Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error

It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k -space via applying current compressive sampling theory. The paper presents a m...

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Veröffentlicht in:	Medical & biological engineering & computing 2012-09, Vol.50 (9), p.991-1000
Hauptverfasser:	Yin, X. X., Ng, B. W.-H., Ramamohanarao, K., Baghai-Wadji, A., Abbott, D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Bioengineering Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Breast Neoplasms - pathology Computer Applications Computer engineering Data imaging Female Fourier transforms Human Physiology Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image reconstruction Imaging Magnetic Resonance Imaging - methods Mathematical models NMR Noise Nuclear magnetic resonance Original Article Pattern Recognition, Automated - methods Radiology Recovery Reproducibility of Results Sample Size Sampling Sampling error Sensitivity and Specificity Sparsity Studies Thresholds Wavelet transforms
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