Statistical-model-based identification of complete vessel-tree frames in coronary angiograms

Coronary angiograms are pre-interventionally recorded moving X-ray images of a patient's beating heart, where the coronary arteries are made visible by a contrast medium. They serve to diagnose, e.g., stenoses, and as roadmaps during the intervention itself. Covering about three to four heart cycles, coronary angiograms consist of three underlying states: inflow, when the contrast medium flows into the vessels, filled state, when the whole vessel tree is visible and outflow, when the contrast medium is washed out. Obviously, only that part of the sequence showing the full vessel tree is useful as a roadmap. We therefore describe methods for automatic identification of these frames. To this end, a vessel map with enhanced vessels and compressed background is first computed. Vessel enhancement is based on the observation that vessels are the locally darkest oriented structures with significant motion. The vessel maps can be regarded as containing two classes, viz. (bright) vessels and (dark) background. From a histogram analysis of each vessel map image, a time-dependent feature curve is computed in which the states inflow, filled state and outflow can already visually be distinguished. We then describe two approaches to segment the feature curve into these states: the first method models the observations in each state by a polynomial, and seeks the segmentation which allows the best fit of three polynomials as measured by a Maximum-Likelihood criterion. The second method models the state sequence by a Hidden Markov model, and estimates it using the Maximum a Posteriori (MAP)-criterion. We will present results for a number of angiograms recorded in clinical routine.