Iterative methods for fast reconstruction of undersampled dynamic contrast-enhanced MRI data. In World Congress on Medical Physics and Biomedical Engineering 2018. (Book Chapter)

  • NORLUX Neuro-Oncology Laboratory
  • Translational Radiomics
January 01, 2019 By:
  • Walner H
  • Bartoš M
  • Mangová M
  • Keunen O
  • Bjerkvig R
  • Jiřík R
  • Šorel M.

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.

2019 Jan. Ibbott GS, Lhotska L, Sukupova L and Lackovic I, eds. Singapore: Springer Verlag, 2019. p.267-271. (IFMBE, Vol. 68, N°1). ISBN 978-981-10-9034-9 (print); 978-981-10-9035-6 (online).
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