Iterative methods for fast reconstruction of undersampled dynamic contrast-enhanced MRI data

Hynek Walner*, Michal Bartoš, Marie Mangová, Olivier Keunen, Rolf Bjerkvig, Radovan Jiřík, Michal Šorel

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)267-271
Number of pages5
JournalIFMBE Proceedings
Issue number1
Publication statusPublished - 2019
EventWorld Congress on Medical Physics and Biomedical Engineering, WC 2018 - Prague, Czech Republic
Duration: 3 Jun 20188 Jun 2018


  • Compressed sensing
  • Iterative reconstruction techniques


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