Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
351-03-013 / 351-03-013
ISMRM Abstract
Spatially-Aware Neural Controlled Differential Equations for IVIM MRI Parameter Estimation in Esophageal Cancer Patients
Primary:
Acquisition & Reconstruction - AI methods
Secondary:
Diffusion - IVIM
351-03-013 · AI Methods
· Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
351-03-013 · AI Methods
· Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
Keywords:Physics-guided deep learningBiomarker estimationIntravoxel incoherent motion (IVIM)
Accepted
Daan Kuppens 1, Roman S Oort1, Stella Mook2, Gert J Meijer2, Oliver J Gurney-Champion1
1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
2Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
Presenting Author: Daan Kuppens
Synopsis
Motivation:
Goals:
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1. S. Barbieri, O.J. Gurney-Champion, R. Klaassen and H.C. Thoeny, "Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI," Magnetic Resonance in Medicine, p. 312– 321, 2020, https://doi.org/10.1002/mrm.27910. [doi]
2. M.P.T. Kaandorp, S. Barbieri, R. Klaassen, H.W.M. van Laarhoven, H. Crezee, P. T. While, A.J. Nederveen and O.J. Gurney-Champion, "Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients," Magnetic Resonance in Medicine, pp. 2250-2265, 2021, https://doi.org/10.1002/mrm.28852. [doi]
3. M.P.T. Kaandorp, F. Zijlstra, D. Karimi, A. Gholipour, P.T. While, “Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI”, Medical Image Analysis 101, 2025, https://doi.org/10.1016/j.media.2024.103414. [doi]
4. S.D. Vasylechko, S.K. Warfield, O. Afacan, S. Kurugol, “Self-supervised IVIM DWI parameter estimation with a physics based forward model”, Magnetic Resonance in Medicine 87, 2025, https://doi.org/10.1002/mrm.28989. [doi]
5. R. Chen, Y. Rubanova, J. Bettencourt and D. Duvenaud, "Neural Ordinary Differential Equations," in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, Canada, 2018
6. P. Kidger, J. Morrill, J. Foster and T. Lyons, "Neural Controlled Differential Equations for Irregular Time Series," in Advances in Neural Information Processing Systems, 2020.
7. D. Kuppens, S. Barbieri, D. van den Berg, P. Schouten, H.C. Thoeny, H.W.M. van Laarhoven, M. Wennen, O.J. Gurney-Champion, “Acquisition-Independennt Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations”, Medical Image Analysis 107, 2025, https://doi.org/10.1016/j.media.2025.103768. [doi]
8. A. Paszke, S. Gross, S. Chintala, G. Chana, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, “Automatic differentiation in PyTorch”, Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017
9. A.S. Borggreve, S. Mook, M. Verheij, V.E.M. Mul, J.J. Bergman, A. Bartels-Rutten, L.C. ter Beek, R.G.H. Beets-Tan, R.J. Bennink, M.I. van Berge Henegouwen, L.A.A. Brosens, I.L. Defize, J.M. van Dieren, H. Dijkstra, R. van Hillegersberg, M.C. Hulshof, H.W.M. van Laarhoven, G.E.H. Lam, A.L.H.M.W. van Lier, C.T. Muijs, W.B. Nagengast, A.J. Nederveen, W. Noordzij, J.T.M. Plukker, P.S.N. van Rossum, J.P. Ruurda, J.W. van Sandick, B.L.A.M. Weusten, F.E.M. Voncken, D. Yakar, G.J. Meijer, “Preoperative image-guided identification of response to neoadjuvant chemoradiotherapy in esophageal cancer (PRIDE): a multicenter observational study”. BMC Cancer 18, 2018, https://doi.org/10.1186/s12885-018-4892-6. [doi]