Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
567-06-013 ISMRM Abstract

Is bigger always better: Does increasing network capacity mitigate degeneracy in self-supervised qMRI parameter estimation?

Accepted
Giulio V Minore 1,2, Timothy J Bray1,3,4, Gary Zhang1,5
1Hawkes Institute, University College London, London, United Kingdom
2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
3Centre for Medical Imaging, University College London, London, United Kingdom
4Department of Imaging, University College London Hospital, London, United Kingdom
5Department of Computer Science, University College London, London, United Kingdom
Presenting Author: Giulio V Minore

Synopsis

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References

1. Barbieri, S., Gurney-Champion, O.J., Klaassen, R., Thoeny, H.C.: Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magnetic resonance in medicine 83(1), 312–321 (2020), doi:10.1002/mrm.27910 [doi]
2. Kaandorp, M.P.T., Barbieri, S., Klaassen, R., Laarhoven, H.W.M., Crezee, H., While, P.T., Nederveen, A.J., Gurney-Champion, O.J.: Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic resonance in medicine 86(4), 2250–2265 (2021), doi:10.1002/mrm.28852 [doi]
3. Grussu, F., Battiston, M., Palombo, M., Schneider, T., Wheeler-Kingshott, C.A.M.G., Alexander, D.C.: Deep learning model fitting for diffusion-relaxometry: A comparative study. In: Gyori, N., Hutter, J., Nath, V., Palombo, M., Pizzolato, M., Zhang, F. (eds.) Computational Diffusion MRI. pp. 159–172. Springer International Publishing, Cham (2021), doi:10.1007/978-3-030-73018-5_13 [doi]
4. Sen, S., Singh, S., Pye, H., Moore, C.M., Whitaker, H.C., Punwani, S., Atkinson, D., Panagiotaki, E., Slator, P.J.: ssVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization. Magnetic resonance in medicine 92(5), 2181–2192 (2024), doi:10.1002/mrm.30186 [doi]
5. Gyori, N.G., Palombo, M., Clark, C.A., Zhang, H., Alexander, D.C.: Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magnetic resonance in medicine 87(2), 932–947 (2022), doi:10.1002/mrm.29014 [doi]
6. Minore, G.V., Dwyer-Hemmings, L., Bray, T.J.P., Zhang, H.: Resolving quantitative MRI model degeneracy in self-supervised machine learning. In: Oguz, I., Zhang, S., Metaxas, D.N. (eds.) Information Processing in Medical Imaging. pp. 186–199. Springer Nature Switzerland, Cham (2025), doi:10.1007/978-3-031-96625-5_13 [doi]
7. Bishop, C.M., Roach, C.M.: Fast curve fitting using neural networks. Review of scientific instruments 63(10), 4450–4456 (1992), doi:10.1063/1.1143696 [doi]
8. Bray, T.J., Minore, G.V., Bainbridge, A., Dwyer-Hemmings, L., Taylor, S.A., Hall-Craggs, M.A., Zhang, H.: RAIDER: Rapid, anatomy-independent, deep learning-based PDFF and R2* estimation using magnitude-only signals, dual neural networks and training data distribution design. Machine Learning for Biomedical Imaging 3, 521–544 (2025), https://doi.org/10.59275/j.melba.2025-bac4 [doi]
9. Starekova, J., Hernando, D., Pickhardt, P.J., Reeder, S.B.: Quantification of liver fat content with CT and MRI: State of the art. Radiology 301(2), 250–262 (2021), doi:10.1148/radiol.2021204288 [doi]
10. Yoon, J.H., Lee, J.M., Lee, K.B., Kim, S.W., Kang, M.J., Jang, J.Y., Kannengiesser, S., Han, J.K., Choi, B.I.: Pancreatic steatosis and fibrosis: Quantitative assessment with preoperative multiparametric MR imaging. Radiology 279(1), 140–150 (2016), doi:10.1148/radiol.2015142254 [doi]
11. Bray, T.J.P., Bainbridge, A., Punwani, S., Ioannou, Y., Hall-Craggs, M.A.: Simultaneous quantification of bone edema/adiposity and structure in inflamed bone using chemical shift-encoded MRI in spondyloarthritis. Magnetic resonance in medicine 79(2), 1031–1042 (2018), doi:10.1002/mrm.26729 [doi]
12. Latifoltojar, A., Hall-Craggs, M., Bainbridge, A., Rabin, N., Popat, R., Rismani, A., D’Sa, S., Dikaios, N., Sokolska, M., Antonelli, M., Ourselin, S., Yong, K., Taylor, S.A., Halligan, S., Punwani, S.: Whole-body MRI quantitative biomarkers are associated significantly with treatment response in patients with newly diagnosed symptomatic multiple myeloma following bortezomib induction. European radiology 27(12), 5325–5336 (2017), doi:10.1007/s00330-017-4907-8 [doi]
13. Morrow, Jasper M, F., Sinclair, Christopher D J, P., Fischmann, Arne, M., Machado, Pedro M, M., Reilly, Mary M, P., Yousry, Tarek A, P., Thornton, John S, P., Hanna, Michael G, P.: MRI biomarker assessment of neuromuscular disease progression: a prospective observational cohort study. Lancet neurology 15(1), 65–77 (2016), doi:10.1016/S1474-4422(15)00242-2 [doi]
14. Reeder, S.B., Yokoo, T., França, M., Hernando, D., Alberich-Bayarri, A., Alústiza, J.M., Gandon, Y., Henninger, B., Hillenbrand, C., Jhaveri, K., Karçaaltıncaba, M., Kühn, J.P., Mojtahed, A., Serai, S.D., Ward, R., Wood, J.C., Yamamura, J., Martí-Bonmatí, L.: Quantification of liver iron overload with MRI: Review and guidelines from the ESGAR and SAR. Radiology 307(1), e221856–e221856 (2023), doi:10.1148/radiol.221856 [doi]
15. Triay Bagur, A., Hutton, C., Irving, B., Gyngell, M.L., Robson, M.D., Brady, M.: Magnitude-intrinsic water–fat ambiguity can be resolved with multipeak fat modeling and a multipoint search method. Magnetic resonance in medicine 82(1), 460–475 (2019), doi:10.1002/mrm.27728 [doi]
16. Bray, T.J.P., Bainbridge, A., Lim, E., Hall-Craggs, M.A., Zhang, H.: MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modeling. Magnetic resonance in medicine 89(3), 1173–1192 (2023), doi:10.1002/mrm.29493 [doi]
17. Jelescu, I.O., Veraart, J., Fieremans, E., Novikov, D.S.: Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR in biomedicine 29(1), 33–47 (2016), doi:10.1002/nbm.3450 [doi]

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