@conference {2478,
	title = {Direct deformation estimation with recurrent inference machines for longitudinal MRI},
	year = {2026},
	publisher = {International Society for Magnetic Resonance in Medicine},
	abstract = {Recently, Delta-MRI was proposed to estimate longitudinal anatomical changes from accelerated follow-up MRI scans. Delta-MRI, however, suffers from long computation times and requires user-defined regularisation. To develop a time-efficient Delta-MRI framework without user-defined regularisation. Delta-MRI is augmented with recurrent inference machines (RIMs) that incorporate a physics-based forward model and learn to solve the inverse problem of estimating anatomical changes directly from a reference image and undersampled follow-up scans, while jointly learning an implicit prior. RIM-augmented Delta-MRI outperforms conventional Delta-MRI in terms of follow-up image quality (PSNR gain: 2 dB), reconstruction speed (100x), and user-friendliness (no user-defined regularisation).},
	author = {Jonas Maes and Jana Osstyn and Matthan Caan and Arnold Jan den Dekker and Jan Sijbers}
}
@article {2190,
	title = {Recurrent Inference Machines as inverse problem solvers for MR relaxometry},
	journal = {Medical Image Analysis},
	volume = {74},
	year = {2021},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.media.2021.102220},
	author = {Emanoel Ribeiro Sabidussi and Stefan Klein and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
@inproceedings {2169,
	title = {Recurrent Inference Machines as Inverse Problem Solvers for MR Relaxometry},
	booktitle = { MIDL 2021 - Medical Imaging with Deep Learning},
	year = {2021},
	author = {Emanoel Ribeiro Sabidussi and Matthan Caan and Shabab Bazrafkan and Arnold Jan den Dekker and Jan Sijbers and Wiro J Niessen and Dirk H J Poot}
}
