Sliding window and compressive sensing for low-field dynamic magnetic resonance imaging

Cristian Toraci; Gabriele Zaccaria; Stefano Ceriani; David Wilson; Marco Fato; Michele Piana

Sliding window and compressive sensing for low-field dynamic magnetic resonance imaging

Computational Engineering, Finance, and Science 2014-02-12 v1 Medical Physics

Authors: Cristian Toraci , Gabriele Zaccaria , Stefano Ceriani , David Wilson , Marco Fato , Michele Piana

Abstract

We describe an acquisition/processing procedure for image reconstruction in dynamic Magnetic Resonance Imaging (MRI). The approach requires sliding window to record a set of trajectories in the k-space, standard regularization to reconstruct an estimate of the object and compressed sensing to recover image residuals. We validated this approach in the case of specific simulated experiments and, in the case of real measurements, we showed that the procedure is reliable even in the case of data acquired by means of a low-field scanner.

Keywords

image reconstruction compressed sensing 3d reconstruction

Cite

@article{arxiv.1402.2453,
  title  = {Sliding window and compressive sensing for low-field dynamic magnetic resonance imaging},
  author = {Cristian Toraci and Gabriele Zaccaria and Stefano Ceriani and David Wilson and Marco Fato and Michele Piana},
  journal= {arXiv preprint arXiv:1402.2453},
  year   = {2014}
}

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Submitted to IEEE Transactions on Medical Imaging

Sliding window and compressive sensing for low-field dynamic magnetic resonance imaging

Abstract

Keywords

Cite

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