Automatic selection of the optimal cardiac phase for gated three-dimensional coronary x-ray angiography.
OBJECTIVE
For the reconstruction of the coronary arteries from rotational angiography data, a crucial point is the selection of the optimal cardiac phase for data reconstruction. To avoid time-consuming interactive selection of the optimal cardiac phase by visual inspection of multiple high-resolution data sets reconstructed at different cardiac phases, an automatic approach for deriving optimal reconstruction windows is attractive.


METHODS
This paper presents a new approach to fully automatic selection of the optimal cardiac phase for image reconstruction. It is based on the analysis of a four-dimensional data set of the region of interest reconstructed at low-spatial resolution utilizing an image quality index, which quantifies the image quality of a single three-dimensional reconstructed volume. The derived image quality index utilizes the histogram information of a single temporal snapshot as a quality measure for the vessel reconstruction. The proposed technique was applied to 16 projection data sets obtained in eight pigs.


RESULTS
Experiments to evaluate the proposed method based on user-defined image quality parameters serving as ground truth, showed a relatively high correlation (>84%) for high-quality (c(phi) > 0.95) images.


CONCLUSIONS
An image-based technique is introduced, which is able to determine the optimal cardiac phase for 3D-RCA fully automatically. The proposed method was successfully applied to 16 data sets obtained in a total of 8 porcine models.
Rasche
Volker
V
Department of Internal Medicine II, University of Ulm, Robert-Koch-Str. 8, 89081 Ulm, Germany.
Movassaghi
Babak
B
Grass
Michael
M
Schäfer
Dirk
D
Buecker
Arno
A
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine