title: Highly undersampled peripheral Time-of-Flight magnetic resonance angiography: optimized data acquisition and iterative image reconstruction.
abstract: OBJECTIVE
  The aim of this study was to investigate the acceleration of peripheral Time-of-Flight magnetic resonance angiography using Compressed Sensing and parallel magnetic resonance imaging (MRI) while preserving image quality and vascular contrast.
  
  
  METHODS
  An analytical sampling pattern is proposed that combines aspects of parallel MRI and Compressed Sensing. It is used in combination with a dedicated Split Bregman algorithm. This approach is compared with current state-of-the-art patterns and reconstruction algorithms.
  
  
  RESULTS
  The acquisition time was reduced from 30 to 2.5 min in a study using ten volunteer data sets, while showing improved sharpness, better contrast and higher accuracy compared to state-of-the-art techniques.
  
  
  CONCLUSIONS
  This study showed the benefits of the proposed dedicated analytical sampling pattern and Split Bregman algorithm for optimizing the Compressed Sensing reconstruction of highly accelerated peripheral Time-of-Flight data.
authors.affiliation: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany. jana.hutter@cs.fau.de.|School of Advanced Optical Technologies, Erlangen, Germany. jana.hutter@cs.fau.de.
authors.forename: Jana
authors.initials: J
authors.lastname: Hutter
authors.affiliation: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany.
authors.forename: Robert
authors.initials: R
authors.lastname: Grimm
authors.affiliation: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany.|School of Advanced Optical Technologies, Erlangen, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Forman
authors.affiliation: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany.|School of Advanced Optical Technologies, Erlangen, Germany.
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.affiliation: MR Applications and Workflow Development, Healthcare Sector, Siemens AG, Erlangen, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Schmitt
mesh_terms: D000465:Algorithms; D016477:Artifacts; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D058729:Peripheral Arterial Disease / Q000473:pathology*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Compressed Sensing; Iterative reconstruction; Non-contrast-enhanced MRA; Peripheral angiography