Publication result detail

Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model

DAŇKOVÁ, M.; RAJMIC, P.; JIŘÍK, R.

Original Title

Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model

English Title

Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model

Type

Paper in proceedings (conference paper)

Original 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.

English 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.

Keywords

Perfusion; MRI; DCE-MRI; Compressed sensing; Sparsity; Locally low-rank

Key words in English

Perfusion; MRI; DCE-MRI; Compressed sensing; Sparsity; Locally low-rank

Authors

DAŇKOVÁ, M.; RAJMIC, P.; JIŘÍK, R.

RIV year

2016

Released

25.08.2015

Publisher

Springer

Location

Liberec

ISBN

978-3-319-22481-7

Book

Latent Variable Analysis and Signal Separation

Pages from

514

Pages to

521

Pages count

8

BibTex

@inproceedings{BUT115848,
  author="Marie {Mangová} and Pavel {Rajmic} and Radovan {Jiřík}",
  title="Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model",
  booktitle="Latent Variable Analysis and Signal Separation",
  year="2015",
  pages="514--521",
  publisher="Springer",
  address="Liberec",
  doi="10.1007/978-3-319-22482-4\{_}60",
  isbn="978-3-319-22481-7"
}