Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging.

Christoph Pohling,Gabriele Schackert,Michael Schmitt,Mariana S Ishikawa,Alex S Duarte,Thomas Bocklitz,Marcus Motzkus,Matthias Kirsch,Jürgen Popp,Ortrud Uckermann,Tiago Buckup,Benjamin Dietzeck,Cinzia Emmanuello

doi:10.1117/1.jbo.22.6.066005

Abstract

Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

Highlights

The application of coherent anti-Stokes Raman scattering (CARS) to microscopy provides access to structural and molecular information on a vast range of chemical and biological systems.[1,2,3,4,5,6] In general, structural information from CARS microscopy is obtained associating a single spectral intensity with a certain color scheme
Taking the lower spectral resolution of the CARS setup into account, the CH-stretching region shows good agreement with the data obtained from spontaneous Raman spectroscopy and is considered to have a higher influence on the later classification result compared with the fingerprint region that is not as prominent as in the Raman control in this study
While the spontaneous Raman spectrum was recorded with an acquisition time of 500 ms, the Ramanextracted Multiplex coherent anti-Stokes Raman scattering (MCARS) spectrum of the same specimen is measured within just 60 ms, but it still shows a three times better signal-tonoise ratio

Summary

Introduction

The application of coherent anti-Stokes Raman scattering (CARS) to microscopy provides access to structural and molecular information on a vast range of chemical and biological systems.[1,2,3,4,5,6] In general, structural information from CARS microscopy is obtained associating a single spectral intensity with a certain color scheme. One way of circumventing this issue can be efficiently implemented if the single chemical constituents are known in advance: in this case, the measured spectra are fitted as the sum of the Raman spectra of the isolated constituents.[7] in a situation where the Raman spectrum of each component is not known a priori, a different approach must be applied In this regard, a multistep analysis[8] of CARS data can be implemented by combining Raman reconstruction algorithms,[9] principal component analysis (PCA), and decomposition of the measured data using pure spectra.

Objectives

Results

Conclusion