Improvement of tissue analysis and classification using optical coherence tomography combined with Raman spectroscopy

Chih-Hao Liu

Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd., Houston, Texas 77204-5060, USA

Ji Qi

Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Rd., Houston, Texas 77204, USA

Jing Lu

Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Rd., Houston, Texas 77204, USA

Shang Wang

Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd., Houston, Texas 77204-5060, USA

Chen Wu

Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd., Houston, Texas 77204-5060, USA

Wei-Chuan Shih

Department of Electrical and Computer Engineering, University of Houston, 4800 Calhoun Rd., Houston, Texas 77204, USA

Kirill V. Larin

Department of Biomedical Engineering, University of Houston, 3605 Cullen Blvd., Houston, Texas 77204-5060, USA

Department of Molecular Physiology and Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA

Received: 15 March 2014

Accepted: 10 April 2014

Published: 12 June 2014

Optical coherence tomography (OCT) provides significant advantages of high resolution (approaching the histopathology level) real-time imaging of tissues without use of contrast agents. Based on these advantages, the microstructural features of tumors can be visualized and detected intra-operatively. However, it is still not clinically accepted for tumor margin delineation due to poor specificity and accuracy. In contrast, Raman spectroscopy (RS) can obtain tissue information at the molecular level, but does not provide real-time imaging capability. Therefore, combining OCT and RS could provide synergy. To this end, we present a tissue analysis and classification method using both the slope of OCT intensity signal vs depth and the principle components from the RS spectrum as the indicators for tissue characterization. The goal of this study was to understand prediction accuracy of OCT and combined OCT/RS method for classification of optically similar tissues and organs. Our pilot experiments were performed on mouse kidneys, livers, and small intestines (SIs). The prediction accuracy with five-fold cross validation of the method has been evaluated by the support vector machine (SVM) method. The results demonstrate that tissue characterization based on the OCT/RS method was superior compared to using OCT structural information alone. This combined OCT/RS method is potentially useful as a noninvasive optical biopsy technique for rapid and automatic tissue characterization during surgery.

Keywords: OCT signal slope; principle component analysis; multi-support vector machine; Raman spectra

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