Learning-based Optimization of the Under-sampling Pattern in MRI

Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this paper, we consider the problem of optimizing the sub-sampling pattern in a data-driven fashion. Since the reconstruction model's performance depends on the s...

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Veröffentlicht in:	arXiv.org 2019-04
Hauptverfasser:	Bahadir, Cagla Deniz, Dalca, Adrian V, Sabuncu, Mert R
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Brain Image reconstruction Machine learning Magnetic resonance imaging Neural networks NMR Nuclear magnetic resonance Optimization Sampling
Online-Zugang:	Volltext
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