Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@inproceedings{BUT115848,
  author="Marie {Mangová} and Pavel {Rajmic} and Radovan {Jiřík}",
  title="Acceleration of Perfusion MRI Using Locally Low-Rank Plus Sparse Model",
  annote="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.",
  address="Springer",
  booktitle="Latent Variable Analysis and Signal Separation",
  chapter="115848",
  doi="10.1007/978-3-319-22482-4_60",
  howpublished="electronic, physical medium",
  institution="Springer",
  year="2015",
  month="august",
  pages="514--521",
  publisher="Springer",
  type="conference paper"
}