8 citations · 8 across the 4 of their papers we have counts for
5 papers
Recurrent Inference Machines as inverse problem solvers for MR relaxometry
E. R. Sabidussi, S. Klein, M. W. A. Caan +5
In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference proc…
Time efficiency analysis for undersampled quantitative MRI acquisitions
Riwaj Byanju, Stefan Klein, Alexandra Cristobal-Huerta +2
To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acc…
An Efficient Method for Multi-Parameter Mapping in Quantitative MRI using B-Spline Interpolation
Willem van Valenberg, Stefan Klein, Frans M. Vos +3
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equat…
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Chaoping Zhang, Florian Dubost, Marleen de Bruijne +2
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application a…
Diffusion MRI microstructure models with in vivo human brain Connectom data: results from a multi-group comparison
Uran Ferizi, Benoit Scherrer, Torben Schneider +20
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the…