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Int J Comput Assist Radiol Surg. 2010 Jan;5(1):19-28. doi: 10.1007/s11548-009-0398-7. Epub 2009 Sep 19.

Optimization of acquisition trajectories for 3D rotational coronary venography.

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  • 1Institute of Telecommunications, Hamburg University of Technology, Eissendorfer Strasse 40, 21073 Hamburg, Germany.



Rotational coronary X-ray imaging on C-arm systems provides a multitude of diagnostic projections from the vascular tree with a single contrast agent bolus. The acquisition trajectory is typically limited to a circular arc with a fixed caudo-cranial angulation. This may cause sub- optimal projection directions for specific vessel segments for all acquired views, e.g., those segments orthogonal to the axis of rotation. In this paper, a method is presented to calculate a patient-independent acquisition trajectory with respect to vessel foreshortening and overlap for multiple vessel segments of the coronary tree. This method can be applied to artery as well as vein anatomy.


Rotational coronary venograms of 14 patients have been used to generate three-dimensional mesh representations with a semi-automatic two view modeling algorithm. The venous tree is divided into seven different vessel segments. Foreshortening and overlap of every segment are calculated and combined for all patients in a measure called obstruction value. The weighted obstruction values of all vessel segments define a cost function for the entire two-dimensional angular range of the C-arm system. Viterbi's algorithm is used to calculate an optimal trajectory with respect to this cost function. The method is validated by leave-one-out cross-validation on the 14 rotational venography data sets and on simulated venograms of a segmented computed tomography (CT) data set. Projection images with a foreshortening value below 10% and overlap below 20% are rated 'optimal'.


In 12 (85.7%) data sets, 43% more optimal images were acquired using the presented method compared to the standard circular arc trajectory. As well, in 13 (92.8%) data sets 38% more vessel segments can be optimally visualized in the acquired images. The test on the CT data set showed that the resulting average root-mean-square error of the extracted centerline points of the segmented CT data set compared to the error based on the views from the circular arc was reduced from 2.52 to 1.55 mm.


In a first test, the method proved to deliver improved image quality by reducing foreshortening and overlap of vessel segments and may therefore also improve the centerline extraction accuracy of the semi-automatic two view modeling method.

[PubMed - indexed for MEDLINE]
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