While histology remains the gold standard for assessing human neuroanatomy, the procedures for sectioning and hand mounting tissue for microscopic imaging are not substantially different than they were 100 years ago. These steps introduce irremediable distortions into the tissue sections making it difficult or impossible to align sections with suffiient accuracy to create 3D histological volumes at the micron scale. In this project, we seek to develop acquisition and analysis tools that use optical coherence tomography (OCT) to generate images that contain information comparable to standard histology. Critically, OCT images the tissue prior to cutting, thus avoiding the concomitant distortions, and allowing large regions of human tissue to be imaged with micron resolution. We anticipate that these large-scale, veridical representations will facilitate the development of automated techniques for tissue quantification and disease detection, dramatically increasing the efficiency, specificity an sensitivity of histology and neuropathology.

Public Health Relevance

Microscopic analysis of cut, mounted and stained sections remains the gold standard of histology and neuropathology, and is the only current means for definitively diagnosing diseases such as Alzheimer's. In this project we see to replace this labor intensive and distortion prone procedure with tools for automatically generating undistorted 3D volumes with microscopic resolution suitable for automated analysis, with the ultimate goal of making the characterization of normal tissue properties and the quantification of disease effects more efficient, accurate, sensitive and specific.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Exploratory/Developmental Grants (R21)
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Neuroscience and Ophthalmic Imaging Technologies Study Section (NOIT)
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Shabestari, Behrouz
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Massachusetts General Hospital
United States
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