title: Automatic recognition of subject-specific cerebrovascular trees.
abstract: An image filter designed for reconstructing cerebrovascular trees from MR images is described. Current imaging techniques capture major cerebral vessels reliably, but often fail to detect small vessels, whose contrast is suppressed due to limited resolution, slow blood flow rate, and distortions around bifurcations or nonvascular structures. An incomplete view of angioarchitecture limits the information available to physicians.
  
  
  
  A novel Hessian-based filter for contrast-enhancement in MR angiography and venography for blood vessel reconstruction without introducing dangling segments is presented. We quantify filter performance with receiver-operating-characteristic and dice-similarity-coefficient analysis. Total extracted vascular length, number-of-segments, volume, surface-to-distance, and positional error are calculated for validation.
  
  
  
  Reconstruction of cerebrovascular trees from MR images of six volunteers show that the new filter renders more complete representations of subject-specific cerebrovascular networks. Validation with phantom models shows the filter correctly detects blood vessels across all length scales without failing at bifurcations or distorting diameters.
  
  
  
  The novel filter can potentially improve the diagnosis of cerebrovascular diseases by delivering metrics and anatomy of the vasculature. It also facilitates the automated analysis of large datasets by computing biometrics free of operator subjectivity. The high quality reconstruction enables computational mesh generation for subject-specific hemodynamic simulations. Magn Reson Med 77:398-410, 2017. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Chih-Yang
authors.initials: CY
authors.lastname: Hsu
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Ben
authors.initials: B
authors.lastname: Schneller
authors.affiliation: Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Alaraj
authors.affiliation: Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Flannery
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.|Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA.|Center for MR Research, University of Illinois at Chicago, Chicago, Illinois, USA.|Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Xiaohong Joe
authors.initials: XJ
authors.lastname: Zhou
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA.|Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA.
authors.forename: Andreas
authors.initials: A
authors.lastname: Linninger
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply* / Q000000981:diagnostic imaging*; D002560:Cerebrovascular Circulation / Q000502:physiology; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results
keywords: image processing; segmentation; vessels