Long-term function of biological heart valve prostheses (BHV) is limited by structural deterioration leading to failure with associated arterial hypertension. The objective of this work was development of an easy to handle real-time pulse reactor for evaluation of biological and tissue engineered heart valves under different pressures and long-term conditions. The pulse reactor was made of medical grade materials for placement in a 37 degrees C incubator. Heart valves were mounted in a housing disc moving horizontally in culture medium within a cylindrical culture reservoir. The microprocessor-controlled system was driven by pressure resulting in a cardiac-like cycle enabling competent opening and closing of the leaflets with adjustable pulse rates and pressures between 0.25 to 2 Hz and up to 180/80 mmHg, respectively. A custom-made imaging system with an integrated high-speed camera and image processing software allow calculation of effective orifice areas during cardiac cycle. This simple pulse reactor design allows reproducible generation of patient-like pressure conditions and data collection during long-term experiments.

This paper presents a new method for automated detection of stents. The method consists of three sequential steps. At first, a 3D to 2D projection procedure is applied to find the global stent location. A local search strategy was designed, using a combination of characteristic image features to extract the sampling point candidates of the stent in each of the original cross-sectional images. Finally, the preselected points are accepted or rejected depending on a set of a priori criteria for position and shape. Using the resulting stent points, geometrical parameters of the stent are automatically calculated and a wire frame model is generated for 3D surface reconstruction. Thus, in combination with our algorithms for automated detection of the lumen cross-sectional area, the new method is an essential component for 3D visualization of stents and the automated quantification of the degree of in-stent restenosis. The evaluation is based on in vitro and in vivo recordings. The results show that the new algorithm is well-suited to replace time consuming manual segmentation and measurements.