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.
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.
| Greve, Douglas N; Fischl, Bruce (2018) False positive rates in surface-based anatomical analysis. Neuroimage 171:6-14 |
| Bianciardi, Marta; Strong, Christian; Toschi, Nicola et al. (2018) A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI. Neuroimage 170:222-230 |
| Aganj, Iman; Harisinghani, Mukesh G; Weissleder, Ralph et al. (2018) Unsupervised Medical Image Segmentation Based on the Local Center of Mass. Sci Rep 8:13012 |
| Wu, Jianxiao; Ngo, Gia H; Greve, Douglas et al. (2018) Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum Brain Mapp : |
| Fischl, Bruce; Sereno, Martin I (2018) Microstructural parcellation of the human brain. Neuroimage 182:219-231 |
| Magnain, Caroline; Augustinack, Jean C; Tirrell, Lee et al. (2018) Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann's area 32 and area 21. Brain Struct Funct : |
| Li, Yi; Barkovich, Matthew J; Karch, Celeste M et al. (2018) Regionally specific TSC1 and TSC2 gene expression in tuberous sclerosis complex. Sci Rep 8:13373 |
| Siless, Viviana; Chang, Ken; Fischl, Bruce et al. (2018) AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 166:32-45 |
| Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia et al. (2018) Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 168:296-320 |
| Zaretskaya, Natalia; Fischl, Bruce; Reuter, Martin et al. (2018) Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. Neuroimage 165:11-26 |
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