. Kesari, X. S. Xie, S. T. C. Wong, and G. S. Young, “Chemically-selective imaging of brain structures with CARS microscopy,” Opt. Express 15(19), 12076–12087 (2007). [CrossRef] [PubMed]

10

10. C. Krafft, B. Dietzek, and J. Popp, “Raman and CARS microspectroscopy of cells and tissues,” Analyst (Lond.) 134(6), 1046–1057 (2009). [CrossRef] [PubMed]

]. This contrasts to linear techniques like Raman and infrared spectroscopy, which were already successfully correlated with histological characterization and classification of cancer types in human brain and lymph tissue [11

11. C. Krafft, L. Shapoval, S. B. Sobottka, K. D. Geiger, G. Schackert, and R. Salzer, “Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images,” Biochim. Biophys. Acta 1758(7), 883–891 (2006). [CrossRef] [PubMed]

13

13. B. Bird, M. Miljković, N. Laver, and M. Diem, “Spectral detection of micro-metastases and individual metastatic cells in lymph node histology,” Technol. Cancer Res. Treat. 10(2), 135–144 (2011). [PubMed]

].

The main challenge of the use of CARS in such applications is mainly due to two reasons. First, as a coherent process, CARS spectra show interference effects between the Raman resonances and the so called nonresonant background that introduces dispersive line profiles finally responsible for the distortions of line shape. Such distortions of spectral lines lead to loss of spectral information, especially for weak Raman modes in the presence of strong resonances and noise. Phase retrieval algorithms were already discussed to circumvent such problems [14

14. E. M. Vartiainen, “Phase Retrieval Approach for Coherent Anti-Stokes-Raman Scattering Spectrum Analysis,” J. Opt. Soc. Am. B 9(8), 1209–1214 (1992). [CrossRef]

16

16. Y. X. Liu, Y. J. Lee, and M. T. Cicerone, “Fast extraction of resonant vibrational response from CARS spectra with arbitrary nonresonant background,” J. Raman Spectrosc. 40(7), 726–731 (2009). [CrossRef]

], but have not been applied regularly. The second issue is the spectral resolution, which is often limited by ultrashort laser pulses necessary to stimulate the nonlinear effects. CARS studies of brain tissue discussed so far circumvented the disadvantage of low spectral resolution by using a synchronized pair of ps-laser sources with a frequency difference matching a single vibrational mode of the sample [8

8. C. L. Evans, X. Y. Xu, S. Kesari, X. S. Xie, S. T. C. Wong, and G. S. Young, “Chemically-selective imaging of brain structures with CARS microscopy,” Opt. Express 15(19), 12076–12087 (2007). [CrossRef] [PubMed]

]. In this case, the image contrast is based mostly at the intense CH-stretching vibrational mode of lipids around 2900 cm−1. As a consequence, CARS investigation of brain tissue could not take advantage of modern multivariate data analysis [17

17. C. Krafft, A. A. Ramoji, C. Bielecki, N. Vogler, T. Meyer, D. Akimov, P. Rösch, M. Schmitt, B. Dietzek, I. Petersen, A. Stallmach, and J. Popp, “A comparative Raman and CARS imaging study of colon tissue,” J Biophotonics 2(5), 303–312 (2009). [CrossRef] [PubMed]

] as it is performed with spontaneous Raman spectroscopy [18

18. T. Meyer, N. Bergner, C. Bielecki, C. Krafft, D. Akimov, B. F. M. Romeike, R. Reichart, R. Kalff, B. Dietzek, and J. Popp, “Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis,” J. Biomed. Opt. 16(2), 021113 (2011). [CrossRef] [PubMed]

]. Recently we have addressed thoroughly these two challenges in CARS microscopy by combining multiplex CARS (MCARS) [19

19. B. von Vacano, L. Meyer, and M. Motzkus, “Rapid polymer blend imaging with quantitative broadband multiplex CARS microscopy,” J. Raman Spectrosc. 38(7), 916–926 (2007). [CrossRef]

] with a “two-step” analysis procedure [20

20. C. Pohling, T. Buckup, and M. Motzkus, “Hyperspectral data processing for chemoselective multiplex coherent anti-Stokes Raman scattering microscopy of unknown samples,” J. Biomed. Opt. 16(2), 021105 (2011). [CrossRef] [PubMed]

]. In this regard, we have extracted the Raman information from the MCARS raw data and classified it afterwards by multivariate statistics. This procedure led to a chemoselective image of complex structured polymer samples and biological samples. Here, we exploit the “two-step” method and apply it to MCARS studies of mouse brain tissue in order to obtain chemoselective image contrast based on a full Raman spectrum. The results were compared to brightfield images of hematoxylin and eosin (H&E) stained references and to lipid intensity contrasted images of MCARS raw data.

2. Experimental section

The MCARS experimental setup is illustrated in Fig. 1
Fig. 1 MCARS experimental setup. BS, beam splitter; F1, band pass filter cuts out the narrowband pump and probe beam; PCF, photonic crystal fiber broadens the Stokes beam; F2, long pass filter where pump and Stokes are spatially overlaid again; MO1, microscope objective for measurement; MO2, microscope objective for signal output collimation; F3, short pass filter.
. The most important issues have been already described [19

19. B. von Vacano, L. Meyer, and M. Motzkus, “Rapid polymer blend imaging with quantitative broadband multiplex CARS microscopy,” J. Raman Spectrosc. 38(7), 916–926 (2007). [CrossRef]

22

22. H. Kano and H. Hamaguchi, “Dispersion-compensated supercontinuum generation for ultrabroadband multiplex coherent anti-Stokes Raman scattering spectroscopy,” J. Raman Spectrosc. 37(1-3), 411–415 (2006). [CrossRef]

]. It is based on the 1.0 W output power of a Ti:sapphire fs-oscillator (Coherent Mira), with the 6 nm-wide spectrum (FWHM) that is split in two parts. About 100 mW are reflected into an end sealed photonic crystal fiber (crystal fiber, A/S) whereas the second part passes a narrowband filter of 1 nm FWHM. The resulting broadband continuum as well as the narrowband pulse is coupled into a commercially available light microscope modified for MCARS imaging. The setup has been equipped with a 50x IR corrected microscope objective with high working distance (Olympus LMPlan50xIR) and piezo driven sample holder. The signal ranging from 500 to 3400 cm−1 is detected by a CCD camera (Andor Idus). A sample area of 100x100 µm was raster scanned with a step size of 1 µm for each scan. The acquisition time per pixel was 200 ms. After submission of this work, we improved our setup regarding hardware and software, which allows measurements as fast as 4 minutes, with pixel dwell times of 20 ms without lowering the signal to noise ratio. Total average power in the focus volume was less than 30 mW.

Concerning the application to biological samples, sections (20 µm) of fresh mouse brain tissue were prepared. Every other slide was stained with hematoxylin-eosin (H&E) as a reference that allowed for the microscopic identification of the different cells. The native slides, all transparent in bright field contrast, were taken for the CARS experiments and air tightly sealed (Gene-Frame, Thermo Scientific, UK) in order to prevent desiccation. No additional water was added to avoid moisture expansion.

3. Results and Discussion

In Fig. 2
Fig. 2 Comparison of images taken by traditional bright field contrast of HE stained samples with our two step approach as well as the plot of the intensity at 2845 cm−1 wavenumbers. (a) (b) Bright field image of three different slides stained by HE showing grey matter (orange), Purkinje cells (red), nuclei of granule cells (dark blue) and white matter (myelin, pink fiber bundles). (c), (d) The corresponding images of the boxed areas using MCARS microscopy, imaginary part extraction and PCA. White matter or myelin (bright green), nuclei of granule cells (red), Purkinje cells (purple) and grey matter (dark green) are reproduced accordingly. (e), (f) Contrast based on CH-vibrational mode of lipids, taken from the raw spectra at 2845 cm−1.
we compare the bright field contrasted images of the HE stained slides with our results of MCARS experiment. Figure 2(a) shows a portion of the cerebellar white matter (asterisks) bordered by the granule cell layers (lower left and upper right, blue staining reflects the nuclei of the granule cells). Figure 2(b) shows the regular cerebellar architecture: myelin on top followed by the granule cell layer which is followed by the Purkinje-cell layer (arrows) and the molecular cell layer. In order to evaluate MCARS analyzed by our two-step method, we selected different areas of the HE stained slides in Figs. 2(a) and (b). These areas are marked as black insets in Figs. 2(a) and (b) and the processed MCARS images are shown in Figs. 2(c) and (d), respectively. In Fig. 2(c) the lipid rich myelin provides the strongest signal and appears in green color. The granule cells expressed as scores of the second weight vector are shown in Red. The scores of the third vector are plotted in blue color enhancing the contrast between myelin and the granule cells. The MCARS based image in Fig. 2(d) allows also to differentiate between the granule cells (red), surrounding tissue (green) and the single layer of Purkinje cells that appear in purple as a mixing term of blue and red. Please note that the eigenvectors obtained by PCA cannot be directly compared between different data sets. As mentioned above, the MCARS data was collected from unstained slides that contain tissue which might be up to 20 µm separated from the associated reference. It follows that single nuclei of the granule cells appear at different positions than in Fig. 2(b). In the third column of Fig. 2, our method is compared to the contrast mechanism typically used in CARS microscopy with narrowband lasers, where the detection is constrained to single spectral features [9

9. C. L. Evans and X. S. Xie, “Coherent anti-stokes Raman scattering microscopy: chemical imaging for biology and medicine,” Annu Rev Anal Chem (Palo Alto Calif) 1(1), 883–909 (2008). [CrossRef] [PubMed]

]. In this case, the intense CH-stretching vibrational mode taken from CARS raw data at 2845 cm−1 provides image contrast when the signal strength is color coded from red to white. This method is mainly sensitive to dense and lipid rich sample components such as myelin appearing in bright structures in Figs. 2(e) and (f). On one hand, the color levels provide a well contrasted map of lipid intensities. On the other hand, different colors are not assigned to different spectral information and therefore lack an essential feature for tissue classification. This can be clearly seen, for example, in Figs. 2(c) and (e): In Fig. 2(c), granule cells (red) can be easily differentiated from myelin rich tissue (green), while Fig. 2(e) provides no further chemical information besides lipid contrast.

The quantitative distribution of different lipid components is a point of interest when CARS is applied in medical diagnostics. Hence, it can help to identify tumor margins that do not appear in bright field contrasted images. As an example of such an application, in Fig. 4
Fig. 4 Three different types of lipid monitoring along the diagonal line in Figs. 3(b) and (c). Red line: Extracted Raman intensity at 2860 cm−1. Black line: Raw data intensity at 2845 cm−1. Blue line: Quantitative fitting result according to Fig. 3(c) showing improved contrast and also a Myelin ratio which is not affected by other sample components anymore (zero amplitude).
we compared three types of data analysis for monitoring the lipid distribution from the lower right to the upper left in Fig. 3 (along the diagonal dashed line in (b) and (c)). The simplest and mostly used approach is to plot the CH-stretching vibrational mode’s intensity taken from the raw data at 2845 cm−1. The variation of the intensity of this mode shows initially a steep decrease down to a level of about 40% of Myelin. The similar trend is well reproduced by taking the amplitude of the extracted linear Raman data set at 2860 cm−1, which corresponds to the blue shifted signal at 2845 cm−1 in the MCARS raw data. Both methods, however, do not take into account the spectral overlapping of other contributions at the same spectral region and, therefore, cannot represent Myelin concentration exclusively. In contrast to that, by applying the two-step method followed by the evolutionary fitting algorithm, we are able to demonstrate that the real Myelin concentration shows a similar trend along the diagonal of Fig. 3(c), but with much higher accuracy and, in particular it also shows sample regions where Myelin is not present at all. The zero amplitude demonstrates that the Myelin ratio is not affected by other sample components anymore.

4. Conclusion and outlook

Acknowledgments

We gratefully acknowledge the BMBF MEDICARS project for funding and generous support of this research-project. The excellent technical assistance of Mrs. Ginette Bortolussi is gratefully acknowledged.

References and links

1.

A. Zumbusch, G. R. Holtom, and X. S. Xie, “Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering,” Phys. Rev. Lett. 82(20), 4142–4145 (1999). [CrossRef]

2.

A. Volkmer, J. X. Cheng, L. D. Book, and X. S. Xie, “New advances in Coherent anti-Stokes Raman scattering (CARS) microscopy and spectroscopy of biological systems,” Biophys. J. 80, 164a (2001).

3.

M. Harz, P. Rösch, and J. Popp, “Vibrational spectroscopy--a powerful tool for the rapid identification of microbial cells at the single-cell level,” Cytometry A 75A(2), 104–113 (2009). [CrossRef] [PubMed]

4.

H. W. Wang, T. T. Le, and J. X. Cheng, “Label-free imaging of arterial cells and extracellular matrix using a multimodal CARS microscope,” Opt. Commun. 281(7), 1813–1822 (2008). [CrossRef] [PubMed]

5.

M. Okuno, H. Kano, P. Leproux, V. Couderc, and H. O. Hamaguchi, “Ultrabroadband multiplex CARS microspectroscopy and imaging using a subnanosecond supercontinuum light source in the deep near infrared,” Opt. Lett. 33(9), 923–925 (2008). [CrossRef] [PubMed]

6.

A. Dogariu, A. Goltsov, and M. O. Scully, “Real-time monitoring of blood using coherent anti-Stokes Raman spectroscopy,” J. Biomed. Opt. 13(5), 054004 (2008). [CrossRef] [PubMed]

7.

H. A. Rinia, M. Bonn, E. M. Vartiainen, C. B. Schaffer, and M. Müller, “Spectroscopic analysis of the oxygenation state of hemoglobin using coherent anti-Stokes Raman scattering,” J. Biomed. Opt. 11(5), 050502 (2006). [CrossRef] [PubMed]

8.

C. L. Evans, X. Y. Xu, S. Kesari, X. S. Xie, S. T. C. Wong, and G. S. Young, “Chemically-selective imaging of brain structures with CARS microscopy,” Opt. Express 15(19), 12076–12087 (2007). [CrossRef] [PubMed]

9.

C. L. Evans and X. S. Xie, “Coherent anti-stokes Raman scattering microscopy: chemical imaging for biology and medicine,” Annu Rev Anal Chem (Palo Alto Calif) 1(1), 883–909 (2008). [CrossRef] [PubMed]

10.

C. Krafft, B. Dietzek, and J. Popp, “Raman and CARS microspectroscopy of cells and tissues,” Analyst (Lond.) 134(6), 1046–1057 (2009). [CrossRef] [PubMed]

11.

C. Krafft, L. Shapoval, S. B. Sobottka, K. D. Geiger, G. Schackert, and R. Salzer, “Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images,” Biochim. Biophys. Acta 1758(7), 883–891 (2006). [CrossRef] [PubMed]

12.

C. Krafft, G. Steiner, C. Beleites, and R. Salzer, “Disease recognition by infrared and Raman spectroscopy,” J Biophotonics 2(1-2), 13–28 (2009). [CrossRef] [PubMed]

13.

B. Bird, M. Miljković, N. Laver, and M. Diem, “Spectral detection of micro-metastases and individual metastatic cells in lymph node histology,” Technol. Cancer Res. Treat. 10(2), 135–144 (2011). [PubMed]

14.

E. M. Vartiainen, “Phase Retrieval Approach for Coherent Anti-Stokes-Raman Scattering Spectrum Analysis,” J. Opt. Soc. Am. B 9(8), 1209–1214 (1992). [CrossRef]

15.

H. A. Rinia, M. Bonn, M. Müller, and E. M. Vartiainen, “Quantitative CARS spectroscopy using the maximum entropy method: the main lipid phase transition,” ChemPhysChem 8(2), 279–287 (2007). [CrossRef] [PubMed]

16.

Y. X. Liu, Y. J. Lee, and M. T. Cicerone, “Fast extraction of resonant vibrational response from CARS spectra with arbitrary nonresonant background,” J. Raman Spectrosc. 40(7), 726–731 (2009). [CrossRef]

17.

C. Krafft, A. A. Ramoji, C. Bielecki, N. Vogler, T. Meyer, D. Akimov, P. Rösch, M. Schmitt, B. Dietzek, I. Petersen, A. Stallmach, and J. Popp, “A comparative Raman and CARS imaging study of colon tissue,” J Biophotonics 2(5), 303–312 (2009). [CrossRef] [PubMed]

18.

T. Meyer, N. Bergner, C. Bielecki, C. Krafft, D. Akimov, B. F. M. Romeike, R. Reichart, R. Kalff, B. Dietzek, and J. Popp, “Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis,” J. Biomed. Opt. 16(2), 021113 (2011). [CrossRef] [PubMed]

19.

B. von Vacano, L. Meyer, and M. Motzkus, “Rapid polymer blend imaging with quantitative broadband multiplex CARS microscopy,” J. Raman Spectrosc. 38(7), 916–926 (2007). [CrossRef]

20.

C. Pohling, T. Buckup, and M. Motzkus, “Hyperspectral data processing for chemoselective multiplex coherent anti-Stokes Raman scattering microscopy of unknown samples,” J. Biomed. Opt. 16(2), 021105 (2011). [CrossRef] [PubMed]

21.

T. W. Kee and M. T. Cicerone, “Simple approach to one-laser, broadband coherent anti-Stokes Raman scattering microscopy,” Opt. Lett. 29(23), 2701–2703 (2004). [CrossRef] [PubMed]

22.

H. Kano and H. Hamaguchi, “Dispersion-compensated supercontinuum generation for ultrabroadband multiplex coherent anti-Stokes Raman scattering spectroscopy,” J. Raman Spectrosc. 37(1-3), 411–415 (2006). [CrossRef]

23.

E. M. Vartiainen, H. A. Rinia, M. Müller, and M. Bonn, “Direct extraction of Raman line-shapes from congested CARS spectra,” Opt. Express 14(8), 3622–3630 (2006). [CrossRef] [PubMed]

24.

R. Viviani, G. Grön, and M. Spitzer, “Functional principal component analysis of fMRI data,” Hum. Brain Mapp. 24(2), 109–129 (2005). [CrossRef] [PubMed]

25.

A. Mizuno, T. Hayashi, K. Tashibu, S. Maraishi, K. Kawauchi, and Y. Ozaki, “Near-infrared FT-Raman spectra of the rat brain tissues,” Neurosci. Lett. 141(1), 47–52 (1992). [CrossRef] [PubMed]

26.

K. V. Branden and M. Hubert, “Robust classification in high dimensions based on the SIMCA method,” Chemom. Intell. Lab. Syst. 79(1-2), 10–21 (2005). [CrossRef]

OCIS Codes
(170.0170) Medical optics and biotechnology : Medical optics and biotechnology
(180.0180) Microscopy : Microscopy
(300.6230) Spectroscopy : Spectroscopy, coherent anti-Stokes Raman scattering

ToC Category:
Microscopy

History
Original Manuscript: May 11, 2011
Revised Manuscript: June 27, 2011
Manuscript Accepted: June 27, 2011
Published: June 30, 2011

Citation
Christoph Pohling, Tiago Buckup, Axel Pagenstecher, and Marcus Motzkus, "Chemoselective imaging of mouse brain tissue via multiplex CARS microscopy," Biomed. Opt. Express 2, 2110-2116 (2011)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-2-8-2110


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. A. Zumbusch, G. R. Holtom, and X. S. Xie, “Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering,” Phys. Rev. Lett. 82(20), 4142–4145 (1999). [CrossRef]
  2. A. Volkmer, J. X. Cheng, L. D. Book, and X. S. Xie, “New advances in Coherent anti-Stokes Raman scattering (CARS) microscopy and spectroscopy of biological systems,” Biophys. J. 80, 164a (2001).
  3. M. Harz, P. Rösch, and J. Popp, “Vibrational spectroscopy--a powerful tool for the rapid identification of microbial cells at the single-cell level,” Cytometry A 75A(2), 104–113 (2009). [CrossRef] [PubMed]
  4. H. W. Wang, T. T. Le, and J. X. Cheng, “Label-free imaging of arterial cells and extracellular matrix using a multimodal CARS microscope,” Opt. Commun. 281(7), 1813–1822 (2008). [CrossRef] [PubMed]
  5. M. Okuno, H. Kano, P. Leproux, V. Couderc, and H. O. Hamaguchi, “Ultrabroadband multiplex CARS microspectroscopy and imaging using a subnanosecond supercontinuum light source in the deep near infrared,” Opt. Lett. 33(9), 923–925 (2008). [CrossRef] [PubMed]
  6. A. Dogariu, A. Goltsov, and M. O. Scully, “Real-time monitoring of blood using coherent anti-Stokes Raman spectroscopy,” J. Biomed. Opt. 13(5), 054004 (2008). [CrossRef] [PubMed]
  7. H. A. Rinia, M. Bonn, E. M. Vartiainen, C. B. Schaffer, and M. Müller, “Spectroscopic analysis of the oxygenation state of hemoglobin using coherent anti-Stokes Raman scattering,” J. Biomed. Opt. 11(5), 050502 (2006). [CrossRef] [PubMed]
  8. C. L. Evans, X. Y. Xu, S. Kesari, X. S. Xie, S. T. C. Wong, and G. S. Young, “Chemically-selective imaging of brain structures with CARS microscopy,” Opt. Express 15(19), 12076–12087 (2007). [CrossRef] [PubMed]
  9. C. L. Evans and X. S. Xie, “Coherent anti-stokes Raman scattering microscopy: chemical imaging for biology and medicine,” Annu Rev Anal Chem (Palo Alto Calif) 1(1), 883–909 (2008). [CrossRef] [PubMed]
  10. C. Krafft, B. Dietzek, and J. Popp, “Raman and CARS microspectroscopy of cells and tissues,” Analyst (Lond.) 134(6), 1046–1057 (2009). [CrossRef] [PubMed]
  11. C. Krafft, L. Shapoval, S. B. Sobottka, K. D. Geiger, G. Schackert, and R. Salzer, “Identification of primary tumors of brain metastases by SIMCA classification of IR spectroscopic images,” Biochim. Biophys. Acta 1758(7), 883–891 (2006). [CrossRef] [PubMed]
  12. C. Krafft, G. Steiner, C. Beleites, and R. Salzer, “Disease recognition by infrared and Raman spectroscopy,” J Biophotonics 2(1-2), 13–28 (2009). [CrossRef] [PubMed]
  13. B. Bird, M. Miljković, N. Laver, and M. Diem, “Spectral detection of micro-metastases and individual metastatic cells in lymph node histology,” Technol. Cancer Res. Treat. 10(2), 135–144 (2011). [PubMed]
  14. E. M. Vartiainen, “Phase Retrieval Approach for Coherent Anti-Stokes-Raman Scattering Spectrum Analysis,” J. Opt. Soc. Am. B 9(8), 1209–1214 (1992). [CrossRef]
  15. H. A. Rinia, M. Bonn, M. Müller, and E. M. Vartiainen, “Quantitative CARS spectroscopy using the maximum entropy method: the main lipid phase transition,” ChemPhysChem 8(2), 279–287 (2007). [CrossRef] [PubMed]
  16. Y. X. Liu, Y. J. Lee, and M. T. Cicerone, “Fast extraction of resonant vibrational response from CARS spectra with arbitrary nonresonant background,” J. Raman Spectrosc. 40(7), 726–731 (2009). [CrossRef]
  17. C. Krafft, A. A. Ramoji, C. Bielecki, N. Vogler, T. Meyer, D. Akimov, P. Rösch, M. Schmitt, B. Dietzek, I. Petersen, A. Stallmach, and J. Popp, “A comparative Raman and CARS imaging study of colon tissue,” J Biophotonics 2(5), 303–312 (2009). [CrossRef] [PubMed]
  18. T. Meyer, N. Bergner, C. Bielecki, C. Krafft, D. Akimov, B. F. M. Romeike, R. Reichart, R. Kalff, B. Dietzek, and J. Popp, “Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis,” J. Biomed. Opt. 16(2), 021113 (2011). [CrossRef] [PubMed]
  19. B. von Vacano, L. Meyer, and M. Motzkus, “Rapid polymer blend imaging with quantitative broadband multiplex CARS microscopy,” J. Raman Spectrosc. 38(7), 916–926 (2007). [CrossRef]
  20. C. Pohling, T. Buckup, and M. Motzkus, “Hyperspectral data processing for chemoselective multiplex coherent anti-Stokes Raman scattering microscopy of unknown samples,” J. Biomed. Opt. 16(2), 021105 (2011). [CrossRef] [PubMed]
  21. T. W. Kee and M. T. Cicerone, “Simple approach to one-laser, broadband coherent anti-Stokes Raman scattering microscopy,” Opt. Lett. 29(23), 2701–2703 (2004). [CrossRef] [PubMed]
  22. H. Kano and H. Hamaguchi, “Dispersion-compensated supercontinuum generation for ultrabroadband multiplex coherent anti-Stokes Raman scattering spectroscopy,” J. Raman Spectrosc. 37(1-3), 411–415 (2006). [CrossRef]
  23. E. M. Vartiainen, H. A. Rinia, M. Müller, and M. Bonn, “Direct extraction of Raman line-shapes from congested CARS spectra,” Opt. Express 14(8), 3622–3630 (2006). [CrossRef] [PubMed]
  24. R. Viviani, G. Grön, and M. Spitzer, “Functional principal component analysis of fMRI data,” Hum. Brain Mapp. 24(2), 109–129 (2005). [CrossRef] [PubMed]
  25. A. Mizuno, T. Hayashi, K. Tashibu, S. Maraishi, K. Kawauchi, and Y. Ozaki, “Near-infrared FT-Raman spectra of the rat brain tissues,” Neurosci. Lett. 141(1), 47–52 (1992). [CrossRef] [PubMed]
  26. K. V. Branden and M. Hubert, “Robust classification in high dimensions based on the SIMCA method,” Chemom. Intell. Lab. Syst. 79(1-2), 10–21 (2005). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1 Fig. 2 Fig. 3
 
Fig. 4
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited