Novel System for Real-Time Integration of 3-D Echocardiography and Fluoroscopy for Image-Guided Cardiac Interventions: Preclinical Validation and Clinical Feasibility Evaluation.

Real-time imaging is required to guide minimally invasive catheter-based cardiac interventions. While transesophageal echocardiography allows for high-quality visualization of cardiac anatomy, X-ray fluoroscopy provides excellent visualization of devices. We have developed a novel image fusion system that allows real-time integration of 3-D echocardiography and the X-ray fluoroscopy.

Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions.

Purpose: X-ray fluoroscopically guided cardiac electrophysiology (EP) procedures are commonly carried out to treat patients with arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of a three-dimensional (3D) roadmap derived from preprocedural volumetric images can be used to add anatomical information. It is useful to know the position of the catheter electrodes relative to the cardiac anatomy, for example, to record ablation therapy locations during atrial fibrillation therapy.

Extended-field-of-view three-dimensional transesophageal echocardiography using image-based X-ray probe tracking.

The use of ultrasound imaging for guidance of cardiac interventional procedures is limited by the small field of view of the ultrasound volume. A larger view can be created by image-based registration of several partially overlapping volumes, but automatic registration is likely to fail unless the registration is initialized close to the volumes' correct alignment. In this article, we use X-ray images to track a transesophageal ultrasound probe and thereby provide initial position information for the registration of the ultrasound volumes.

Synthetic echocardiographic image sequences for cardiac inverse electro-kinematic learning.

In this paper, we propose to create a rich database of synthetic time series of 3D echocardiography (US) images using simulations of a cardiac electromechanical model, in order to study the relationship between electrical disorders and kinematic patterns visible in medical images. From a real 4D sequence, a software pipeline is applied to create several synthetic sequences by combining various steps including motion tracking and segmentation. We use here this synthetic database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from invariant kinematic descriptors such as local displacements or strains.