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]