Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-01-002 ISMRM Abstract

Direct deformation estimation with recurrent inference machines for longitudinal MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Image Registration and Alignment
368-01-002 · Registration, Atlases, and Motion · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row I
Keywords: Longitudinal MRI Accelerated MRI reconstruction Deformable image registration Recurrent inference machines
Accepted
Jonas Maes 1,2, Jana Osstyn1,2, Matthan W Caan3, Arnold J den Dekker1,2, Jan Sijbers1,2
1imec - Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
2μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
3Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
Presenting Author: Jonas Maes

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References

1. Renders J, Shafieizargar B, Verhoye M, De Beenhouwer J, den Dekker AJ, Sijbers J. Delta-MRI: Direct Deformation Estimation from Longitudinally Acquired K-Space Data. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). 2023:1-4. doi:10.1109/ISBI53787.2023.10230697 [doi]
2. Lønning K, Putzky P, Sonke JJ, Reneman L, Caan M, Welling M. Recurrent inference machines for reconstructing heterogeneous MRI data. Medical Image Analysis. 2019;53:64-78. doi:10.1016/j.media.2019.01.005 [doi]
3. Hassan S, Akaila D, Arjemandi M, Papineni V, Yaqub M. MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study. Scientific Reports. 2025;15(1). doi:10.1038/s41598-025-97674-0 [doi]
4. Safari M, Eidex Z, Chang CW, Qiu RLJ, Yang X. Advancing MRI reconstruction: A systematic review of deep learning and compressed sensing integration. Biomedical Signal Processing and Control. 2026;111:108291. doi:10.1016/j.bspc.2025.108291 [doi]
5. Germann J, Gouveia FV, Chakravarty MM, Devenyi GA. Longitudinal deformation-based morphometry pipeline to study neuroanatomical differences in structural MRI based on SyN unbiased templates. Aperture Neuro. 2025;5. doi:10.52294/001c.133510 [doi]
6. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine. 2007;58(6):1182-1195. doi:10.1002/mrm.21391 [doi]
7. Knoll F, Zbontar J, Sriram A, et al. FastMRI: a publicly available raw K-Space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiology Artificial Intelligence. 2020;2(1):e190007. doi:10.1148/ryai.2020190007 [doi]
8. Kingma DP, Ba LJ. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR). 2015.

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http://echo.ismrm.org/p/ISMRM2026/368-01-002