Publication detail

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

BARTOŠ, M. ŠOREL, M. MANGOVÁ, M. RAJMIC, P. STANDARA, M. JIŘÍK, R.

Original Title

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

English Title

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

Type

abstract

Language

en

Original Abstract

DCE-MRI perfusion analysis suffers from low reliability, especially when 2nd generation pharmacokinetic models are used to estimate perfusion parameter maps (voxel-by-voxel estimation) in low SNR conditions. These models provide estimates of plasma flow and capillary permeability in addition to the commonly used parameters Ktrans, kep. This contribution presents a method for estimation of perfusion maps using the tissue homogeneity model with incorporated spatial regularization in the form of total variation. The algorithm is based on the proximal minimization methods well established in image reconstruction problems. The use of state-of-the-art minimization and image regularization techniques stabilizes the estimates of perfusion parameter maps and keeps the computational demands low.

English abstract

DCE-MRI perfusion analysis suffers from low reliability, especially when 2nd generation pharmacokinetic models are used to estimate perfusion parameter maps (voxel-by-voxel estimation) in low SNR conditions. These models provide estimates of plasma flow and capillary permeability in addition to the commonly used parameters Ktrans, kep. This contribution presents a method for estimation of perfusion maps using the tissue homogeneity model with incorporated spatial regularization in the form of total variation. The algorithm is based on the proximal minimization methods well established in image reconstruction problems. The use of state-of-the-art minimization and image regularization techniques stabilizes the estimates of perfusion parameter maps and keeps the computational demands low.

Keywords

DCE-MR; Perfusion Analysis; L1-Norm; Spatial Regularization

Released

21.06.2018

Pages from

1

Pages to

3

Pages count

3

BibTex


@misc{BUT149003,
  author="Michal {Bartoš} and Michal {Šorel} and Marie {Mangová} and Pavel {Rajmic} and Michal {Standara} and Radovan {Jiřík}",
  title="DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization
",
  annote="DCE-MRI perfusion analysis suffers from low reliability, especially when 2nd generation pharmacokinetic models are used to estimate perfusion parameter maps (voxel-by-voxel estimation) in low SNR conditions. These models provide estimates of plasma flow and capillary permeability in addition to the commonly used parameters Ktrans, kep. This contribution presents a method for estimation of perfusion maps using the tissue homogeneity model with incorporated spatial regularization in the form of total variation. The algorithm is based on the proximal minimization methods well established in image reconstruction problems. The use of state-of-the-art minimization and image regularization techniques stabilizes the estimates of perfusion parameter maps and keeps the computational demands low.",
  booktitle="Joint Annual Meeting ISMRM-ESMRMB 2018, Paris Expo Porte de Versailles, Paris, France, June 16--21",
  chapter="149003",
  howpublished="online",
  year="2018",
  month="june",
  pages="1--3",
  type="abstract"
}