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Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation

Issue title: Papers from the 4th International Conference on Biomedical Engineering and Biotechnology (ICBEB2015), 18-21 August 2015, Shanghai, China ``New Paradigms for Biomedical Engineering and Biotechnology''

Article type: Research Article

Authors: Huang, Jianpinga; b | Wang, Lihuic | Chu, Chunyua | Zhang, Yanlia | Liu, Wanyua; b; * | Zhu, Yuemina; b

Affiliations: [a] Metislab, LIA CNRS, Harbin Institute of Technology, Harbin, Heilongjiang, China | [b] CREATIS, INSA Lyon, INSA Lyon, University of Lyon, Villeurbanne, France | [c] College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China

Correspondence: [*] Corresponding author: Wanyu Liu, Metislab, LIA CNRS, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin 150001, Heilongjiang, China. Tel.: +86 0451 86403287; E-mail:[email protected]

Keywords: Diffusion tensor imaging, compressed sensing, low rank, sparsity constraint

DOI: 10.3233/THC-161186

Journal: Technology and Health Care, vol. 24, no. s2, pp. S593-S599, 2016

Published: 13 June 2016
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Abstract

Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques.

References

[1] 

Le Bihan D., , Mangin J. F., , Poupon C., , Clark C. A., , Pappata S., , Molko N., , and Chabriat H., Diffusion tensor imaging: Concepts and applications, Journal of Magnetic Resonance Imaging, vol. 13: , pp. 534-546, Apr (2001) .

[2] 

Jiang Y., and Hsu E. W., Accelerating MR diffusion tensor imaging via filtered reduced-encoding projection-reconstruction, Magnetic Resonance in Medicine, vol. 53: , pp. 93-102, Jan (2005) .

[3] 

Haldar J. P., , Wedeen V. J., , Nezamzadeh M., , Dai G., , Weiner M. W., , Schuff N., , and Liang Z.-P., Improved diffusion imaging through SNR-enhancing joint reconstruction, Magnetic Resonance in Medicine, vol. 69: , pp. 277-289, Jan (2013) .

[4] 

Holdsworth S. J., , Skare S., , Newbould R. D., , and Bammer R., Robust GRAPPA-accelerated diffusion-weighted readout-segmented (RS)-EPI, Magnetic Resonance in Medicine, vol. 62: , pp. 1629-1640, Dec (2009) .

[5] 

Bammer R., , Auer M., , Keeling S. L., , Augustin M., , Stables L. A., , Prokesch R. W., , Stollberger R., , Moseley M. E., , and Fazekas F., Diffusion tensor imaging using single-shot SENSE-EPI, Magnetic Resonance in Medicine, vol. 48: , pp. 128-136, Jul (2002) .

[6] 

Candes E. J., , Romberg J., , and Tao T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol. 52: , pp. 489-509, Feb (2006) .

[7] 

Donoho D. L., Compressed sensing, IEEE Transactions on Information Theory, vol. 52: , pp. 1289-1306, Apr (2006) .

[8] 

Adluru G., , Hsu E., , and Di Bella E. V. R., , Constrained reconstruction of sparse cardiac MR DTI data, in Functional Imaging and Modeling of the Heart, Proceedings, vol. 4466: , Sachse F. B., and Seemann G., Eds., ed, (2007) , pp. 91-99.

[9] 

Wu Y., , Zhu Y.-J., , Tang Q.-Y., , Zou C., , Liu W., , Dai R.-B., , Liu X., , Wu E. X., , Ying L., , and Liang D., Accelerated MR diffusion tensor imaging using distributed compressed sensing, Magnetic Resonance in Medicine, vol. 71: , pp. 763-772, Feb (2014) .

[10] 

Li L., , Hu X., , and Gao H., Compressive Diffusion MRI-Part 1 Why Low-Rank, in Proceedings of the 21th Annual Meeting of ISMRM, Salt Lake, (2013) .

[11] 

Welsh C. L., , DiBella E. V. R., , Adluru G., , and Hsu E. W., Model-based reconstruction of undersampled diffusion tensor k-space data, Magnetic Resonance in Medicine, vol. 70: , pp. 429-440, Aug (2013) .

[12] 

Huang J., , Zhang S., , and Metaxas D., Efficient MR image reconstruction for compressed MR imaging, Medical Image Analysis, vol. 15: , pp. 670-679, Oct (2011) .

[13] 

Wang L., , Zhu Y., , Li H., , Liu W., , and Magnin I. E., Multiscale modeling and simulation of the cardiac fiber architecture for DMRI, IEEE Transactions on Biomedical Engineering, vol. 59: , pp. 16-19, Jan (2012) .

[14] 

Lustig M., , Donoho D., , and Pauly J. M., Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, vol. 58: , pp. 1182-1195, Dec (2007) .

[15] 

Lingala S. G., , Hu Y., , DiBella E., , and Jacob M., Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR, IEEE Transactions on Medical Imaging, vol. 30: , pp. 1042-1054, May (2011) .

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