3D Catheter Visualization Using Neural Image Matching
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Solution Overview
Problem
Atrial fibrillation ablation procedures face challenges in accurately navigating an ablation catheter to the pulmonary veins due to difficulties in understanding the orientation and position of the catheter relative to the 3D anatomy of the heart, especially when using intracardiac echocardiography, requiring significant experience.
Innovation Solution
An apparatus and method utilizing neural networks to visualize the position and orientation of a catheter within a 3D model by extracting neural network encodings from query images, querying a synthetic image repository for matching images, and displaying estimated positions and orientations using a processor and memory configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If intracardiac echocardiography is used to confirm catheter positioning and anatomy, then visualization of cardiac structures is achieved, but understanding the orientation and position of the catheter relative to the 3D anatomy becomes challenging
Solution Approach 1:
The system transforms 2D intracardiac echocardiography images into 3D spatial context by mapping image features to a 3D anatomical model. This dimensional transformation allows operators to understand catheter orientation and position relative to cardiac structures by viewing the overlaid 3D visualization, which provides spatial context that 2D images alone cannot convey.
Solution Approach 2:
The system creates a virtual copy of the catheter's position and orientation by detecting features in the intracardiac echocardiography image and representing them as a 3D model overlay. This virtual catheter model is then visualized within the 3D anatomical context, allowing operators to understand spatial relationships without directly interpreting complex 2D echocardiographic views.
2Measurement precision
If traditional 3D electro-anatomical mapping is used for navigation, then catheter positioning information is obtained, but the integration with real-time echocardiographic visualization remains challenging
Solution Approach 1:
The system merges 3D electro-anatomical mapping data with intracardiac echocardiography images by detecting catheter features in the echocardiographic image and overlaying a corresponding 3D model representation. This integration combines the precise positioning information from electro-anatomical mapping with the real-time anatomical visualization from echocardiography, creating a unified navigation system.
Solution Approach 2:
The system uses an intermediary processing step that detects catheter features in the echocardiography image and translates them into 3D model coordinates. This intermediary transformation layer bridges the gap between the two different data modalities (electro-anatomical mapping and echocardiography), enabling their integration without direct complex coupling.
3Reliability
If more experience is required to perform atrial fibrillation ablation procedures, then procedural accuracy can be maintained, but procedure time and accessibility increase
Solution Approach 1:
The system provides automated visualization and guidance by automatically detecting catheter features in intracardiac echocardiography images and generating 3D model overlays without requiring manual interpretation by experienced operators. This self-service capability reduces dependence on operator experience while maintaining procedural accuracy, allowing less experienced operators to perform procedures efficiently.
Data Source
AI summary
Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated position and orientation within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a slice extracted at a specific position and orientation in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.