title: Correcting for large vessel contamination in dynamic susceptibility contrast perfusion MRI by extension to a physiological model of the vasculature.
abstract: OBJECTIVE
  Dynamic susceptibility contrast (DSC) perfusion images are contaminated by contributions from macro vascular signal arising from contrast agent within the larger arteries that do not contribute directly to the local tissue perfusion.
  
  
  METHODS
  A vascular model of the DSC perfusion signal was extended by the inclusion of a macro vascular component based on the arterial input function. This was implemented within a Bayesian nonlinear model-fitting algorithm that included automatic model complexity reduction. Results were compared with existing methods that do not correct for the macro vascular contamination as well as an independent component analysis technique.
  
  
  RESULTS
  Macro vascular signal was identified in regions corresponding to larger arteries resulting in reductions by 62% within a region of interest identified with high contamination. Whereas visually similar results could be achieved with independent component analysis, it resulted in reductions in global tissue perfusion and was not robustly applicable to patient data.
  
  
  CONCLUSIONS
  A model-based strategy for correction of macro vascular contamination in DSC perfusion images is feasible, although the model may currently need extending to more accurately account for nonlinear effects of contrast agent in large arteries. Magn Reson Med 74:280-290, 2015. © 2014 Wiley Periodicals, Inc.
authors.affiliation: Institute of Biomedical Engineering, University of Oxford, ORCRB, Old Road Campus, Headington, Oxford, United Kingdom.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Chappell
authors.affiliation: Institute of Biomedical Engineering, University of Oxford, ORCRB, Old Road Campus, Headington, Oxford, United Kingdom.
authors.forename: Amit
authors.initials: A
authors.lastname: Mehndiratta
authors.affiliation: Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia.|Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Australia.
authors.forename: Fernando
authors.initials: F
authors.lastname: Calamante
keywords: Bayesian analysis; cerebral blood flow (CBF); dynamic susceptibility contrast MRI; macro vascular contamination; vascular model