Detail publikace

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

Originální název

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

Anglický název

DCE-MRI Perfusion Analysis with L1-Norm Spatial Regularization

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}