Optical Coherence Tomography for Brain Imaging

  • Gangjun Liu
  • Zhongping Chen
Part of the Bioanalysis book series (BIOANALYSIS, volume 3)


Recently, there has been growing interest in using OCT for brain imaging. A feasibility study of OCT for guiding deep brain probes has found that OCT can differentiate the white matter and gray matter because the white matter tends to have a higher peak reflectivity and steeper attenuation rate compared to gray matter. In vivo 3D visualization of the layered organization of a rat olfactory bulb with OCT has been demonstrated. OCT has been used for single myelin fiber imaging in living rodents without labeling. The refractive index in the rat somatosensory cortex has also been measured with OCT. In addition, functional extension of OCT, such as Doppler-OCT (D-OCT), polarization sensitive-OCT (PS-OCT), and phase-resolved-OCT (PR-OCT), can image and quantify physiological parameters in addition to the morphological structure image. Based on the scattering changes during neural activity, OCT has been used to measure the functional activation in neuronal tissues. PS-OCT, which combines polarization sensitive detection with OCT to determine tissue birefringence, has been used for the localization of nerve fiber bundles and the mapping of micrometer-scale fiber pathways in the brain. D-OCT, also named optical Doppler tomography (ODT), combines the Doppler principle with OCT to obtain high resolution tomographic images of moving constituents in highly scattering biological tissues. D-OCT has been successfully used to image cortical blood flow and map the blood vessel network for brain research. In this chapter, the principle and technology of OCT and D-OCT are reviewed and examples of potential applications are described.


Optical Coherence Tomography Optical Coherence Tomography Image Rose Bengal Optical Coherence Tomography System Broadband Light Source 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to acknowledge Dr. Christopher Lay, Dr. Melissa Davis, and Prof. Ron Frostig for preparing the rat used in the manuscript. Dr. Chen also acknowledges grant support from the National Institutes of Health (R01EB-10090, R01EY-021529, P41EB-015890, R01HL-103764, and R01HL-105215), Air Force Office of Scientific Research (F49620-00-1-0371, FA9550-04-0101), and the Beckman Laser Institute Endowment.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringBeckman Laser Institute, University of CaliforniaIrvineUSA

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