Automatic vessel segmentation using active contours in cine phase contrast flow measurements.
The segmentation of images obtained by cine magnetic resonance (MR) phase contrast velocity mapping using manual or semi-automated methods is a time consuming and observer-dependent process that still hampers the use of flow quantification in a clinical setting. A fully automatic segmentation method based on active contour model algorithms for defining vessel boundaries has been developed. For segmentation, the phase image, in addition to the magnitude image, is used to address image distortions frequently seen in the magnitude image of disturbed flow fields. A modified definition for the active contour model is introduced to reduce the influence of missing or spurious edge information of the vessel wall. The method was evaluated on flow phantom data and on in vivo images acquired in the ascending aorta of humans. Phantom experiments resulted in an error of 0.8% in assessing the luminal area of a flow phantom equipped with an artificial heart valve. Blinded evaluation of the volume flow rates from automatic vs. manual segmentation of gradient echo (FFE) phase contrast images obtained in vivo resulted in a mean difference of -0.9 +/- 3%. The mean difference from automatic vs. manual segmentation of images acquired with a hybrid phase contrast sequence (TFEPI) within a single breath-hold was -0.9 +/- 6%.
Kozerke
S
S
Institute of Biomedical Engineering and Medical Informatics, University of Zurich and Swiss Federal Institute of Technology, Switzerland.
Botnar
R
R
Oyre
S
S
Scheidegger
M B
MB
Pedersen
E M
EM
Boesiger
P
P
D000328:Adult; D000465:Algorithms; D001011:Aorta / Q000502:physiology*; D016477:Artifacts; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019028:Magnetic Resonance Imaging, Cine; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D019047:Phantoms, Imaging; D012039:Regional Blood Flow; D012212:Rheology