To derive blood flow dynamics from cineangiograms (CAG), we have developed an image processing algorithm to estimate a two-dimensional blood fiow velocity map projected on CAG. Each image area of CAG is diveded into blocks, and it is assumed that the movement of the contrast medium between two serial frames is restricted only to adjacent blocks. By this assumption, a fundamental equation" and the maximum flow constraints" are derived. The equation and constraints state the relationship between the volume of contrast medium in each block and the flow components" that are the volumes of contrast medium flowing from/to its adjacent blocks. The initial guess" that is a set of approximately obtained flow components is corrected using these relationships. The corrected flow components are then transformed into blood flow velocities, which are illustrated in the form of a needle diagram. In numerical experiments, the estimation error between the real flow velocity generated artificially and the flow velocity estimated with our algorithm was evaluated under one of the worst conditions. Although the maximum error was fairly large, the estimated flow velocity map was still acceptable for visual inspection of flow velocity pattern. We then applied our algorithm to an abdominal CAG (clinical data). The results showed flow stagnation and reverse flow in the abdominal aneurysm, which are consistent with the presence of a thrombus in the aneurysm. This algorithm may be a useful diagnostic tool in the assessment of vascular disease.

This paper deals with a method of registration and superimposition of a coronary arterial tree on a myocardial SPECT (Single Photon Emission Computed Tomography) image. We can grasp the myocardial function more easily in connection with the shape of the coronary arterial tree. The superimposed image is easily obtainable through some manual pointing on coronary angiograms (CAG) followed by an automatic matching method: First, a rough shape model of left ventricle is estimated by using SPECT data. This 3-D left ventricular model is projected on a pair of bi-plane CAG images. We can obtain two 2-D coronary images on bull's eye map by scanning the left ventricular surface projected on CAG. By maximizing a matching degree between two 2-D coronary images, registration between CAG and SPECT is performed. Finally the superimposed image is obtained by integrating two 2-D coronary images and bull's eye image of SPECT. We validated our method by numerical experiments with artificial data set and also applied it to two clinical data sets.