May 14, 2026

In Vivo T1 Mapping of Blood-Brain Barrier Leakage in Mouse Brain Using Gadolinium Enhanced RARE-VTR Magnetic Resonance Imaging (MRI) V.2

DOI
https://dx.doi.org/10.17504/protocols.io.bp2l6j5nkvqe/v2
  • 1Department of neurology and neurosurgery, Faculty of Medicine, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada;
  • 2Department of pharmacology and physiology, Faculty of Medicine, Université de Montréal;
  • 3Neural Signaling and Circuitry research group (SNC);
  • 4Center for Interdisciplinary Research on the Brain and Learning (CIRCA);
  • 5Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815;
  • 6Department of neuroscience and physiology, Faculty of Medicine, Université de Montréal;
  • 7Institut Courtois d’innovation biomédicale
Amandine Even
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DOI: https://dx.doi.org/10.17504/protocols.io.bp2l6j5nkvqe/v2
Protocol Citation: Vladimir Grouza, Sriparna Mukherjee, Louis-Éric Trudeau, David Rudko 2026. In Vivo T1 Mapping of Blood-Brain Barrier Leakage in Mouse Brain Using Gadolinium Enhanced RARE-VTR Magnetic Resonance Imaging (MRI). protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6j5nkvqe/v2Version created by Amandine Even
License: This is an open access  protocol  distributed under the terms of the  Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 13, 2026
Last Modified: May 14, 2026
Protocol  Integer ID: 317021
Keywords: vivo assessment of blood brain barrier, vivo t1 mapping of blood, blood brain barrier, brain barrier leakage in mouse brain, vivo t1 mapping, brain barrier leakage, preclinical mri system, vtr magnetic resonnance imaging, mri, permeability in mouse brain, based brain extraction, brain extraction, enhanced t1 mapping, vivo assessment, quality control of contrast delivery, contrast delivery, using gadolinium, vein gadolinium administration, level quantification of contrast, downstream image processing, using gadolinium enhanced rare, downstream image processing for regional analysis, mouse brain, gadolinium enhanced rare, associated t1 shift, vtr magnetic resonance imaging
Funders Acknowledgements:
Aligning Science Across Parkinson's
Grant ID: ASAP-000525
Abstract
This protocol describes in vivo assessment of blood brain barrier (BBB) permeability in mouse brain using gadolinium-enhanced T1 mapping on a Bruker Pharmascan 7T preclinical MRI system. The workflow includes animal preparation, tail-vein gadolinium administration, pre- and post-contrast RARE-VTR acquisition, quality control of contrast delivery, and downstream image processing for regional analysis of T1 shortening. Image analysis consists of motion correction, denoising, Gibbs-ringing correction, SHERM-based brain extraction, bias-field correction, atlas registration, monoexponential T1 fitting, and ROI-level quantification of contrast-associated T1 shift using Earth Mover’s Distance.
Image Attribution
Figure 1. Representative coronal RARE-VTR images acquired before and after GBCA injection. Figure 2. Overview of the image-processing workflow for T1 mapping and regional brain delineation. Figure 3. T1 value distributions for each ROI obtained using nonlinear least square fitting.
Materials
**Animals**
- Mice assigned according to the experimental design.

**MRI and monitoring**
- Bruker Pharmascan preclinical 7T MRI scanner
- Appropriate mouse brain RF coil setup
- Isoflurane anesthesia system
- Oxygen gas supply
- Heating system for maintaining body temperature
- Respiration and temperature monitoring system

**Contrast and injection supplies**
- Gadolinium-based contrast agent
- Sterile saline
- Tail-vein injection catheter: Anicath Non-Winged IV Cannulae 26g (Millpledge, MP06127)

**Software for image reconstruction and analysis**
- DiPy v1.4.0 (Python 3.8)
- MATLAB R2019b or compatible version
- N3 algorithm in ANT software
- SHERM brain extraction workflow
- Atlas/template at 40 × 40 × 40 µm resolution
- Custom scripts for T1 fitting and ROI analysis
Before start
- All animal procedures must be approved by the appropriate institutional animal care committee and conducted in accordance with institutional guidelines.
- Ensure that the isoflurane induction chamber is properly assembled and functioning correctly before placing the animal inside.
- Prepare the saline solution and gadolinium dilutions in advance.
Prepare the mouse for MRI
Note the body weight of each mouse before induction.
Induce and maintain anesthesia with isoflurane during MRI acquisition.
Anesthesia should be maintained at 2-3% isoflurane.
Apply eye gel on the eyes to prevent corneal damage.
Clean the tail vein with 70% ethanol and insert the catheter in the tail vein.
Secure the catheter so that there is no blood loss.
Position the mouse securely in the MRI machine and maintain physiological stability throughout imaging.
Body temperature and respiration should be monitored continuously.
Acquire pre-contrast RARE-VTR images
Acquire the full pre-contrast RARE-VTR dataset using the TR series: 8000 , 3600, 2400, 1480, 940, 650, and 501.1 ms.
Use the same geometry for all animals and ensure consistent slice placement across mice.
Administer gadolinium contrast agent
The contrast agent should be diluted in saline before administration.

Inject gadolinium based contract agent (GBCA) via the tail vein at 0.4 Mass Percent .
Additionally inject 500 µL saline intraperitoneally to keep the mouse hydrated.
Verify successful contrast delivery
Immediately after GBCA administration, perform a rapid QC scan, such as a single RARE acquisition, and confirm visible post-contrast hyperintensity in soft tissue.
Acquire post-contrast RARE-VTR images
Repeat the full RARE-VTR acquisition after contrast administration using the same geometry and TR values as the pre-contrast scan.
Minimize motion and avoid any repositioning between pre- and post-contrast acquisitions.
Data processing
Reconstruct images:
Reconstruct the raw MRI data offline using the reconstruction workflow based on Bruker2Nifti.
Perform motion correction:
For each RARE-VTR dataset, compute a rigid-body transformation between the TR = 8000 ms image and each of the other TR volumes.
Apply motion correction to align all volumes within each 4D dataset.
Denoise and correct ringing artifact:
Apply Marcenko-Pastur principal component analysis denoising and Gibbs-ringing correction using DiPy v1.4.0.
Extract the brain:
Segment the brain from skull and surrounding tissue using the SHERM algorithm implemented in MATLAB (Version R2019b).
Apply bias-field correction:
Perform bias-field correction on the brain-masked TR = 8000 ms image using the N3 algorithm in Advanced Normalization Tools (ANTs). Apply the estimated field to the remaining volumes in the dataset.
Register images to an atlas:
Register the extracted and corrected mouse brain image to a high-resolution anatomical template at 40 × 40 × 40 µm using affine registration with 12 degrees of freedom.
Then invert the transformation and project the atlas labels back into the native RARE-VTR space for ROI parcellation.
Use nearest-neighbor interpolation for labels.
Fit voxel-wise T1 maps:
Fit the monoexponential saturation-recovery model voxel-wise using a nonlinear least-squares solver in MATLAB.
Generate whole-brain pre- and post-contrast T1 maps for each specimen.
Quantify regional T1 shift:
Within each ROI, generate histograms of T1 values from the pre-contrast and post-contrast maps. Quantify the magnitude of contrast-associated T1 shortening using Earth Mover’s Distance between the paired histograms.
Figure 1. Representative coronal RARE-VTR images acquired before and after GBCA injection. The RARE-VTR sequence acquires T1-weighted images at multiple repetition times to sample the saturation recovery curve in each voxel for T1 mapping. Successful administration of the GBCA is indicated by visible post-contrast hyperintensity in soft tissues.

Figure 2. Overview of the image-processing workflow for T1 mapping and regional brain delineation. This figure presents the main steps of the image-processing pipeline used for T1 mapping and region-of-interest (ROI) analysis. The images are shown in three anatomical orientations: coronal (left), axial (middle), and sagittal (right). Panel (i) shows the TR = 8000 ms image after correction of imaging artefacts. Panel (ii) shows the brain mask generated using the SHERM-based segmentation method. Panel (iii) shows the atlas labels transformed back onto the native TR = 8000 ms image using the inverse affine transformation. Each color corresponds to a distinct anatomical ROI used for downstream segmentation and analysis.


Figure 3. T1 value distributions for each ROI obtained using nonlinear least square fitting.

Troubleshooting
ABC
Problem Possible cause Solution
No visible post-contrast soft tissue hyperintensity Failed tail-vein injection Verify needle placement and injection integrity. There might be blood clot at the injection site due to which the delivery of contrast agent failed.
Excess motion between TR volumes or between pre- and post-contrast scans Inadequate animal positioning or unstable anesthesia Stabilize the mouse carefully during acquisition and check the oxygen/isoflurane supply.
Protocol references
1. Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394-406.
2. Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016;76(5):1574-81.
3. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, et al. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform. 2014;8:8.
4. Liu Y, Unsal HS, Tao Y, Zhang N. Automatic Brain Extraction for Rodent MRI Images. Neuroinformatics. 2020;18(3):395-406.
5. Dorr AE, Lerch JP, Spring S, Kabani N, Henkelman RM. High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. Neuroimage. 2008;42(1):60-9.
6. Rubner Y, Tomasi C, Guibas LJ. The Earth Mover's Distance as a Metric for Image Retrieval. International Journal of Computer Vision. 2000;40(2):99-121.