2D to 3D fusion of echocardiography and cardiac CT for TAVR and TAVI image guidance.
This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis "Mercedes Benz" sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Azira
A
Khalil
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Amir
A
Faisal
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Khin Wee
KW
Lai
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Siew Cheok
SC
Ng
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia. liewym@um.edu.my.
Yih Miin
YM
Liew
D000368:Aged; D000369:Aged, 80 and over; D001021:Aortic Valve / Q000000981:diagnostic imaging / Q000601:surgery*; D057791:Cardiac Imaging Techniques / Q000379:methods*; D000072226:Computed Tomography Angiography / Q000379:methods*; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D025321:Surgery, Computer-Assisted / Q000379:methods*; D065467:Transcatheter Aortic Valve Replacement / Q000379:methods*
Cardiovascular; Computed tomography; Echocardiography; Fusion; Image registration