Abstract
Perfusion magnetic resonance imaging is a technique used in diagnostics and evaluation of therapy response, where the quantification is done by analyzing the perfusion curves. Perfusion- and permeability-related tissue parameters can be obtained using advanced pharmacokinetic models, but, these models require high spatial and temporal resolution of the acquisition simultaneously. The resolution is usually increased by means of compressed sensing: the acquisition is accelerated by under-sampling. However, these techniques need to be improved to achieve higher spatial resolution and/or to allow multislice acquisition. We propose a modification of the L+S model for the reconstruction of perfusion curves from the under-sampled data. This model assumes that perfusion data can be modelled as a superposition of locally low-rank data and data that are sparse in the spectral domain. We show that our model leads to a better performance compared to the other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jackson, A., Buckley, D.L., Parker, M.: Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology. Springer, Berlin (2005)
Mézl, M., Jiřík, R., Harabiš, V.: Acquisition and data processing in ultrasound perfusion analysis. In: New trends in biomedical engineering, pp. 44–51. Brno University of Technology, Czech (2013)
Harabiš, V., Kolář, R., Mézl, M., Jiřík, R.: Comparison and evaluation of indicator dilution models for bolus ofultrasound contrast agents. Physiol. Meas. 34, 151–162 (2013)
Bartoš, M.: Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging. Doctoral thesis, Brno University of Technology (2014)
Sourbron, S.P., Buckley, D.L.: Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys. Med. Biol. 57(2), R1–R33 (2012)
Han, S.H., Paulsen, J.L., Zhu, G., Song, Y., Chun, S.I., Cho, G., Ackerstaff, E., Koutcher, J.A., Cho, H.J.: Temporal/spatial resolution improvement of in vivo DCE-MRI with compressed sensing-optimized FLASH. Magn. Reson. Imaging 30(6), 741–752 (2012)
Block, K.T., Uecker, M., Frahm, J.: Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magn. Reson. Med. 57(6), 1086–1098 (2007)
Li, Z., Berman, B.P., Altbach, M.I., et al.: Highly accelerated 3D dynamic imaging with variable density golden angle stack-of-stars sampling. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 21, p. 3797 (2013)
Santos, J.M., Cunningham, C.H., Lustig, M., Hargreaves, B.A., Hu, B.S., Nishimura, D.G., Pauly, J.M.: Single breath-hold whole-heart MRA using variable-density spirals at 3T. Magn. Reson. Med. 55(2), 371–379 (2006)
Sungheon, K., Feng, L., Moy, L., et al.: Highly accelerated golden-angle radial acquisition with joint compressed sensing and parallel imaging reconstruction for breast DCE-MRI. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 21, p. 1468 (2012)
Zhang, N., Song, G., Liao, W., et al.: Accelerating dynamic contrast-enhanced MRI using K-T ISD. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 21, p. 4221 (2012)
Wang, H., Miao, Y., Zhou, K., Yanming, Y., Bao, S., He, Q., Dai, Y., Xuan, S.Y., Tarabishy, B., Ye, Y., Jiani, H.: Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. Med. Phys. 37(9), 4971–4981 (2010)
Lingala, S.G., Hu, Y., DiBella, E., Jacob, M.: Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans. Med. Imaging 30(5), 1042–1054 (2011)
Otazo, R., Candes, E., Sodickson, D.K.: Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn. Reson. Med. 73(3), 1125–1136 (2015)
Zhang, T., Cheng, J.Y., Potnick, A.G., Barth, R.A., Alley, M.T., Uecker, M., Lustig, M., Pauly, J.M., Vasanawala, S.S.: Fast pediatric 3D free-breathing abdominal dynamic contrast enhanced MRI with high spatiotemporal resolution. J. Magn. Reson. Imaging 41(2), 460–473 (2015)
Zhang, T., Alley, M., Lustig, M., Li, X., Pauly, J., Vasanawala, S.: Fast 3D DCE-MRI with sparsity and low-rank enhanced SPIRiT (SLR-SPIRiT). In: Proceedings of the 21st Annual Meeting of ISMRM, Salt Lake City, Utah, USA, p. 2624 (2013)
Shepp, L.A., Logan, B.F.: The Fourier reconstruction of a head section. IEEE Trans. Nucl. Sci. 21, 21–43 (1974)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Bauschke, H.H., Burachik, R.S., Combettes, P.L., Elser, V., Russell Luke, D., Wolkowicz, H. (eds.) Fixed-Point Algorithms forInverse Problems in Science and Engineering, pp. 185–212. Springer, New York (2011)
Acknowledgement
The authors thank M. Šorel for careful reading of the manuscript. The research was supported by the Ministry of Education, Youth, and Sports of the Czech Republic (project No. LO1212), by the SIX project registration number CZ.1.05/2.1.00/03.0072, and by the Czech Science Foundation grant no. GA15-12607S.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Daňková, M., Rajmic, P., Jiřík, R. (2015). Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-22482-4_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22481-7
Online ISBN: 978-3-319-22482-4
eBook Packages: Computer ScienceComputer Science (R0)