title: Blood flow quantification from 2D phase contrast MRI in renal arteries using an unsupervised data driven approach.
abstract: We present a clustering approach to segment the renal artery from 2D PC Cine MR images to measure arterial blood velocity and flow. Such information is important in grading renal artery stenosis and to support the decision on surgical interventions like percutaneous transluminal angioplasty. Results from 20 data sets (3 volunteers, 7 patients) show that the renal arteries could be extracted automatically and the corresponding velocity profiles were close (r = 0.977) to that obtained by manual delineations of the vessel areas.
authors.affiliation: Computer Assisted Clinical Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Germany. frank.zoellner@medma.uni-heidelberg.de
authors.forename: Frank Gerrit
authors.initials: FG
authors.lastname: Zöllner
authors.forename: Jan Ankar
authors.initials: JA
authors.lastname: Monssen
authors.forename: Jarle
authors.initials: J
authors.lastname: Rørvik
authors.forename: Arvid
authors.initials: A
authors.lastname: Lundervold
authors.forename: Lothar R
authors.initials: LR
authors.lastname: Schad
mesh_terms: D001783:Blood Flow Velocity / Q000502:physiology*; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D011673:Pulsatile Flow; D012077:Renal Artery / Q000502:physiology*