Real-time tracking of contrast bolus propagation in X-ray peripheral angiography

This paper describes a robust and fast algorithm for real-time tracking of contrast bolus propagation in vessels during X-ray peripheral angiography studies, in which the authors are interested in imaging the arterial structures in the legs. A bolus of contrast medium is injected to the patient and a time-sequence of X-ray images records the bolus propagation. Since the field of view of the imaging device is small compared to the object length, the device has to step through an number of preset stations to follow the bolus. Currently it requires a physician to manually issue stepping commands according to his/her visual assessment of bolus propagation observed in a monitor. A smart data acquisition technology is being developed to replace this error-prone manual process in which the bolus tracking algorithm plays a key role. Novel feature normalization circumvents the need for estimating vessel sizes and enables one to estimate contrast density and trade bolus using only features that can be extracted efficiently from acquired image sequences. The use of a parametric model, developed to characterize extracted features, makes the algorithm resistant to noise and feature extraction errors. Extensive experiments on real and simulated angiographic sequences have demonstrated the robustness, accuracy and efficiency of the tracking algorithm.