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Restoring morphology of light sheet microscopy data based on magnetic resonance histology.

Publication ,  Journal Article
Tian, Y; Cook, JJ; Johnson, GA
Published in: Front Neurosci
2022

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.

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Published In

Front Neurosci

DOI

ISSN

1662-4548

Publication Date

2022

Volume

16

Start / End Page

1011895

Location

Switzerland

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 1702 Cognitive Sciences
  • 1701 Psychology
  • 1109 Neurosciences
 

Citation

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MLA
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Tian, Y., Cook, J. J., & Johnson, G. A. (2022). Restoring morphology of light sheet microscopy data based on magnetic resonance histology. Front Neurosci, 16, 1011895. https://doi.org/10.3389/fnins.2022.1011895
Tian, Yuqi, James J. Cook, and G Allan Johnson. “Restoring morphology of light sheet microscopy data based on magnetic resonance histology.Front Neurosci 16 (2022): 1011895. https://doi.org/10.3389/fnins.2022.1011895.
Tian Y, Cook JJ, Johnson GA. Restoring morphology of light sheet microscopy data based on magnetic resonance histology. Front Neurosci. 2022;16:1011895.
Tian, Yuqi, et al. “Restoring morphology of light sheet microscopy data based on magnetic resonance histology.Front Neurosci, vol. 16, 2022, p. 1011895. Pubmed, doi:10.3389/fnins.2022.1011895.
Tian Y, Cook JJ, Johnson GA. Restoring morphology of light sheet microscopy data based on magnetic resonance histology. Front Neurosci. 2022;16:1011895.

Published In

Front Neurosci

DOI

ISSN

1662-4548

Publication Date

2022

Volume

16

Start / End Page

1011895

Location

Switzerland

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 1702 Cognitive Sciences
  • 1701 Psychology
  • 1109 Neurosciences