Pre- and post-processing filters for improvement of blood velocity estimation

The standard deviation on the blood velocity estimates are influenced by measurement noise, velocity spread, and signal alteration introduced by de-noising and clutter filters. A noisy and non-smooth appearance of the velocity distribution is obtained, which is not consistent with the actual velocity in the vessels. Post-processing is beneficial to obtain an image that minimizes the variation, and present the important information to the clinicians. Applying the theory of fluid mechanics introduces restrictions on the variations possible in a flow field. Neighboring estimates in time and space should be highly correlated, since transitions should occur smoothly. This idea is the basis of the algorithm developed in this study. From Bayesian image processing theory an a posteriori probability distribution for the velocity field is computed based on constraints on smoothness. An estimate of the velocity in a given point is computed by maximization of the probability, given prior knowledg..