Novel approaches to the measurement of arterial blood flow from dynamic digital X-ray images

Rhode, K.S. and Lambrou, Tryphon and Hawkes, D. J. and Seifalian, A. M. (2005) Novel approaches to the measurement of arterial blood flow from dynamic digital X-ray images. IEEE transactions on medical imaging, 24 (4). pp. 500-513. ISSN 0278-0062

Full content URL: http://dx.doi.org/10.1109/TMI.2004.843202

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Item Type:Article
Item Status:Live Archive

Abstract

We have developed two new algorithms for the measurement of blood flow from dynamic X-ray angiographic images. Both algorithms aim to improve on existing techniques. First, a model-based (MB) algorithm is used to constrain the concentration-distance curve matching approach. Second, a weighted optical flow algorithm (OP) is used to improve on point-based optical flow methods by averaging velocity estimates along a vessel with weighting based on the magnitude of the spatial derivative. The OP algorithm was validated using a computer simulation of pulsatile blood flow. Both the OP and the MB algorithms were validated using a physiological blood flow circuit. Dynamic biplane digital X-ray images were acquired following injection of iodine contrast medium into a variety of simulated arterial vessels. The image data were analyzed using our integrated angiographic analysis software SARA to give blood flow waveforms using the MB and OP algorithms. These waveforms were compared to flow measured using an electromagnetic flow meter (EMF). In total 4935 instantaneous measurements of flow were made and compared to the EMF recordings. It was found that the new algorithms showed low measurement bias and narrow limits of agreement and also out-performed the concentration-distance curve matching algorithm (ORG) and a modification of this algorithm (PA) in all studies. © 2005 IEEE.

Keywords:Algorithms, Angiography, Blood vessels, Data reduction, Optical flows, X rays, Blood flow measurements, Curve matching, X -ray measurements, X-ray angiography, Medical imaging
Subjects:F Physical Sciences > F350 Medical Physics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8676
Deposited On:04 May 2013 22:09

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