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摘要
Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors.
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索引
1 University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University of Sussex, Department of Neuroscience, Brighton and Sussex Medical School, Brighton, UK (GRID:grid.12082.39) (ISNI:0000 0004 1936 7590); CUBRIC, Cardiff University, Cardiff, UK (GRID:grid.5600.3) (ISNI:0000 0001 0807 5670); Polytechnique Montreal, NeuroPoly Lab, Montreal, Canada (GRID:grid.183158.6) (ISNI:0000 0004 0435 3292)
2 University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
3 University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University College London, Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
4 University College London, Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
5 University College London, Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University College London, Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
6 University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); Massachusetts General Hospital and Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924); Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)