Using flow information to support 3D vessel reconstruction from rotational angiography.
For the assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) morphologic and hemodynamic information about the vessel system. Rotational angiography is routinely used to image the 3D vascular geometry and we have shown previously that rotational subtraction angiography has the potential to also give quantitative information about blood flow. Flow information can be determined when the angiographic sequence shows inflow and possibly outflow of contrast agent. However, a standard volume reconstruction assumes that the vessel tree is uniformly filled with contrast agent during the whole acquisition. If this is not the case, the reconstruction exhibits artifacts. Here, we show how flow information can be used to support the reconstruction of the 3D vessel centerline and radii in this case. Our method uses the fast marching algorithm to determine the order in which voxels are analyzed. For every voxel, the rotational time intensity curve (R-TIC) is determined from the image intensities at the projection points of the current voxel. Next, the bolus arrival time of the contrast agent at the voxel is estimated from the R-TIC. Then, a measure of the intensity and duration of the enhancement is determined, from which a speed value is calculated that steers the propagation of the fast marching algorithm. The results of the fast marching algorithm are used to determine the 3D centerline by backtracking. The 3D radius is reconstructed from 2D radius estimates on the projection images. The proposed method was tested on computer simulated rotational angiography sequences with systematically varied x-ray acquisition, blood flow, and contrast agent injection parameters and on datasets from an experimental setup using an anthropomorphic cerebrovascular phantom. For the computer simulation, the mean absolute error of the 3D centerline and 3D radius estimation was 0.42 and 0.25 mm, respectively. For the experimental datasets, the mean absolute error of the 3D centerline was 0.45 mm. Under pulsatile and nonpulsatile conditions, flow information can be used to enable a 3D vessel reconstruction from rotational angiography with inflow and possibly outflow of contrast agent. We found that the most important parameter for the quality of the reconstruction of centerline and radii is the range through which the x-ray system rotates in the time span of the injection. Good results were obtained if this range was at least 135 degrees. As a standard c-arm can rotate 205 degrees, typically one third of the acquisition can show inflow or outflow of contrast agent, which is required for the quantification of blood flow from rotational angiography.
Waechter
Irina
I
Centre for Medical Image Computing, University College London, Malet Place Engineering Building, London WC1E BT, United Kingdom. I.waechter@cs.ucl.ac.uk
Bredno
Joerg
J
Weese
Juergen
J
Barratt
Dean C
DC
Hawkes
David J
DJ
D000465:Algorithms; D000792:Angiography; D001808:Blood Vessels / Q000473:pathology; D002533:Cerebral Angiography / Q000295:instrumentation* / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology; D004867:Equipment Design; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed
D003287:Contrast Media