Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-05-001 ISMRM Abstract

Predicting sequence-induced variability in T1-weighted image-derived phenotypes using physics-based synthetic MRI

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Analysis Methods - Image Synthesis and Translation
564-05-001 · The qMRI Variety Show: A Little Bit of Everything · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row E
Keywords: Quantitative synthetic MRI UK Biobank Physics-based synthesis Data harmonisation
Accepted
Hongyan Liu 1, Nikos Priovoulos1, Somtochukwu Ibeme1, Karla L Miller1, Aaron T Hess1
1Oxford Centre for Integrative Neuroimaging (OXCIN), University of Oxford, Oxford, United Kingdom
Presenting Author: Hongyan Liu

Synopsis

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References

1. Miller, K., Alfaro-Almagro, F., Bangerter, N. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19, 1523–1536 (2016). https://doi.org/10.1038/nn.4393 [doi]
2. Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P. F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage, 49(2), 1271-1281. https://doi.org/10.1016/j.neuroimage.2009.10.002 [doi]
3. Nöth, U., Hattingen, E., Bähr, O., Tichy, J., & Deichmann, R. (2015). Improved visibility of brain tumors in synthetic MP‐RAGE anatomies with pure T1 weighting. NMR in Biomedicine, 28(7), 818-830. https://doi.org/10.1002/nbm.3324 [doi]
4. Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., ... & Dale, A. M. (2018). The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Developmental cognitive neuroscience, 32, 43-54. https://doi.org/10.1016/j.dcn.2018.03.001 [doi]
5. Weber, C. J., Carrillo, M. C., Jagust, W., Jack Jr, C. R., Shaw, L. M., Trojanowski, J. Q., ... & Weiner, M. W. (2021). The worldwide Alzheimer's disease neuroimaging initiative: ADNI‐3 updates and global perspectives. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 7(1), e12226. https://doi.org/10.1002/trc2.12226 [doi]
6. Tian, Q., Zaretskaya, N., Fan, Q., Ngamsombat, C., Bilgic, B., Polimeni, J. R., & Huang, S. Y. (2021). Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising. NeuroImage, 233, 117946. https://doi.org/10.1016/j.neuroimage.2021.117946 [doi]
7. Warrington, S., Ntata, A., Mougin, O., Campbell, J., Torchi, A., Craig, M., ... & Sotiropoulos, S. N. (2023). A resource for development and comparison of multimodal brain 3 T MRI harmonisation approaches. Imaging Neuroscience, 1, 1-27. https://doi.org/10.1162/imag_a_00042 [doi]
8. Weigel, M. (2015). Extended phase graphs: dephasing, RF pulses, and echoes‐pure and simple. Journal of Magnetic Resonance Imaging, 41(2), 266-295. https://doi.org/10.1002/jmri.24619 [doi]
9. Kent, J. L., Dragonu, I., Valkovič, L., & Hess, A. T. (2023). Rapid 3D absolute B1+ mapping using a sandwiched train presaturated TurboFLASH sequence at 7 T for the brain and heart. Magnetic Resonance in Medicine, 89(3), 964-976. https://doi.org/10.1002/mrm.29497 [doi]
10. Robson, P. M., Grant, A. K., Madhuranthakam, A. J., Lattanzi, R., Sodickson, D. K., & McKenzie, C. A. (2008). Comprehensive quantification of signal‐to‐noise ratio and g‐factor for image‐based and k‐space‐based parallel imaging reconstructions. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 60(4), 895-907. https://doi.org/10.1002/mrm.21728 [doi]
11. Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L., Griffanti, L., Douaud, G., ... & Smith, S. M. (2018). Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage, 166, 400-424. https://doi.org/10.1016/j.neuroimage.2017.10.034 [doi]
12. Bhalerao, G., Markiewicz, P., Thomas, D., De Vita, E., Parkes, L., Thompson, G., MacKwen, J., Krokos, G., Wimberley, C., William, H., Su, L., Smith, S., Malhotra, P., Hoggard, N., Taylor, J.-P., Ritchie, C., Wardlaw, J., Matthews, P., Aigbirhio, F., O’Brien, J., Hammers, A., Fox, N., Herholz, K., Barkhof, F., Miller, K., Matthews, J., & Griffanti, L. (2025, June). Exploring brain MRI variability and harmonisation approaches in the DPUK PET–MR dataset. In Aperture Neuro (Ed.), OHBM 2025 Annual Meeting Abstract Book (Brisbane, Australia). https://doi.org/10.5281/zenodo.15641972 [doi]
13. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782-790. https://doi.org/10.1016/j.neuroimage.2011.09.015 [doi]
14. Masoud, M., Hu, F., & Plis, S. (2023). Brainchop: In-browser MRI volumetric segmentation and rendering. Journal of Open Source Software, 8(83), 5098. https://doi.org/10.21105/joss.05098 [doi]

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http://echo.ismrm.org/p/ISMRM2026/564-05-001