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Liu CJ, Ammon W, Jones RJ, Nolan JC, Gong D, Maffei C, Edlow BL, Augustinack JC, Magnain C, Yendiki A, Villiger M, Fischl B, Wang H. Quantitative imaging of three-dimensional fiber orientation in the human brain via two illumination angles using polarization-sensitive optical coherence tomography. bioRxiv 2023:2023.10.20.563298. [PMID: 37961162 PMCID: PMC10634685 DOI: 10.1101/2023.10.20.563298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Optical imaging methods such as polarization-sensitive optical coherence tomography (PS-OCT) provide important tools to directly quantify fiber orientation at micrometer resolution. However, brain imaging based on the optic axis by PS-OCT so far has been limited to two-dimensional in-plane orientation, preventing the comprehensive study of connectivity in 3D. In this work, we present a novel method to obtain the 3D fiber orientation in full angular space with only two illumination angles. We measure the optic axis orientation and the apparent birefringence by PS-OCT from a normal and a 15 deg tilted illumination, and then apply a computational method yielding the 3D optic axis orientation and true birefringence. We verify that our method accurately recovers a large range of through-plane orientations from -85 deg to 85 deg with a high angular precision. We further present 3D fiber orientation maps of entire coronal sections of human cerebrum and brainstem with 10 μm in-plane resolution, revealing unprecedented details of fiber configurations. We envision that further development of our method will open a promising avenue towards large-scale 3D fiber axis mapping in the human brain and other complex fibrous tissues at microscopic level.
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Liu CJ, Ammon W, Jones RJ, Nolan J, Wang R, Chang S, Frosch MP, Yendiki A, Boas DA, Magnain C, Fischl B, Wang H. Refractive-index matching enhanced polarization sensitive optical coherence tomography quantification in human brain tissue. Biomed Opt Express 2022; 13:358-372. [PMID: 35154876 PMCID: PMC8803034 DOI: 10.1364/boe.443066] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 05/11/2023]
Abstract
The importance of polarization-sensitive optical coherence tomography (PS-OCT) has been increasingly recognized in human brain imaging. Despite the recent progress of PS-OCT in revealing white matter architecture and orientation, quantification of fine-scale fiber tracts in the human brain cortex has been a challenging problem, due to a low birefringence in the gray matter. In this study, we investigated the effect of refractive index matching by 2,2'-thiodiethanol (TDE) immersion on the improvement of PS-OCT measurements in ex vivo human brain tissue. We show that we can obtain fiber orientation maps of U-fibers that underlie sulci, as well as cortical fibers in the gray matter, including radial fibers in gyri and distinct layers of fibers exhibiting laminar organization. Further analysis shows that index matching reduces the noise in axis orientation measurements by 56% and 39%, in white and gray matter, respectively. Index matching also enables precise measurements of apparent birefringence, which was underestimated in the white matter by 82% but overestimated in the gray matter by 16% prior to TDE immersion. Mathematical simulations show that the improvements are primarily attributed to the reduction in the tissue scattering coefficient, leading to an enhanced signal-to-noise ratio in deeper tissue regions, which could not be achieved by conventional noise reduction methods.
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Affiliation(s)
- Chao J Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - William Ammon
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Robert J Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Jackson Nolan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Ruopeng Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Shuaibin Chang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - David A Boas
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Caroline Magnain
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
- MIT HST, Computer Science and AI Lab, Cambridge, MA 02139, USA
| | - Hui Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, USA
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Liu CJ, Ammon W, Siless V, Fogarty M, Wang R, Atzeni A, Aganj I, Iglesias JE, Zöllei L, Fischl B, Schmahmann JD, Wang H. Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution. Neuroimage 2021; 244:118627. [PMID: 34607020 PMCID: PMC8603939 DOI: 10.1016/j.neuroimage.2021.118627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/23/2021] [Accepted: 09/27/2021] [Indexed: 11/17/2022] Open
Abstract
The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.
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Affiliation(s)
- Chao J Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - William Ammon
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - Viviana Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO 63130, and Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Ruopeng Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - Alessia Atzeni
- Centre for Medical Image Computing, University College London, United Kingdom
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States; Centre for Medical Image Computing, University College London, United Kingdom; MIT HST, Computer Science and AI Lab, Cambridge, MA 02139, United States
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States; MIT HST, Computer Science and AI Lab, Cambridge, MA 02139, United States
| | - Jeremy D Schmahmann
- Ataxia Center, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, United States
| | - Hui Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA 02129, United States.
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