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Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning
Published on: December 6, 2016
Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem
Istvan N Huszar1, Menuka Pallebage-Gamarallage2, Sarah Bangerter-Christensen3
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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.
Insights
This study introduces an automated pipeline for registering histology sections to MRI data. The method achieves high accuracy, enabling better validation of imaging biomarkers against neuropathology.
Area of Science:
- Neuroimaging
- Histopathology
- Computational Biology
Background:
- Accurate registration of microscopy and MRI data is crucial for validating imaging biomarkers and understanding microstructural MRI signal dependencies.
- Existing registration methods are often manual or require serial sampling, limiting their use with large numbers of histology sections.
- A customizable pipeline is presented to facilitate the registration of standalone histology sections to whole-brain MRI data.
Purpose of the Study:
- To develop and validate a customizable pipeline for registering standalone histology sections to whole-brain MRI data.
- To automate the process of aligning microscopy and MRI data for neuropathology studies.
- To provide a flexible framework adaptable to various microscopy-MRI research.
Main Methods:
- A four-stage registration pipeline was developed using the Tensor Image Registration Library (TIRL).
- The pipeline utilizes block face images and coronal brain slab photographs as intermediaries for registration.
- The method was applied to 87 histology sections from 14 subjects, with accuracy and robustness experiments conducted.
Main Results:
- All 87 histology sections were successfully registered to MRI data.
- Stage 1 (histology-to-block) achieved 0.2-0.4 mm accuracy.
- Stage 2 (block-to-slice) demonstrated robustness with 0.2 mm accuracy.
- Stage 3 (slice-to-volume) achieved sub-voxel accuracy (0.13 mm) and compensated for significant 3D deformations.
- Stage 4 integrated previous stages for refined pixelwise alignment.
Conclusions:
- The open-source pipeline offers robust, automated tools for registering histology sections to MRI data with sub-voxel precision.
- The underlying framework is adaptable for diverse microscopy-MRI studies.
- This method enhances the validation of imaging biomarkers and the analysis of microstructural MRI.
