title: Automatic selection of optimal systolic and diastolic reconstruction windows for dual-source CT coronary angiography.
abstract: The aim of this study was to assess the performance of a motion-map algorithm that automatically determines optimal reconstruction windows for dual-source coronary CT angiography. In datasets from 50 consecutive patients, optimal systolic and diastolic reconstruction windows were determined using the motion-map algorithm. For manual determination of the optimal reconstruction window, datasets were reconstructed in 5% steps throughout the RR interval. Motion artifacts were rated for each major coronary vessel using a five-point scale. Mean motion scores using the motion-map algorithm were 2.4 +/- 0.8 for systolic reconstructions and 1.9 +/- 0.8 for diastolic reconstructions. Using the manual approach, overall motion scores were significantly better (1.9 +/- 0.5 and 1.7 +/- 0.6, p < 0.05), but diagnostic image quality was reached in >90% of cases using either approach. Using the automated approach, there was a negative correlation between heart rate and motion scores for systolic reconstructions (rho = -0.26, p < 0.05) and a positive correlation for diastolic reconstructions (rho = 0.46, p < 0.01). For the manual approach, no significant correlation was found for systolic reconstructions (rho = -0.1, p = 0.52), while there was a positive correlation for diastolic reconstructions (rho = 0.48, p < 0.01). Thus, the motion-map algorithm is a useful tool to save time in finding an appropriate reconstruction window in patients with heart rates <70 bpm (diastolic reconstruction) and >80 bpm (systolic reconstruction).
authors.affiliation: Department of Clinical Radiology, University of Muenster, Albert-Schweitzer-Strasse 33, 48149, Muenster, Germany. seifarth@uni-muenster.de
authors.forename: H
authors.initials: H
authors.lastname: Seifarth
authors.forename: M
authors.initials: M
authors.lastname: Puesken
authors.forename: S
authors.initials: S
authors.lastname: Wienbeck
authors.forename: D
authors.initials: D
authors.lastname: Maintz
authors.forename: R
authors.initials: R
authors.lastname: Fischbach
authors.forename: W
authors.initials: W
authors.lastname: Heindel
authors.forename: K-U
authors.initials: KU
authors.lastname: Juergens
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult