Tensor image registration library: deformable registration of stand -alone histology images to whole-brain post-mortem MRI data
Author(s)
Huszar, Istvan N
Pallebage-Gamarallage, Menuka
Bangerter-Christensen, Sarah
Brooks, Hannah
Fitzgibbon, Sean
Type
Journal Article
Abstract
Background
Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data.
Methods
Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline.
Results
All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2–0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks.
Conclusions
Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.
Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data.
Methods
Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline.
Results
All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2–0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks.
Conclusions
Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.
Date Issued
2023-01-01
Date Acceptance
2022-12-04
Citation
NeuroImage, 2023, 265
ISSN
1053-8119
Publisher
Elsevier
Journal / Book Title
NeuroImage
Volume
265
Copyright Statement
© 2023 Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/36509214
PII: S1053-8119(22)00913-2
Subjects
Brain
COREGISTRATION
HIPPOCAMPAL
Histology
Human
IN-VIVO MRI
Life Sciences & Biomedicine
MRI
Neuroimaging
Neurosciences
Neurosciences & Neurology
OPTIMIZATION
Post-mortem
POSTMORTEM HUMAN BRAINS
QUANTITATIVE VALIDATION
Radiology, Nuclear Medicine & Medical Imaging
RECONSTRUCTION
Registration
ROBUST
Science & Technology
SLICES
TEMPORAL-LOBE
Publication Status
Published
Coverage Spatial
United States
Article Number
119792
Date Publish Online
2022-12-09