Automated Quantification of Bileaflet Mechanical Heart Valve Leaflet Angles in CT Images

Revisiting echocardiographic features of prosthetic heart valves: the necessity of correct differentiation of mono-leaflet vs. bileaflet mechanical heart valves in a case report

BACKGROUND

Mechanical heart valve replacement is a standard treatment for severe valvular disorders. The use of mono-leaflet valves has decreased recently. Recognizing the echocardiographic features of mono-leaflet and bileaflet valves is crucial for accurate complication diagnosis and proper management.

CASE PRESENTATION

A 65-year-old female with mono-leaflet mitral and bileaflet tricuspid valves underwent an echocardiographic assessment. This simple educational case provides a unique opportunity to compare the echocardiographic features of these valves within a single patient.

CONCLUSION

There is a crucial need for clinicians, particularly those in training, to differentiate between mono-leaflet and bileaflet mechanical heart valves adeptly. With mono-leaflet valves decreasing in prevalence, proficiency in recognizing the echocardiographic nuances of each type is imperative. Failure to do so may result in misdiagnoses and inappropriate management. This underscores the significance of continuous education and vigilance in echocardiographic assessments to ensure optimal patient care.

Automatic segmentation and tracking of biological prosthetic heart valves

Purpose: Prosthetic heart valve designs must be rigorously tested using cardiovascular equipment. The valve orifice area over time constitutes a key quality metric which is typically assessed manually, thus a tedious and error-prone task. From a computer vision viewpoint, a major unsolved issue lies in the orifice being partly occluded by the leaflets' inner side or inaccurately depicted due to its transparency. Here, we address this issue, which allows us to focus on the accurate and automatic computation of valve orifice areas. Approach: We propose a segmentation approach based on the detection of the leaflets' free edges. Using video frames recorded with a high-speed digital camera during in vitro simulations, an initial estimation of the orifice area is first obtained via active contouring and thresholding and then refined to capture the leaflet free edges via a curve transformation mechanism. Results: Experiments on video data from pulsatile flow testing demonstrate the effectiveness of our approach: a root-mean-square error (RMSE) on the temporal extracted orifice areas between 0.8% and 1.2%, an average Jaccard similarity coefficient between 0.933 and 0.956, and an average Hausdorff distance between 7.2 and 11.9 pixels. Conclusions: Our approach significantly outperformed a state-of-the-art algorithm in terms of evaluation metrics related to valve design (RMSE) and computer vision (accuracy of the orifice shape). It can also cope with lower quality videos and is better at processing frames showing an almost closed valve, a crucial quality for assessing valve design malfunctions related to their improper closing.