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Related Experiment Video

Updated: Sep 23, 2025

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

Deep learning-based framework for motion-compensated image fusion in catheterization procedures.

Ina Vernikouskaya¹, Dagmar Bertsche¹, Wolfgang Rottbauer¹

¹Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|May 16, 2022

Summary

This summary is machine-generated.

A new convolutional neural network (CNN) method automatically detects and compensates for cardiac and respiratory motion during X-ray (XR) fluoroscopy. This improves 3D anatomic model overlays for catheter interventions, outperforming traditional methods.

Keywords:

Convolutional neural network Cross-correlation Image-guided interventions Model serving Motion compensation Rapid pacing catheter

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Last Updated: Sep 23, 2025

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

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Published on: June 3, 2018

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Interventional Cardiology

Background:

Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays enhances catheterization procedures.
Cardiac and respiratory motion degrade the accuracy of these augmented fluoroscopy overlays.
Motion compensation is crucial for updating static models with real-time patient movement.

Purpose of the Study:

To investigate the feasibility of using a convolutional neural network (CNN) for motion detection between fluoroscopic frames.
To integrate this CNN-based motion detection into the 3D-XGuide open-source software framework.
To enable automatic motion detection and compensation for improved augmented fluoroscopy.

Main Methods:

A CNN was trained using reference data from catheter tip tracking via normalized cross-correlation.
The CNN motion compensation model was deployed as a standalone web service with a REST API.
The 3D-XGuide framework was extended with a module utilizing the CNN service for motion compensation of 3D model overlays.

Main Results:

The CNN motion compensation model was evaluated on 1690 fluoroscopic image pairs from ten clinical datasets.
The CNN method significantly outperformed catheter tip tracking using normalized cross-correlation.
Prediction frame rates were suitable for live clinical application.

Conclusions:

A novel CNN-based method for automatic motion compensation in augmented fluoroscopy was developed and integrated.
Automatic motion extraction from 2D XR images using CNNs offers substantial improvements for catheter interventions.
This approach enhances the reliability of 3D-XR fusion during interventional procedures.