Dictionary learning and time sparsity for dynamic MR data reconstruction

Caballero J, Price AN, Rueckert D, Hajnal JV (2014)

Publication Type: Journal article

Publication year: 2014

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 33

Pages Range: 979-994

Article Number: 6716020

Journal Issue: 4

DOI: 10.1109/TMI.2014.2301271

Abstract

The reconstruction of dynamic magnetic resonance data from an undersampled k-space has been shown to have a huge potential in accelerating the acquisition process of this imaging modality. With the introduction of compressed sensing (CS) theory, solutions for undersampled data have arisen which reconstruct images consistent with the acquired samples and compliant with a sparsity model in some transform domain. Fixed basis transforms have been extensively used as sparsifying transforms in the past, but recent developments in dictionary learning (DL) have been shown to outperform them by training an overcomplete basis that is optimal for a particular dataset. We present here an iterative algorithm that enables the application of DL for the reconstruction of cardiac cine data with Cartesian undersampling. This is achieved with local processing of spatio-temporal 3D patches and by independent treatment of the real and imaginary parts of the dataset. The enforcement of temporal gradients is also proposed as an additional constraint that can greatly accelerate the convergence rate and improve the reconstruction for high acceleration rates. The method is compared to and shown to systematically outperform k-tFOCUSS, a successful CS method that uses a fixed basis transform. © 2014 IEEE.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) St Thomas' Hospital GB United Kingdom (GB)

How to cite

APA:

Caballero, J., Price, A.N., Rueckert, D., & Hajnal, J.V. (2014). Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Transactions on Medical Imaging, 33(4), 979-994. https://doi.org/10.1109/TMI.2014.2301271

MLA:

Caballero, Jose, et al. "Dictionary learning and time sparsity for dynamic MR data reconstruction." IEEE Transactions on Medical Imaging 33.4 (2014): 979-994.

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