Novel Methods to Achieve the Highest Resolution in-vivo Child Brain MRI
Poster No:
1017
Submission Type:
Abstract Submission
Authors:
Brooklyn Wright1, Steve Kassem2, Mark Schira3, George Paxinos4
Institutions:
1University of New South Wales, Sydney, New South Wales, 2Neuroscience Research Australia, Sydney, New Sotuh Wales, 3University of Wollongong, Wollongong, NSW, 4Neuroscience Research Australia, Sydney, NSW
First Author:
Co-Author(s):
Introduction:
Neurological development is an extraordinarily complex and dynamic process in which brain structure, function, and connectivity evolves throughout the first three decades of life (Sowell et al., 2002; van Blooijs et al., 2023). Despite recent quality and resolution advancements in MRI, there is no high-quality paediatric data. It is assumed that children have lower participant compliance and increased motion artefacts, which would impact data quality in longer & high-resolution MRI acquisition methods (Greene et al., 2016; Makowski et al., 2019). Our research employs tailored structural 3T MRI acquisition and processing protocols, adapted from the Human Brain Atlas project (Schira et al., 2023), to deliver high-resolution whole-brain paediatric images of unrivalled quality.
Methods:
This study employed brief (~8-minute) structural MRI acquisition protocols with a resolution of 0.65mm isotropic. Four T1-weighted (T1-w) and four T2-weighted (T2-w) scans were acquired for each of two female participants, aged 9 and 12. The use of multiple shorter scans allowed for breaks between sessions vital for improving participant compliance. MRI data processing was conducted using Neurodesk (Renton et al., 2024), emphasising reproducibility and accessibility through open-source tools. For each participant, the individual T1-w and T2-w scans were averaged to create single-subject T1-w and T2-w templates. Initial templates were generated using MRtrix3 (Tournier et al., 2019), and then a modified multivariate template construction script from ANTs (Avants et al., 2011) was applied to further enhance image quality permitting final image upsampling to 0.5mm isotropic. All processing methods and scripts are openly available at https://osf.io/ckh5t/ to promote transparency and reproducibility in neuroimaging and brain mapping research.
Results:
Applying template generation methods to single participant datasets reduces head-motion artefacts, reveals structural details and facilitates up-sampling to a voxel size of 0.5mm isotropic in the final data. Figure 1 compares outputs from our method to the Haskins child atlas (Molfese et al., 2021), illustrating the marked improvement in structure visibility and an 8-fold increase in resolution we achieve. This improvement in clarity permits the visualisation of fine structural boundaries essential for accurate, comprehensive neuroanatomical delineation.
Conclusions:
To our best knowledge this represents the highest resolution paediatric MRI dataset by a significant margin. It permits the investigation of all cortical and subcortical structures, including complex regions such as the hippocampal subfields which can only be sufficiently visualised at high resolution (Wisse et al., 2021). Our methods, tailored to the child brain, are key to advancing paediatric clinical interventions and facilitating the investigation of child brain anatomy directly, instead of deduction from adult anatomy.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Normal Development
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Atlasing
Computational Neuroscience
Data analysis
Development
MRI
NORMAL HUMAN
Open-Source Code
PEDIATRIC
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.
Greene, D. J., Black, K. J., & Schlaggar, B. L. (2016). Considerations for MRI study design and implementation in pediatric and clinical populations. Developmental Cognitive Neuroscience, 18, 101–112. https://doi.org/10.1016/j.dcn.2015.12.005
Makowski, C., Lepage, M., & Evans, A. C. (2019). Head motion: The dirty little secret of neuroimaging in psychiatry. In Journal of Psychiatry and Neuroscience (Vol. 44, Issue 1, pp. 62–68). Canadian Medical Association. https://doi.org/10.1503/jpn.180022
Molfese, P. J., Glen, D., Mesite, L., Cox, R. W., Hoeft, F., Frost, S. J., Mencl, W. E., Pugh, K. R., & Bandettini, P. A. (2021). The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas. Pediatric Radiology, 51(4), 628–639. https://doi.org/10.1007/s00247-020-04875-y
Schira, M. M., Isherwood, Z. J., Kassem, M. S., Barth, M., Shaw, T. B., Roberts, M. M., & Paxinos, G. (2023). HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations. Brain Structure and Function. https://doi.org/10.1007/s00429-023-02653-8
Sowell, E. R., Trauner, D. A., Gamst, A., & Jernigan, T. L. (2002). Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Developmental medicine and child neurology, 44(1), 4–16. https://doi.org/10.1017/s0012162201001591
Tournier, J.-D., Smith, R. E., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible, and open software framework for medical image processing and visualization. NeuroImage, 202, 116–137.
Van Blooijs, D., van den Boom, M. A., van der Aar, J. F., Huiskamp, G. M., Castegnaro, G., Demuru, M., Zweiphenning, W. J. E. M., van Eijsden, P., Miller, K. J., Leijten, F. S. S., & Hermes, D. (2023). Developmental trajectory of transmission speed in the human brain. Nature Neuroscience, 26(4), 537–541. https://doi.org/10.1038/s41593-023-01272-0
Wisse, L. E. M., Chételat, G., Daugherty, A. M., de Flores, R., la Joie, R., Mueller, S. G., Stark, C. E. L., Wang, L., Yushkevich, P. A., Berron, D., Raz, N., Bakker, A., Olsen, R. K., & Carr, V. A. (2021). Hippocampal subfield volumetry from structural isotropic 1 mm3 MRI scans: A note of caution. Human Brain M