Chaoping Zhang1, Dirk Poot2, Bram Coolen1, Hugo Vrenken3, Pierre-Louis Bazin4,5, Birte Forstmann4, and Matthan W.A. Caan1
1Biomedical Engineering & Physics, Amsterdam UMC, Amsterdam, Netherlands, 2Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, Netherlands, 3Radiology, Amsterdam UMC, Amsterdam, Netherlands, 4Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, Netherlands, 5Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
1Biomedical Engineering & Physics, Amsterdam UMC, Amsterdam, Netherlands, 2Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, Netherlands, 3Radiology, Amsterdam UMC, Amsterdam, Netherlands, 4Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, Netherlands, 5Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
We present a unified model embedded in the Recurrent Inference Machine for joint reconstruction and estimation of R2*-maps from subsampled k-space data. Applied to a cohort study, this leads to an improved reconstruction and estimation,increasingly improving for higher acceleration
Left: The quantitative
Recurrent Inference Machine jointly reconstructs and estimates an R2*-map from
sparsely sampled multi-echo gradient echo data. Right: The netwerk architecture
of the quantitative Recurrent Inference Machine is shown with the log-likelihood
function l(M,R2*,B0) depicted on top.
Example R2* images of the
reference image and 12-times accelerated reconstructions using the RIM with
least-squares fit, and the proposed method qRIM with integrated reconstruction
and parameter estimation.
