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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-22482-4_60
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