title: Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography.
abstract: OBJECTIVE
  We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed.
  
  
  METHODS
  After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting.
  
  
  RESULTS
  Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels.
  
  
  CONCLUSIONS
  To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.
authors.affiliation: Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84108, USA. lzhang@ucair.med.utah.edu
authors.forename: Ling
authors.initials: L
authors.lastname: Zhang
authors.forename: Brian E
authors.initials: BE
authors.lastname: Chapman
authors.forename: Dennis L
authors.initials: DL
authors.lastname: Parker
authors.forename: John A
authors.initials: JA
authors.lastname: Roberts
authors.forename: Junyu
authors.initials: J
authors.lastname: Guo
authors.forename: Prashanthi
authors.initials: P
authors.lastname: Vemuri
authors.forename: Sung M
authors.initials: SM
authors.lastname: Moon
authors.forename: Frederic
authors.initials: F
authors.lastname: Noo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000098:blood supply* / Q000166:cytology*; D002560:Cerebrovascular Circulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity