Quantitative MRI Analysis of the Young Wistar Rat Brain Including Relaxometry and Susceptibility
Poster No:
1744
Submission Type:
Abstract Submission
Authors:
Belal TAVASHI1, Leen Hakki1, Uluç Pamuk1, Oğuzhan Hüraydın1, Esin Öztürk Işık1, Pınar Özbay1
Institutions:
1Boğaziçi University, Istanbul, Türkiye
First Author:
Co-Author(s):
Introduction:
Quantitative MRI (qMRI) provides biomarkers for assessing healthy and abnormal brain conditions. Previous work has quantitatively characterized rat brains, revealing structural and volumetric changes with aging (Gull et al., 2023), reflected in QSM, T1, and T2 maps, which are influenced by water content, microstructure, iron, and myelination (Cho et al., 2022). While T1, T2, R2*, and QSM values have been studied in Sprague Dawley and Long-Evans rats, data for Wistar rats remain limited. For example, (Deruelle et al., 2020) established T1 and T2 reference maps using male Sprague Dawley rats scanned at two centers with 7T Bruker systems, and (Behroozi et al., 2018) explored T1 and T2 values in four adult male Long-Evans rats on a 7T scanner. QSM has been applied in Sprague Dawley rats for myelin volume estimation and traumatic brain injury research (Chary et al., 2021; Cho et al., 2022), while (Johnson et al., 2021) created a high-resolution QSM atlas from six male Wistar rat scans. This study provides a multiparametric analysis of young female Wistar rat brains, addressing this gap in the literature.
Methods:
Eighteen Wistar rats (female, mean age: 74.2 ± 8.2 days; weight: 178.2 ± 11.6 g) were scanned with a 7T preclinical MR scanner (MR Solutions Ltd., UK). Due to technical issues, three rats were scanned later than planned. Motion was minimized with a head holder (bite bar and ear bars) under 1–2% isoflurane anesthesia, with vital signs monitored (respiratory and temperature probes, SA Instruments, USA).
Each animal underwent a 3-hour protocol, including the following sequences: T1w (FSE, TR/TE: 1000/11 ms, FA: 90°, Res: 0.125×0.125×0.8 mm³), T2w (FSE, TR/TE: 2500/40 ms, FA: 90°, Res: 0.125×0.135×0.8 mm³), IR FLASH (TR/TE/TI: 10/4/50 ms, FA: 8°, Res: 0.125×0.25×2 mm³), MEMS (TR/TE: 4000/150 ms, FA: 90°, Res: 0.16×0.31×1 mm³), and MGE (TR: 1620 ms, FA: 60°, Res: 0.36×0.36×0.36 mm³, Min/Max TE: 4/21.12 ms, 9 TEs) with magnitude and phase. Figure 1 summarizes the process.
ROIs were segmented using the SIGMA InVivo template (reference), including white matter, gray matter, ventricles, hippocampus, corpus callosum, and thalamus. T1 maps were generated with MR Solutions (MRS) Preclinical Scan software, producing relaxometry images. T2 maps were created by fitting an exponential decay function to voxel data using Scipy's curve_fit. QSM and R2* maps were processed with the Sepia toolbox (Chan & Marques, 2021). Odd echoes of the phase image were combined using weighted phase increment (WPI) (Özbay et al., 2015), as the scanner acquires both negative and positive echo polarities. Phase unwrapping was performed using the Laplacian operator, followed by V-SHARP with one voxel erosion for background removal (Özbay et al., 2017). STAR-QSM was used for dipole inversion (Wei et al., 2015). R2* maps were calculated using non-linear least square fitting. ROI-based average values were then computed.
Each animal underwent a 3-hour protocol, including the following sequences: T1w (FSE, TR/TE: 1000/11 ms, FA: 90°, Res: 0.125×0.125×0.8 mm³), T2w (FSE, TR/TE: 2500/40 ms, FA: 90°, Res: 0.125×0.135×0.8 mm³), IR FLASH (TR/TE/TI: 10/4/50 ms, FA: 8°, Res: 0.125×0.25×2 mm³), MEMS (TR/TE: 4000/150 ms, FA: 90°, Res: 0.16×0.31×1 mm³), and MGE (TR: 1620 ms, FA: 60°, Res: 0.36×0.36×0.36 mm³, Min/Max TE: 4/21.12 ms, 9 TEs) with magnitude and phase. Figure 1 summarizes the process.
ROIs were segmented using the SIGMA InVivo template (reference), including white matter, gray matter, ventricles, hippocampus, corpus callosum, and thalamus. T1 maps were generated with MR Solutions (MRS) Preclinical Scan software, producing relaxometry images. T2 maps were created by fitting an exponential decay function to voxel data using Scipy's curve_fit. QSM and R2* maps were processed with the Sepia toolbox (Chan & Marques, 2021). Odd echoes of the phase image were combined using weighted phase increment (WPI) (Özbay et al., 2015), as the scanner acquires both negative and positive echo polarities. Phase unwrapping was performed using the Laplacian operator, followed by V-SHARP with one voxel erosion for background removal (Özbay et al., 2017). STAR-QSM was used for dipole inversion (Wei et al., 2015). R2* maps were calculated using non-linear least square fitting. ROI-based average values were then computed.
·Figure 1: Summary of the multiparametric mapping pipeline.
Results:
Figure 2 shows T1, T2, R2*, and QSM plots with example images.
·Figure 2: Mean and standard deviations of quantitative values on the left and exemplary images for QSM (A), R2* (B), T1 (C), and T2 (D) maps on the right.
Conclusions:
QSM results showed greater consistency across subjects compared to other methods. Increased variability was observed in the globus pallidus, likely due to its smaller voxel count and potential segmentation inconsistencies, while all techniques displayed variance in the ventricles. Similar patterns have been reported in the literature (Deruelle et al., 2020), on a smaller scale. Additional time points are required to determine whether this variability is age-related or influenced by other factors. Surprisingly, R2* outliers were not linked to the three older subjects; instead, animals with the highest R2* values also had the lowest weight, suggesting tissue composition as a contributing factor. T1 and T2 values differ from those reported for other rat strains or male Wistar rats, also likely due to the very young age of the subjects. This study provides a foundation for future longitudinal research across age groups.
Acknowledgments: This research is supported by TÜBİTAK 1004 Grant (No. 22AG016).
Acknowledgments: This research is supported by TÜBİTAK 1004 Grant (No. 22AG016).
Lifespan Development:
Early life, Adolescence, Aging 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development 1
Keywords:
Aging
ANIMAL STUDIES
MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Chan, K.-S. (2021). SEPIA—Susceptibility mapping pipeline tool for phase images. NeuroImage, 227, 117611. https://doi.org/10.1016/j.neuroimage.2020.117611
Chary, K., (2021). Quantitative susceptibility mapping of the rat brain after traumatic brain injury. NMR in Biomedicine, 34(2), e4438. https://doi.org/10.1002/nbm.4438
Cho, H., (2022). Quantitative susceptibility mapping and R measurement: Determination of the myelin volume fraction in the aging ex vivo rat corpus callosum. NMR in Biomedicine, 35(3), e4645. https://doi.org/10.1002/nbm.4645
Deruelle, T., (2020). A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps. Frontiers in Neuroinformatics, 14, 22. https://doi.org/10.3389/fninf.2020.00022
Gull, S., (2023). Brain Macro-Structural Alterations in Aging Rats: A Longitudinal Lifetime Approach. Cells, 12(3), Article 3. https://doi.org/10.3390/cells12030432
Johnson, G. A., (2021). A multicontrast MR atlas of the Wistar rat brain. NeuroImage, 242, 118470. https://doi.org/10.1016/j.neuroimage.2021.118470
Özbay, P. S., (2017). A comprehensive numerical analysis of background phase correction with V-SHARP. NMR in Biomedicine, 30(4). https://doi.org/10.1002/nbm.3550
Özbay, P. S., (2015). Effect of respiratory hyperoxic challenge on magnetic susceptibility in human brain assessed by quantitative susceptibility mapping (QSM). NMR in Biomedicine, 28(12), 1688–1696. https://doi.org/10.1002/nbm.3433
Wei, H., (2015). Streaking Artifact Reduction for Quantitative Susceptibility Mapping of Sources with Large Dynamic Range. NMR in Biomedicine, 28(10), 1294. https://doi.org/10.1002/nbm.3383