Automatic selection of optimal systolic and diastolic reconstruction windows for dual-source CT coronary angiography.

Seifarth H, Puesken M, Wienbeck S, Maintz D, Fischbach R, Heindel W, Juergens KU

Forschungsartikel (Zeitschrift)

Zusammenfassung

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).

Details zur Publikation

FachzeitschriftEuropean Radiology
Jahrgang / Bandnr. / Volume19
Ausgabe / Heftnr. / Issue7
Seitenbereich1645-1652
StatusVeröffentlicht
Veröffentlichungsjahr2009
Sprache, in der die Publikation verfasst istEnglisch
DOI10.1007/s00330-009-1329-2
StichwörterMale; Coronary Angiography; Tomography X-Ray Computed; Aged; Adult; Young Adult; Sensitivity and Specificity; Pattern Recognition Automated; Artificial Intelligence; Aged 80 and over; Female; Middle Aged; Radiographic Image Interpretation Computer-Assisted; Reproducibility of Results; Coronary Artery Disease; Algorithms; Humans; Artifacts; Male; Coronary Angiography; Tomography X-Ray Computed; Aged; Adult; Young Adult; Sensitivity and Specificity; Pattern Recognition Automated; Artificial Intelligence; Aged 80 and over; Female; Middle Aged; Radiographic Image Interpretation Computer-Assisted; Reproducibility of Results; Coronary Artery Disease; Algorithms; Humans; Artifacts

Autor*innen der Universität Münster

Heindel, Walter Leonhard
Klinik für Radiologie Bereich Lehre & Forschung
Maintz, David
Klinik für Radiologie Bereich Lehre & Forschung
Püsken, Michael
Klinik für Radiologie Bereich Lehre & Forschung
Seifarth, Harald
Klinik für Radiologie Bereich Lehre & Forschung
Wienbeck, Susanne
Klinik für Radiologie Bereich Lehre & Forschung