title: Objective stenosis quantification from post-stenotic signal loss in phase-contrast magnetic resonance angiographic datasets of flow phantoms and renal arteries.
abstract: In this study a semi-automated and observer-independent algorithm for quantifying post-stenotic signal loss (PSL) in three-dimensional phase-contrast (PC) magnetic resonance angiography (MRA) of patients with renal artery stenosis is presented. This algorithm was developed on MRA datasets of stenotic phantoms, included in a flow circuit with stationary flows. The length and the severity of the PSL (incorporating both the length and the degree of PSL) in the MRA datasets were proposed for quantifying the stenoses. The algorithm was tested in renal arteries; ten patients with renal artery stenosis and seven healthy volunteers were investigated. Digital subtraction angiography was performed in the patients and served as the gold standard. Stenosis severity showed better correlation with the severity of the PSL than with the length, both for in vitro and in vivo measurements. Spearman correlation coefficients (rs) showed statistically significant correlations between the severity of the PSL and parameters determined by digital subtraction angiography, i.e., percent diameter stenosis (rs = 0.90). The length of the PSL showed no correlation with the diameter stenosis (rs = 0.37). In conclusion, this study presents a semi-automated and observer-independent way of quantifying signal loss, and the severity of the PSL is proposed for quantifying stenoses, rather than the length of PSL.
authors.affiliation: Division of Image Processing, Leiden University Medical Centre, The Netherlands.
authors.forename: J J
authors.initials: JJ
authors.lastname: Westenberg
authors.forename: R J
authors.initials: RJ
authors.lastname: van der Geest
authors.forename: M N
authors.initials: MN
authors.lastname: Wasser
authors.forename: J
authors.initials: J
authors.lastname: Doornbos
authors.forename: P M
authors.initials: PM
authors.lastname: Pattynama
authors.forename: A
authors.initials: A
authors.lastname: de Roos
authors.forename: J
authors.initials: J
authors.lastname: Vanderschoot
authors.forename: J H
authors.initials: JH
authors.lastname: Reiber
mesh_terms: D000368:Aged; D000465:Algorithms; D003199:Computer Systems; D005260:Female; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D018810:Magnetic Resonance Angiography / Q000295:instrumentation*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D019047:Phantoms, Imaging; D012016:Reference Values; D012077:Renal Artery / Q000473:pathology / Q000503:physiopathology; D012078:Renal Artery Obstruction / Q000175:diagnosis* / Q000503:physiopathology; D012680:Sensitivity and Specificity