title: Feasibility of a novel blood noise reduction algorithm to enhance reproducibility of ultra-high-frequency intravascular ultrasound images.
abstract: BACKGROUND
  Ultra-high-frequency (40- to 50-MHz) intravascular ultrasound (IVUS) improves image quality compared with conventional 20- to 30-MHz IVUS. However, as the frequency of IVUS increases, high-intensity backscatter from blood components may cause visual difficulties in discrimination between the lumen and arterial wall structure. The purpose of this study was to evaluate the effect of a novel blood noise reduction algorithm (BNR) on quantitative coronary ultrasound measurements.
  
  
  RESULTS
  IVUS studies using a 40-MHz transducer were performed in 35 patients with coronary artery disease. A total of 620 gray-scale images (310 pairs) were processed with and without the BNR, and lumen cross-sectional area (CSA) was determined by 2 independent observers. With the BNR, the intraobserver and interobserver correlation coefficients for lumen CSA were significantly improved (0.85 to 0.99 and 0.80 to 0.98, respectively). In the 270 images (135 pairs) in which vessel wall measurements were possible, the BNR significantly improved the intraobserver and interobserver correlation coefficients for plaque plus media CSA (0.83 to 0.99 and 0.76 to 0.97, respectively), whereas no influence was observed for external elastic membrane CSA (1.00 to 1.00 and 0.99 to 0.99, respectively).
  
  
  CONCLUSIONS
  This study demonstrates the feasibility of this novel algorithm to reduce blood noise, thereby enabling accurate lumen border delineation and providing reproducible measurements of both the lumen and plaque plus media CSAs. Incorporating a digital BNR may serve as an important adjunct to ultra-high-frequency IVUS imaging for improving accurate quantitative evaluation of vessel dimensions.
authors.affiliation: Division of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA 94305, USA.
authors.forename: K
authors.initials: K
authors.lastname: Hibi
authors.forename: A
authors.initials: A
authors.lastname: Takagi
authors.forename: X
authors.initials: X
authors.lastname: Zhang
authors.forename: T J
authors.initials: TJ
authors.lastname: Teo
authors.forename: H N
authors.initials: HN
authors.lastname: Bonneau
authors.forename: P G
authors.initials: PG
authors.lastname: Yock
authors.forename: P J
authors.initials: PJ
authors.lastname: Fitzgerald
mesh_terms: D000465:Algorithms; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D018084:Ultrasonography, Interventional / Q000379:methods*