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Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT.
Int J Biomed Imaging 2013; 2013:907501IJ

Abstract

Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

23840199

Citation

Zhu, Zangen, et al. "Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT." International Journal of Biomedical Imaging, vol. 2013, 2013, p. 907501.
Zhu Z, Wahid K, Babyn P, et al. Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT. Int J Biomed Imaging. 2013;2013:907501.
Zhu, Z., Wahid, K., Babyn, P., & Yang, R. (2013). Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT. International Journal of Biomedical Imaging, 2013, p. 907501. doi:10.1155/2013/907501.
Zhu Z, et al. Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT. Int J Biomed Imaging. 2013;2013:907501. PubMed PMID: 23840199.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT. AU - Zhu,Zangen, AU - Wahid,Khan, AU - Babyn,Paul, AU - Yang,Ran, Y1 - 2013/06/06/ PY - 2013/02/04/received PY - 2013/04/30/revised PY - 2013/05/13/accepted PY - 2013/7/11/entrez PY - 2013/7/11/pubmed PY - 2013/7/11/medline SP - 907501 EP - 907501 JF - International journal of biomedical imaging JO - Int J Biomed Imaging VL - 2013 N2 - Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index. SN - 1687-4188 UR - https://www.unboundmedicine.com/medline/citation/23840199/Compressed_Sensing_Based_MRI_Reconstruction_Using_Complex_Double_Density_Dual_Tree_DWT_ L2 - https://dx.doi.org/10.1155/2013/907501 DB - PRIME DP - Unbound Medicine ER -