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http://echo.ismrm.org/p/ISMRM2025/1045
1045
ISMRM Abstract
Retrospective relaxometry from conventional contrasts by physics-informed deep learning: A pilot on Tumor, MS, Stroke and Epilepsy patients
Accepted
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1. Jacobs L, Mandija S, Liu H, van den Berg CAT, Sbrizzi A, Maspero M. Generalizable synthetic MRI with physics-informed convolutional networks. Med Phys. 2024; 51: 3348–3359. https://doi.org/10.1002/mp.16884 2. Qiu S, Wang L, Sati P, Christodoulou AG, Xie Y, Li D. Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma. Magn Reson Med. 2024; 92: 2683-2695. doi: 10.1002/mrm.30226 3. Qui S, Xie Y, Christodoulou AG, Sati P, Maya M, Sicotte NL, Li D. Protocol-aware unsupervised retrospective T1 and T2 mapping with diverse imaging parameters . In Proc ISMRM , 2024. pp. [doi]
2. Qiu S, Chen Y, Ma S, Fan Z, Moser FG, Maya MM, Christodoulou AG, Xie Y, Li D. Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning. Magn Reson Med. 2022 Jan;87(1):488-495. doi: 10.1002/mrm.28962. [doi]
3. Moya-Sáez E, Peña-Nogales Ó, de Luis-García R, et al. A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data. Comput Methods Programs Biomed. 2021;210:106371. doi: https://doi.org/10.1016/j.cmpb.2021.106371.
4. Wu Y, Ma Y, Kee Y, et al. Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning. arXiv preprint arXiv:2108.04912. 2021 Aug 10.
5. Varadarajan D, Bouman KL, van der Kouwe A, Fischl B, Dalca AV. Unsupervised learning of MRI tissue properties using MRI physics models. arXiv preprint arXiv:2107.02704. 2021 Jul 6.
6. Kleinloog, J.P.D., Mandija, S., D'Agata, F., Liu, H., van der Heide, O., Koktas, B., Dankbaar, J.W., Keil, V.C., Vonken, E.-J., Jacobs, S.M., van den Berg, C.A.T., Hendrikse, J., van der Kolk, A.G. and Sbrizzi, A. (2023), Synthetic MRI with Magnetic Resonance Spin TomogrAphy in Time-Domain (MR-STAT): Results from a Prospective Cross-Sectional Clinical Trial. J Magn Reson Imaging, 57: 1451-1461. https://doi.org/10.1002/jmri.28425 9. Warntjes, J.B.M., Leinhard, O.D., West, J. and Lundberg, P. (2008), Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magn. Reson. Med ., 60: 320-329. https://doi.org/10.1002/mrm.21635 10. Manjón JV, Romero JE, Vivo-Hernando R, Rubio G, Aparici F, de la Iglesia-Vaya M, Coupé P. vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis. Front Neuroinform . 2022 May 24;16:862805. doi: 10.3389/fninf.2022.862805. [doi]
7. McCarthy P. FSLeyes. Published online June 13, 2023. doi:10.5281/ZENODO.8033457 12. Bojorquez JZ, Bricq S, Acquitter C, Brunotte F, Walker PM, Lalande A. What are normal relaxation times of tissues at 3 T? Magn Reson Imaging . 2017 Jan;35:69-80. doi: 10.1016/j.mri.2016.08.021 13. Wansapura, J.P., Holland, S.K., Dunn, R.S. and Ball, W.S., Jr. (1999), NMR relaxation times in the human brain at 3.0 tesla†. J. Magn. Reson. Imaging , 9: 531-538. https://doi.org/10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L 14. Stanisz GJ, Odrobina EE, Pun J, Escaravage M, Graham SJ, Bronskill MJ, Henkelman RM. T1, T2 relaxation and magnetization transfer in tissue at 3T. Magn Reson Med . 2005 Sep;54(3):507-12. doi: 10.1002/mrm.20605. [doi]