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A Hybrid Catheter Localisation Framework in Echocardiography Based on Electromagnetic Tracking and Deep Learning
Fei Jia1, Shu Wang2, V T Pham3
1Faculty of Natural, Mathematical and Engineering Sciences, King's College London, London, UK.
Insights
This study introduces a hybrid framework combining electromagnetic tracking and deep learning for precise catheter tip localization during interventional cardiology procedures, enhancing safety and reliability in ultrasound imaging.
Area of Science:
- Interventional Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Interventional cardiology utilizes minimally invasive techniques for cardiovascular diseases.
- Ultrasound imaging (echocardiography) is crucial for monitoring catheter placement.
- Accurate medical device localization is vital for procedural safety and reliability.
Purpose of the Study:
- To develop an automatic hybrid framework for precise catheter tip localization.
- To overcome limitations of standalone external device tracking and image-based tracking methods.
Main Methods:
- A hybrid framework integrating an electromagnetic tracking system (North Digital Inc) and a deep learning-based ultrasound image analysis (UNet) was developed.
- The electromagnetic tracking system provided precise external localization of the catheter tip.
- UNet performed automatic semantic segmentation of the catheter tip in ultrasound images.
Main Results:
- The hybrid framework successfully combined precise external localization with automatic image-based segmentation.
- This approach offers a novel solution for identifying moving medical devices in low-resolution ultrasound images.
- The integrated system enhances the accuracy and automation of catheter tip localization.
Conclusions:
- The proposed hybrid localization framework effectively integrates electromagnetic tracking and deep learning for interventional cardiology.
- This method improves the safety and reliability of catheter-based procedures by accurately identifying the catheter tip.
- The framework presents a significant advancement for real-time device tracking in challenging ultrasound environments.
Abstract:
Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases, and aortic diseases. Ultrasound imaging, also called echocardiography, is a typical imaging tool that monitors catheter puncturing. Localising a medical device accurately during cardiac interventions can help improve the procedure's safety and reliability under ultrasound imaging. However, external device tracking and image-based tracking methods can only provide a partial solution. Thus, we proposed a hybrid framework, with the combination of both methods to localise the catheter tip target in an automatic way. The external device used was an electromagnetic tracking system from North Digital Inc (NDI), and the ultrasound image analysis was based on UNet, a deep learning network for semantic segmentation. From the external method, the tip's location was determined precisely, and the deep learning platform segmented the exact catheter tip automatically. This novel hybrid localisation framework combines the advantages of external electromagnetic (EM) tracking and the deep learning-based image method, which offers a new solution to identify the moving medical device in low-resolution ultrasound images.
