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We have developed a blood flow waveform shape model using principal component analysis (PCA) and applied this to our existing concentration-distance curve matching technique for the extraction of flow waveforms from dynamic digital x-ray images. The aim of the study was to validate the system using a moving-vessel flow phantom. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the “gold standard” measurement. A model waveform was constructed from 256 previously recorded waveforms from the EMF using PCA. Flow waveforms were extracted from parametric images derived from dynamic x-ray data by finding the parameters of the shape model that minimized the mean value of our cost function. The computed waveforms were compared to the EMF recordings. The model-based approach produced narrower limits of agreement with the EMF data than our previously developed algorithms and, in the presence of increasing noise in the parametric images, it out-performed the other algorithms.

In-Vitro Validation of a Novel Model-Based Approach to the Measurement of Arterial Blood Flow Waveforms from Dynamic Digital X-ray Images