Evaluation of model-based blood flow quantification from rotational angiography

For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel morphology and hemodynamics. Rotational angiography is routinely used to determine 3D geometry, and we recently outlined a method to estimate the blood flow waveform and mean volumetric flow rate from images acquired using rotational angiography. Our method uses a model of contrast agent dispersion to estimate the flow parameters from the spatial and temporal progression of the contrast agent concentration, represented by a flow map. Artifacts due to the rotation of the c-arm are overcome by using a reliability map. An attenuation calibration can be used to support our method, but it might not be available in clinical practice. In this paper, we analyze the influence of the attenuation calibration on our method. Furthermore, we concentrate on the validation of the proposed algorithm, with particular emphasis on the influence of parameters such as the length of the analyzed vessel segment, the frame rate of the acquisition, and the duration of the injection on accuracy. For the validation, rotational angiographic image sequences from a computer simulation and from a phantom experiment were used. With a mean error of about 10% for the mean volumetric flow rate and about 13% for the blood flow waveform from the phantom experiments, we conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions which are accurate enough to be clinically useful.

[1]  W. B. Gogarty,et al.  Numerical and experimental results on the dispersion of a solute in a fluid in lamin/ar flow through a tube , 1962, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[2]  Jyeshtharaj B. Joshi,et al.  Axial mixing in laminar pipe flows , 2004 .

[3]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[4]  J. Alison Noble,et al.  Estimating cerebral blood flow from a rotational angiographic system , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[5]  Kawal S. Rhode,et al.  Novel approaches to the measurement of arterial blood flow from dynamic digital X-ray images , 2005, IEEE Transactions on Medical Imaging.

[6]  Irina Waechter,et al.  Using flow information to support 3D vessel reconstruction from rotational angiography. , 2008, Medical physics.

[7]  J. N. H. Brunt,et al.  Development of a Model to Predict the Potential Accuracy of Vessel Blood Flow Measurements from Dynamic Angiographic Recordings , 1988 .

[8]  Greg Bednarz,et al.  Absolute volumetric coronary blood flow measurement with digital subtraction angiography , 1998, The International Journal of Cardiac Imaging.

[9]  J P Woodcock,et al.  Physiological significance of arterial models derived using transcutaneous ultrasonic flowmeters [proceedings]. , 1978, The Journal of physiology.

[10]  David J. Hawkes,et al.  Quantification of Blood Flow from Rotational Angiography , 2007, MICCAI.

[11]  R. Close,et al.  X-ray videodensitometric methods for blood flow and velocity measurement: a critical review of literature. , 2000, Medical physics.

[12]  Ruola Ning,et al.  Blood flow measurement by cone-beam CT bolus imaging , 2006, SPIE Medical Imaging.

[13]  John D. Carroll,et al.  Quantitative analysis of reconstructed 3-D coronary arterial tree and intracoronary devices , 2002, IEEE Transactions on Medical Imaging.

[14]  J. Womersley.  Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known , 1955, The Journal of physiology.

[15]  G. Taylor.  Dispersion of soluble matter in solvent flowing slowly through a tube , 1953, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.