Publication detail

Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

WALNER, H. BARTOŠ, M. MANGOVÁ, M. KEUNEN, O. BJERKVIG, R. JIŘÍK, R. ŠOREL, M.

Original Title

Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

English Title

Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

Type

conference paper

Language

en

Original Abstract

This paper introduces new variational formulation for reconstruction from subsampled dynamic contrast-enhanced DCE-MRI data, that combines a data-driven approach using estimated temporal basis and total variation regularization (PCA TV). We also experimentally compares the performance of such model with two other state-of-the-art formulations. One models the shape of perfusion curves in time as a sum of a curve belonging to a low-dimensional space and a function sparse in a suitable domain (L + S model). The other possibility is to regularize both spatial and time domains (ICTGV). We are dealing with the specific situation of the DCE-MRI acquisition with a 9.4T small animal scanner, working with noisier signals than human scanners and with a smaller number of coil elements that can be used for parallel acquisition and small voxels. Evaluation of the selected methods is done through subsampled reconstruction of radially-sampled DCE-MRI data. Our analysis shows that compressed sensed MRI in the form of regularization can be used to increase the temporal resolution of acquisition while keeping a sufficient signal-to-noise ratio.

English abstract

This paper introduces new variational formulation for reconstruction from subsampled dynamic contrast-enhanced DCE-MRI data, that combines a data-driven approach using estimated temporal basis and total variation regularization (PCA TV). We also experimentally compares the performance of such model with two other state-of-the-art formulations. One models the shape of perfusion curves in time as a sum of a curve belonging to a low-dimensional space and a function sparse in a suitable domain (L + S model). The other possibility is to regularize both spatial and time domains (ICTGV). We are dealing with the specific situation of the DCE-MRI acquisition with a 9.4T small animal scanner, working with noisier signals than human scanners and with a smaller number of coil elements that can be used for parallel acquisition and small voxels. Evaluation of the selected methods is done through subsampled reconstruction of radially-sampled DCE-MRI data. Our analysis shows that compressed sensed MRI in the form of regularization can be used to increase the temporal resolution of acquisition while keeping a sufficient signal-to-noise ratio.

Keywords

DCE-MRI; Iterative reconstruction techniques; Compressed sensing

Released

30.05.2018

ISBN

978-981-10-9035-6

Book

World Congress on Medical Physics and Biomedical Engineering 2018

Pages from

267

Pages to

271

Pages count

8

BibTex


@inproceedings{BUT149006,
  author="Hynek {Walner} and Michal {Bartoš} and Marie {Mangová} and Olivier {Keunen} and Rolf {Bjerkvig} and Radovan {Jiřík} and Michal {Šorel}",
  title="Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data",
  annote="This paper introduces new variational formulation for reconstruction from subsampled dynamic contrast-enhanced DCE-MRI data, that combines a data-driven approach using estimated temporal basis and total variation regularization (PCA TV). We also experimentally compares the performance of such model with two other state-of-the-art formulations. One models the shape of perfusion curves in time as a sum of a curve belonging to a low-dimensional space and a function sparse in a suitable domain (L + S model). The other possibility is to regularize both spatial and time domains (ICTGV). We are dealing with the specific situation of the DCE-MRI acquisition with a 9.4T small animal scanner, working with noisier signals than human scanners and with a smaller number of coil elements that can be used for parallel acquisition and small voxels. Evaluation of the selected methods is done through subsampled reconstruction of radially-sampled DCE-MRI data. Our analysis shows that compressed sensed MRI in the form of regularization can be used to increase the temporal resolution of acquisition while keeping a sufficient signal-to-noise ratio.",
  booktitle="World Congress on Medical Physics and Biomedical Engineering 2018",
  chapter="149006",
  doi="10.1007/978-981-10-9035-6_48",
  howpublished="online",
  year="2018",
  month="may",
  pages="267--271",
  type="conference paper"
}