title: Infimal convolution of total generalized variation functionals for dynamic MRI.
abstract: To accelerate dynamic MR applications using infimal convolution of total generalized variation functionals (ICTGV) as spatio-temporal regularization for image reconstruction.
  
  
  
  ICTGV comprises a new image prior tailored to dynamic data that achieves regularization via optimal local balancing between spatial and temporal regularity. Here it is applied for the first time to the reconstruction of dynamic MRI data. CINE and perfusion scans were investigated to study the influence of time dependent morphology and temporal contrast changes. ICTGV regularized reconstruction from subsampled MR data is formulated as a convex optimization problem. Global solutions are obtained by employing a duality based non-smooth optimization algorithm.
  
  
  
  The reconstruction error remains on a low level with acceleration factors up to 16 for both CINE and dynamic contrast-enhanced MRI data. The GPU implementation of the algorithm suites clinical demands by reducing reconstruction times of one dataset to less than 4 min.
  
  
  
  ICTGV based dynamic magnetic resonance imaging reconstruction allows for vast undersampling and therefore enables for very high spatial and temporal resolutions, spatial coverage and reduced scan time. With the proposed distinction of model and regularization parameters it offers a new and robust method of flexible decomposition into components with different degrees of temporal regularity. Magn Reson Med 78:142-155, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, Graz, Austria; BioTechMed-Graz, Graz, Austria.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schloegl
authors.affiliation: Institute for Mathematics and Scientific Computing, University of Graz, Heinrichstrasse 36, Graz, Austria; BioTechMed-Graz, Graz, Austria.
authors.forename: Martin
authors.initials: M
authors.lastname: Holler
authors.affiliation: Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, Graz, Austria; BioTechMed-Graz, Graz, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schwarzl
authors.affiliation: Institute for Mathematics and Scientific Computing, University of Graz, Heinrichstrasse 36, Graz, Austria; BioTechMed-Graz, Graz, Austria.
authors.forename: Kristian
authors.initials: K
authors.lastname: Bredies
authors.affiliation: Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, Graz, Austria; BioTechMed-Graz, Graz, Austria.
authors.forename: Rudolf
authors.initials: R
authors.lastname: Stollberger
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D009991:Oscillometry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis
keywords: CMR; dynamic magnetic resonance imaging; infimal convolution; perfusion imaging; total generalized variation; variational models