Development of a Hybrid Training Simulator for Structural Heart Disease Interventions

Abstract

Percutaneous interventions for structural heart diseases, such as transcatheter aortic valve replacement (TAVR), transcatheter mitral valve repair (TMVr), or left atrial appendage occlusion (LAAO), are rapidly growing and widely available. [1] According to the reports analyzing National Inpatient Sample (NIS) database, there were 40 005 cases of TAVR, 4195 cases of TMVr, and 7550 cases of LAAO in the United States in 2016. [2] Although these catheter-based interventions for structural heart diseases are generally safe, there are still life-threatening complications (such as aortic perforation, cardiac tamponade, or aortic root puncture) that may originate from a lack of precision in procedural steps. [3] Interventionalists performing these complex procedures should be trained more efficiently and routinely formulate comprehensive procedure planning which help optimize clinical outcomes and minimized healthcare cost burden. [4] Although medical procedures for structural heart diseases have significantly evolved in the last 20 years, most of the interventional cardiologists have learned to refine the procedural techniques on patients following initial brief training. Due to the complexity and variability of the human body, there are limitations to what can realistically be accomplished with the

A Coordinate-Regression-Based Deep Learning Model for Catheter Detection during Structural Heart Interventions

“…Despite the limitations listed above, this work provides the primary advantage of developing deep learning models that can be optimized without the tedious task of developing image masks, as typically needed by U-Net models [22]. Furthermore, these models can be used to perform catheter tracking for training purposes for custom 3D-printed heart models [15] or on commercial training systems (Biomodex Inc., Paris, France).…”

“…We collected a fluoroscopic image dataset for our custom-made, 3D-printed heart model during a mock procedure in the catheterization lab at NewYork-Presbyterian Hospital. The custom model contained a heart that was transparent under X-ray and a metal spray-painted spine that was visible under X-ray, which is the same as in our previous publication [15]. In addition, some metal spheres were present as fiducial markers, but those were not used in this study.…”

“…Our group [15][16][17], among others [18][19][20][21], have shown the promise of augmented and mixed-reality visualization to address the limitations in depth perception for cardiac interventions by using 3D holographic headsets. In order to leverage mixed-reality technology as a navigation tool for fluoroscopy-guided percutaneous procedures, catheter tracking is a cornerstone of the image guidance system.…”

“…In order to leverage mixed-reality technology as a navigation tool for fluoroscopy-guided percutaneous procedures, catheter tracking is a cornerstone of the image guidance system. In our earlier publications, we demonstrated a mixed-reality-based training simulator for structural heart disease interventions using a 3D-printed heart phantom [15]. In this work, the catheter was coupled, at three distinct locations along its distal end, with three electromagnetic (EM) sensors, each allowing the real-time simultaneous tracking of three spatial positions (X, Y, and Z) and three orientation angles (azimuth, elevation, and roll).…”

High-Fidelity Pose Estimation for Real-Time Extended Reality (XR) Visualization for Cardiac Catheterization

“…This paper delves into the technical challenges required to provide real-time catheter tracking to an extended reality (XR) system with high speed and accuracy. Such capabilities will have applications for various use in percutaneous transcatheter cardiac interventions training 9 , pre-procedural planning, intra-operative guidance 10 , and Telesurgery to enable physicians to remotely maneuver catheters 11 . Conventional training techniques, anchored in uoroscopy-guided observations and hands-on practice, pose challenges such as high costs, radiation exposure, and limited spatial comprehension for trainees [12][13][14] .…”