@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}
}
