Patients suffering from a heart valve deficiency are often treated by replacing the valve with an artificial or
biological implant. In case of biological implants, the use of porcine heart valves is common. Quality assessment
and inspection methods are mandatory to supply the patients (and also medical research) with only the best
such xenograft implants thus reducing the number of follow-up surgeries to replace worn-up valves. We describe
an approach for automatic in-vitro evaluation of prosthetic heart valves in an artificial circulation system. We
show how to measure the orifice area during a heart cycle to obtain an orifice curve. Different quality parameters
are then estimated on such curves.
A number of image analysis tasks of the heart region have to cope
with both the problem of respiration and heart contraction. While
the heart contraction status can be estimated based on the ECG,
respiration status estimation must be based on the images themselves, unless additional devices for respiration measurements
are used. Since diaphragm motion is closely linked to respiration,
we describe a method to detect and track the diaphragm in x-ray
projections. We model the diaphragm boundary as being approximately
circular. Diaphragm detection is then based on edge detection
followed by a Hough transform for circles. To avoid that the
detection algorithm is misled by high frequency image content, we
first apply a morphological multi-scale top hat operator. A Canny
edge detector is then applied to the top hat filtered images. In the
edge images, the circle corresponding to the diaphragm boundary is
found by the Hough transform. To restrict the search in the 3D Hough
parameter space (parameters are circle center coordinates and
radius), prior anatomical knowledge about position and size of the
diaphragm for the given image acquisition geometry is taken into
account. In subsequent frames, diaphragm position and size are
predicted from previous detection and tracking results. For each
detection result, a confidence measure is computed by analyzing the
Hough parameter space with respect to the goodness of the peak
giving the circle parameters and by analyzing the coefficient of
variation of the pixel that form the circle described by the maximum
in Hough parameter space. If the confidence is not sufficiently high
-- indicating a poor fit between the Hough circle and true diaphragm
boundary -- the detection result is optionally refined by an active
contour algorithm.
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.
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