Compressed sensing reconstruction for magnetic resonance parameter mapping

By M. Doneva, P. Börnert, H. Eggers, C. Stehning, J. Sénégas, Alfred Mertins. Magnetic Resonance in Medicine, 2010

367 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, background): “, 2007 (Doneva et al., 2010; Landman et al., 2010, 2012; Menzel et al., 2011;Michailovich and Rathi, 2010;Michailovich et al., 2011), although not widely available yet, will bring the acquisition times down even further.”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, neutral): “t induce prior information is incorporated into objective function for the the reconstruction. Possible choices for the regularizer include total variation (10,40,41), ℓ1-norm of wavelet coefficients (4,8,42), sparsity in adaptive transform domains (9,43), and more recently neural networks (25,26,31”
Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom (2018, methodology): “With the advent of methods such as magnetic resonance fingerprinting (122) and compressed sensing (123,124), quantitative MRI can now be performed in a clinically appropriate timeline; however, quality control with a quantitative MRI phantom is necessary to ensure the accuracy and precision of resul”
Magnetic Resonance Fingerprinting (2013, positive): “For example, T 1 , T 2 , off-resonance frequency are included in this study, or other properties such as diffusion and magnetization transfer (MT) using the well-established Bloch equation formalism of MR 21,22 .”