Clinical angiography requires hundreds of X-ray images, putting the patients and particularly the medical staff at risk. Dosage reduction involves an inevitable sacrifice in image quality. In this work, the latter problem is addressed by first modeling the signal-dependent, Poisson-distributed noise that arises as a result of this dosage reduction. The commonly utilized noise model for single images is shown to be obtainable from the new model. Stochastic temporal filtering techniques are proposed to enhance clinical fluoroscopy sequences corrupted by quantum mottle. The temporal versions of these filters as developed here are more suitable for filtering image sequences, as correlations along the time axis can be utilized. For these dynamic sequences, the problem of displacement field estimation is treated in conjunction with the filtering stage to ensure that the temporal correlations are taken along the direction of motion to prevent object blur.
A method is described for the spatio-temporal filtering of digital angiographic image sequences corrupted by simulated quantum mottle. An x-ray dosage reduction in coronary imaging studies inevitably leads to the introduction of quantum mottle -a Poisson distributed, signal dependent noise that occurs as a result of statistical fluctuations in the arrival of photons at the image intensifier tube. Although spatial filtering of individual frames in the sequence is often performed to improve image quality, this technique does not utilize valuable information from temporal correlations between images.The spatio-temporal filter here estimates motion trajectories for individual pixels and then filters along the direction of motion. This method is different from temporal filtering techniques that do not use motion compensation as the latter always blur the edges of the coronary arteries. Although the method is derived for the estimation of a single frame from two degraded frames of a sequence, it is easily generalized to multi-frame estimates. The performance of the above filter is examined using real image sequences corrupted by quantum mottle.
We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.