Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning
December 6th, 2016
15.4K views
The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.
Overview
This article presents a detailed protocol for accurately aligning magnetic resonance imaging (MRI) volumes with histology sections using customized 3D printed brain holders and slicer boxes. The method, demonstrated with marmoset brain tissue, enables precise radiological-histopathological correlation and can be adapted for brains from other species, including humans. This approach addresses the challenge of matching MRI findings with underlying pathology by improving the consistency and accuracy of brain sectioning for histological analysis.
Key Study Components
Area of Science
- Neuroimaging
- Histology
- 3D Printing and Modeling
- Neuropathology
Background
- MRI is highly sensitive for detecting tissue abnormalities but often cannot distinguish between different pathological processes based solely on imaging.
- In the central nervous system, various pathologies (inflammation, demyelination, axonal damage, gliosis, neuronal death) can produce similar MRI findings.
- Accurate correlation between MRI and histology is essential for understanding underlying pathology.
- Traditional brain sectioning methods are imprecise, complicating MRI-histology comparisons.
Purpose of Study
- To develop and demonstrate a protocol for precise alignment of MRI images with histological sections.
- To create customized 3D printed brain slicers for accurate and reproducible brain sectioning.
- To facilitate improved radiological-histopathological correlation in research and diagnostic settings.
Methods Used
- Fixation of brain tissue with formalin and preparation for MRI scanning.
- Acquisition of high-resolution T2-weighted MRI images.
- Digital image processing: resampling, noise reduction, segmentation, and creation of binary masks.
- 3D modeling of the brain and slicer box using specialized software (Netfabb, Meshmixer, Cura).
- 3D printing of customized brain holders and slicer boxes.
- Sectioning of the brain using the printed slicer box and microtome blades.
- Comparison of MRI, post-mortem MRI, and histology sections for validation.
Main Results
- The protocol enables accurate and consistent alignment between MRI images and histology sections.
- Visual and structural correspondence between in vivo MRI, post-mortem MRI, and histological slabs was achieved.
- Small lesions detected on in vivo MRI could be tracked and validated on post-mortem MRI and histology.
- Some lesions not visible on in vivo MRI were detected on post-mortem MRI and confirmed by histology.
Conclusions
- This methodology allows for precise assessment of the pathology underlying MRI findings.
- The approach is promising for identifying novel biomarkers and understanding specific pathological processes such as inflammation and remyelination.
- The protocol is adaptable to various species and can enhance research in neuroimaging and neuropathology.
Frequently Asked Questions
What is the main advantage of using 3D printed brain slicers in this protocol?
3D printed brain slicers enable precise and reproducible alignment of MRI images with histology sections, improving the accuracy of radiological-histopathological correlation.
Can this protocol be applied to species other than marmosets?
Yes, the protocol can be adapted for use with rodent, non-human primate, and human brains with minor modifications.
How does this method improve upon traditional brain sectioning techniques?
Traditional sectioning is often imprecise, making MRI-histology comparisons difficult. The 3D printed slicer ensures consistent, accurate sectioning that matches MRI planes.
What types of pathological processes can be studied using this approach?
The method is suitable for studying various CNS pathologies, including inflammation, demyelination, axonal damage, gliosis, and neuronal death.
What software tools are used in the protocol?
The protocol utilizes image processing and 3D modeling software such as Netfabb, Meshmixer, and Cura for creating and preparing the brain slicer models for 3D printing.
How are MRI and histology findings validated for correspondence?
After sectioning, visual and structural comparisons are made between in vivo MRI, post-mortem MRI, and histology sections to confirm accurate alignment and lesion detection.
What are potential applications of this methodology?
This approach can be used in research to identify novel biomarkers, study disease mechanisms, and improve diagnostic accuracy in neuropathology.
Executive Industry Relevance
Accurate radiological-histopathological correlation is critical for de-risking CNS target validation and advancing translational biomarker discovery. This protocol leverages 3D printing to precisely align MRI and histology, reducing ambiguity in lesion mapping and supporting confident mechanistic interpretation. The approach enhances predictive value at the interface of imaging and pathology, directly impacting early discovery and preclinical decision points.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables direct mapping of MRI signal changes to underlying pathology for robust target validation.
- Supports mechanistic de-risking by distinguishing between overlapping CNS pathological processes.
- Improves predictive confidence in biomarker identification and disease model fidelity.
Screening & Assay Development
- Facilitates preparation of anatomically accurate tissue sections for downstream quantitative assays.
- Standardizes sectioning to ensure reproducibility and comparability across studies.
- Enables reliable evaluation of imaging-based endpoints in compound screening workflows.
Translational & Preclinical Research
- Aligns imaging and histology outputs to strengthen translational biomarker continuity.
- Supports risk-adjusted advancement by confirming lesion presence and distribution in preclinical models.
- Enhances disease relevance by enabling precise correlation of imaging findings with histopathology.
Pipeline & Workflow Integration
This protocol bridges early discovery imaging with preclinical pathology, supporting workflows from hypothesis testing through lead identification and translational validation.
- Discovery Biology: Provides a platform for hypothesis-driven interrogation of CNS lesions and their biological underpinnings.
- Screening: Delivers reproducible, MRI-aligned tissue sections for quantitative analysis and assay development.
- Analytics: Generates matched datasets for statistical comparison of imaging and histological endpoints.
- Translational Research: Enables continuity of biomarker evaluation from in vivo imaging to ex vivo pathology.
- Enterprise Reuse: Offers a scalable, adaptable workflow for diverse CNS models and species.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in CNS research.
- Operational Value: Standardizes sectioning and imaging correlation for reproducible, scalable studies.
- Strategic Value: Improves go/no-go decisions by clarifying imaging-pathology relationships.
- Portfolio Impact: Supports risk-adjusted prioritization of CNS targets and biomarkers.
Implementation Considerations
- Requires expertise in MRI acquisition, digital image processing, and 3D modeling software.
- Demands access to high-resolution MRI, 3D printers, and compatible slicing hardware.
- Necessitates cross-team standardization of imaging and histology protocols.
- Adaptable to various species and CNS models with protocol modifications.
- Workflow complexity and software proficiency are practical considerations for adoption.
Biopharma FAQs
Why does null hypothesis testing matter for MRI-histology alignment?
Null hypothesis testing enables objective evaluation of whether observed MRI signal changes correspond to true pathological differences, reducing interpretive bias in target validation and biomarker studies.
How does independent variable isolation fit the 3D printed brain slicer workflow?
Isolating variables such as slice orientation and thickness ensures that differences in histology and MRI are attributable to biological factors, not technical inconsistencies, supporting robust discovery-stage analyses.
What do quantitative dependent variable measurements enable in this protocol?
Quantitative measurements of lesion size, location, and correspondence between MRI and histology enable statistical validation of imaging biomarkers and mechanistic hypotheses in CNS research.
Why are replication requirements critical for cross-functional MRI-histology studies?
Replication ensures that MRI-histology correlations are consistent across samples and studies, facilitating reliable data sharing and decision-making between imaging, pathology, and translational teams.
What statistical analysis capabilities are required before implementing MRI-histology correlation?
Teams must be equipped to perform quantitative comparisons, thresholding, and validation of imaging-pathology alignment to support rigorous, portfolio-relevant conclusions in CNS R&D.
Chapters in this video
0:00
Title
0:36
MRI Preparation and Acquisition
1:53
Extracting Brain Surface
4:06
Selecting Slice Location
5:05
Creating MRI Blade Map
5:46
Imaging Brain and Blade Gap Surfaces
7:27
Editing Brain Contours
7:50
Creating the Brain Slicer Box
11:25
Printing the Brain Slicer Box
12:16
Cutting the Brain
13:16
Representative Results
15:14
Conclusion
