title: Reliable estimation of microvascular flow patterns in patients with disrupted blood-brain barrier using dynamic susceptibility contrast MRI.
abstract: To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier.
  
  
  
  We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations. Next, we assessed possible over- or under correction for tracer extravasation in two patients receiving contrast agent preloading and two patients not receiving preloading. Perfusion metrics for N = 60 patients diagnosed with either grade III (N = 14) or grade IV gliomas (N = 46) were analyzed across tissue types to evaluate the ability to distinguish regions with different hemodynamic patterns. Finally, N = 4 patient cases undergoing anti-angiogenic treatment are evaluated qualitatively for treatment effects. All patient data were acquired at 3.0 Tesla.
  
  
  
  The simulation studies showed good robustness against low signal-to-noise ratios, exemplified with Pearson correlations of R = 0.833 (mean transit time) and R = 0.738 (CTH) at signal-to-noise ratio = 20. Region-of-interest analysis of the N = 60 glioma patients showed that cerebral blood volume (CBV) significantly separated enhancing core from edema (grade IV: P < 10-8, grade III: P < 0.05) and enhancing core from normal appearing ipsilateral white matter (NAWM) (grade IV: P < 10-8, grade III: P < 0.05). The microvascular parameters were particularly good in separating edematous tissue from NAWM tissue in grade IV gliomas (P < 0.001). Finally, CTH separated grade III and grade IV core tissue (P < 0.05).
  
  
  
  We have demonstrated robustness of the proposed Bayesian algorithm against experimental noise and demonstrated complementary value in microvascular parameters to the CBV parameter in separating tissue types in gliomas.
  
  
  
  3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:537-549.
authors.affiliation: Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
authors.forename: Mikkel Bo
authors.initials: MB
authors.lastname: Hansen
authors.affiliation: Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.|Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark.|Institute of Neuroradiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, D-10117, Berlin.
authors.forename: Anna
authors.initials: A
authors.lastname: Tietze
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Jayashree
authors.initials: J
authors.lastname: Kalpathy-Cramer
authors.affiliation: Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Gerstner
authors.affiliation: Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
authors.forename: Tracy T
authors.initials: TT
authors.lastname: Batchelor
authors.affiliation: Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.|Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Leif
authors.initials: L
authors.lastname: Østergaard
authors.affiliation: Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark.
authors.forename: Kim
authors.initials: K
authors.lastname: Mouridsen
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D020533:Angiogenesis Inhibitors / Q000494:pharmacology; D001499:Bayes Theorem; D001812:Blood-Brain Barrier / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000188:drug therapy; D003198:Computer Simulation; D003287:Contrast Media; D018572:Disease-Free Survival; D005260:Female; D005910:Glioma / Q000000981:diagnostic imaging*; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008833:Microcirculation; D008875:Middle Aged; D015233:Models, Statistical; D010477:Perfusion; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
chemical_list: D020533:Angiogenesis Inhibitors; D003287:Contrast Media
keywords: Bayesian expectation-maximization; DSC-MRI; capillary transit-time heterogeneity (CTH); cerebral neoplasms; contrast agent leakage; oxygen extraction fraction (OEF)