Blood flow rate and velocity are important parameters for the study of vascular systems, and for the diagnosis, monitoring and evaluation of treatment of cerebro- and cardiovascular disease. For rapid imaging of cerebral and cardiac blood vessels, digital x-ray subtraction angiography has numerous advantages over other modalities. Roentgen-videodensitometric techniques measure blood flow and velocity from changes of contrast material density in x-ray angiograms. Many roentgen-videodensitometric flow measurement methods can also be applied to CT, MR and rotational angiography images. Hence, roentgen-videodensitometric blood flow and velocity measurement from digital x-ray angiograms represents an important research topic. This work contains a critical review and bibliography surveying current and old developments in the field. We present an extensive survey of English-language publications on the subject and a classification of published algorithms. We also present descriptions and critical reviews of these algorithms. The algorithms are reviewed with requirements imposed by neuro- and cardiovascular clinical environments in mind.

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Fluoroscopic images are degraded by scattering of x-rays from within the patient and by veiling glare in the image intensifier. Both of these degradations are well described by a response function applied to either the scatter-free or primary intensity. The response function is variable, with dependence on such factors as patient thickness and imaging geometry. We describe an automated regularization technique for obtaining response function parameters with a minimal loss of signal. This method requires a high-transmission structured reference object to be interposed between the x-ray source and the subject. We estimate the parameters by minimizing residual correlations between the reference object and the computed subject density after a scatter-glare correction. We use simulated images to evaluate our method for both ideal and clinically realistic conditions. We find that the residual root-mean-square (rms) error ideally decreases with an increasing number of independent pixels (N) as (1/N)1/2. In simulated 256x256 angiograms mean normalized rms errors were reduced from 40% to 11% in noise-free images, and from 41% to 17% in noisy images, with a similar improvement in densitometric vessel cross-section measurements. These results demonstrate the validity of the method for simulated images and characterize its expected performance on clinical images.

A novel method is presented for correcting errors in measurements of biplane projection imaging geometry without prior identification of corresponding points in the two images. For imaged objects that project onto both images, a constraint equation is obtained that relates weighted integrals along corresponding epipolar lines. The integrals are computed to first order in the angular beamwidth, which is assumed to be small. Starting from measured or estimated values, geometrical parameters are computed iteratively in order to maximize the correlation between epipolar line integrals in the two images. Improvement in the computation of corresponding epipolar lines is demonstrated on images of a wire phantom. The root mean square distance of the epipolar lines from the corresponding reference points is improved from 15 pixel widths to less than 4 pixel widths (1.3 mm). Convergence is demonstrated on phantom images for individual parameter variations up to 70% in relative magnification, a relative shift of the imaging planes by 50 pixels, or a relative rotation of at least 35 degrees around either of two axes. Applicability to clinical images is demonstrated by using a biplane angiogram of a pig to align corresponding points determined from images of a Perspex cube acquired with the same geometry.

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