Computer vision based performance analysis of prosthetic heart valves




Alizadeh, Maryam

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Prosthetic heart valves (PHVs) are routinely used to replace defective native heart valves in patients suffering from valvular heart diseases. While PHVs are life-saving, they have limitations in performance and durability. Therefore, it is crucial to rigorously test and evaluate their designs before their implantation. PHVs are commonly examined using cardiovascular testing equipment that measures the hemodynamic characteristics of the valves, while also providing the opportunity for their visual assessment by collecting high-quality videos. Such visual data, obtained during mechanical simulations, are typically assessed by human experts, which is a tedious and error-prone task. Automatic assessment of PHVs from video data is possible, however, there are some challenges that need to be addressed. The evolution of the valve orifice area during one cardiac cycle is one of the key quality metrics for PHV visual assessment. Very fast motion of the valve’s leaflets is one of the challenges while dealing with the visual data. Nevertheless, the more important issue lies in the orifice being partly occluded by the inner side of the leaflets or inaccurately depicted due to its transparency. This issue has not been addressed in the literature. In the first part of the thesis, a novel orifice area segmentation algorithm is proposed for automatic quantitative performance analysis of PHVs, based on the leaflet free edges to accurately extract the actual orifice area. The video frames, recorded by a high-speed digital camera during in vitro simulations, are used to obtain an initial estimate of the orifice area using active contouring methods. This initial estimate is then refined to detect leaflet free edges via a curve extension scheme and considering brightness and smoothness criteria. Both of the developed algorithms are later modified for addressing challenges related to the fast motion of leaflets, automatic detection of the beginning of a cycle, and overly bright spots and narrow areas. Evaluation on several cases including three different PHVs and with different video qualities demonstrated the effectiveness of the proposed approach and adjustments in detecting valve leaflet free edges and extraction of the actual orifice area. The proposed method significantly outperforms a baseline algorithm both in terms of valve design and computer vision evaluation metrics. It can also cope with lower quality videos and is better at processing frames with a very small opening, which is a very crucial quality for determining the malfunctions related to improper closing of the valves. In the second part of the thesis, the above-mentioned segmented orifice area is used for the durability estimation of the prosthetic heart valves. More than 50% of PHVs encounter a structural failure within 15 years post-implantation mostly because of the excessive localized forces on some areas. We perform a computer vision (CV)-based analysis of the visual symmetry of valve leaflet motion and investigate its correlation with the functional symmetry of the valve. We hypothesize that an asymmetry in the valve leaflet motion will generate an asymmetry in the flow patterns, resulting in added local stress and forces on some of the leaflets. Two pair-wise leaflet symmetry scores are proposed based on diagonals of orthogonal projection matrices (DOPM) and dynamic time warping (DTW) techniques. The proposed symmetry score profiles are compared with fluid dynamic parameters (vorticity and velocity values) at the leaflet borders, obtained from valve-specific numerical simulations. Experiments on four cases including different tricuspid PHV designs yielded promising results, with DTW scores showing good coherence with respect to the simulations, which confirms our hypothesis. The established link between visual and functional symmetry opens the door for durability estimation of prosthetic heart valves using computer vision techniques.



Computer Vision, Prosthetic Heart Valves, Medical Image Processing