Geert Gijsbers

Computed tomography-fluoroscopy overlay evaluation during catheter ablation of left atrial arrhythmia

AIMS Proper visualization of left atrial (LA) and pulmonary vein (PV) anatomy is of crucial importance during atrial fibrillation (AF) ablation. This two-centre study evaluated a new automatic computed tomography (CT)-fluoroscopy overlay system (EP navigator, Philips Medical Systems, Best, The Netherlands) and the accuracy of different registration methods. METHODS AND RESULTS Fifty-six consecutive patients (age: 56 +/- 14) with symptomatic AF underwent contrast CT of the LA/PV prior to ablation. Three registration methods were evaluated and validated by comparison with LA angiography: (i) catheter registration: the placement of catheters in identifiable anatomical structures; (ii) heart contour: based on aligning the fluoroscopy heart contours and the 3D-rendered CT volume; and (iii) spine registration: based on automatically aligning the segmented CT spine on fluoroscopy. Computed tomography segmentation was achieved in all but one patient due to motion artefacts. The mean duration of segmentation was 10 min and average registration lasted 7 min. Catheter and heart contour registration were highly accurate (discrepancy of 1.3 +/- 0.6 and 0.3 +/- 0.5 mm, respectively) when compared with spine registration (17 +/- 9 mm, P < 0.05). The EP navigator was helpful during trans-septal puncture, gave an internal view of the atria and allowed tracking of ablation lesions. CONCLUSION The EP navigator enabled accurate live integration of CT images and real-time fluoroscopy. Registration utilizing catheter placement or heart contours was stable and reliable.

Registration of 3D trans-esophageal echocardiography to x-ray fluoroscopy using image-based probe tracking

Two-dimensional (2D) X-ray imaging is the dominant imaging modality for cardiac interventions. However, the use of X-ray fluoroscopy alone is inadequate for the guidance of procedures that require soft-tissue information, for example, the treatment of structural heart disease. The recent availability of three-dimensional (3D) trans-esophageal echocardiography (TEE) provides cardiologists with real-time 3D imaging of cardiac anatomy. Increasingly X-ray imaging is now supported by using intra-procedure 3D TEE imaging. We hypothesize that the real-time co-registration and visualization of 3D TEE and X-ray fluoroscopy data will provide a powerful guidance tool for cardiologists. In this paper, we propose a novel, robust and efficient method for performing this registration. The major advantage of our method is that it does not rely on any additional tracking hardware and therefore can be deployed straightforwardly into any interventional laboratory. Our method consists of an image-based TEE probe localization algorithm and a calibration procedure. While the calibration needs to be done only once, the GPU-accelerated registration takes approximately from 2 to 15s to complete depending on the number of X-ray images used in the registration and the image resolution. The accuracy of our method was assessed using a realistic heart phantom. The target registration error (TRE) for the heart phantom was less than 2mm. In addition, we assess the accuracy and the clinical feasibility of our method using five patient datasets, two of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures. The registration results showed our technique had mean registration errors of 1.5-4.2mm and 95% capture range of 8.7-11.4mm in terms of TRE.

Clinical Evaluation of Respiratory Motion Compensation for Anatomical Roadmap Guided Cardiac Electrophysiology Procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.

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. Also, the electrode positions of the coronary sinus (CS) catheter or lasso catheter can be used for road map motion correction. METHODS In this paper, the authors present a novel unified computational framework for image-based catheter detection and tracking without any user interaction. The proposed framework includes fast blob detection, shape-constrained searching and model-based detection. In addition, catheter tracking methods were designed based on the customized catheter models input from the detection method. Three real-time detection and tracking methods are derived from the computational framework to detect or track the three most common types of catheters in EP procedures: the ablation catheter, the CS catheter, and the lasso catheter. Since the proposed methods use the same blob detection method to extract key information from x-ray images, the ablation, CS, and lasso catheters can be detected and tracked simultaneously in real-time. RESULTS The catheter detection methods were tested on 105 different clinical fluoroscopy sequences taken from 31 clinical procedures. Two-dimensional (2D) detection errors of 0.50 ± 0.29, 0.92 ± 0.61, and 0.63 ± 0.45 mm as well as success rates of 99.4%, 97.2%, and 88.9% were achieved for the CS catheter, ablation catheter, and lasso catheter, respectively. With the tracking method, accuracies were increased to 0.45 ± 0.28, 0.64 ± 0.37, and 0.53 ± 0.38 mm and success rates increased to 100%, 99.2%, and 96.5% for the CS, ablation, and lasso catheters, respectively. Subjective clinical evaluation by three experienced electrophysiologists showed that the detection and tracking results were clinically acceptable. CONCLUSIONS The proposed detection and tracking methods are automatic and can detect and track CS, ablation, and lasso catheters simultaneously and in real-time. The accuracy of the proposed methods is sub-mm and the methods are robust toward low-dose x-ray fluoroscopic images, which are mainly used during EP procedures to maintain low radiation dose.

Evaluation of a real-time hybrid three-dimensional echo and x-ray imaging system for guidance of cardiac catheterisation procedures

Minimally invasive cardiac surgery is made possible by image guidance technology. X-ray fluoroscopy provides high contrast images of catheters and devices, whereas 3D ultrasound is better for visualising cardiac anatomy. We present a system in which the two modalities are combined, with a trans-esophageal echo volume registered to and overlaid on an X-ray projection image in real-time. We evaluate the accuracy of the system in terms of both temporal synchronisation errors and overlay registration errors. The temporal synchronisation error was found to be 10% of the typical cardiac cycle length. In 11 clinical data sets, we found an average alignment error of 2.9 mm. We conclude that the accuracy result is very encouraging and sufficient for guiding many types of cardiac interventions. The combined information is clinically useful for placing the echo image in a familiar coordinate system and for more easily identifying catheters in the echo volume.

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. The system was validated in the following two stages: 1) preclinical to determine function and validate accuracy; and 2) in the clinical setting to assess clinical workflow feasibility and determine overall system accuracy. In the preclinical phase, the system was assessed using both phantom and porcine experimental studies. Median 2-D projection errors of 4.5 and 3.3 mm were found for the phantom and porcine studies, respectively. The clinical phase focused on extending the use of the system to interventions in patients undergoing either atrial fibrillation catheter ablation (CA) or transcatheter aortic valve implantation (TAVI). Eleven patients were studied with nine in the CA group and two in the TAVI group. Successful real-time view synchronization was achieved in all cases with a calculated median distance error of 2.2 mm in the CA group and 3.4 mm in the TAVI group. A standard clinical workflow was established using the image fusion system. These pilot data confirm the technical feasibility of accurate real-time echo-fluoroscopic image overlay in clinical practice, which may be a useful adjunct for real-time guidance during interventional cardiac procedures.

Image-based automatic ablation point tagging system with motion correction for cardiac ablation procedures

X-ray fluoroscopically guided cardiac ablation procedures are commonly carried out for the treatment of cardiac arrhythmias, such as atrial fibrillation (AF). X-ray images have poor soft tissue contrast and, for this reason, overlay of a 3D roadmap derived from pre-procedural volumetric image data can be used to add anatomical information. It is a requirement to determine and record the 3D positions of the ablation catheter tip in the 3D road map during AF ablation. This feature can be used as a guidance and post-procedure analysis tool. The 3D positions of the catheter tip can be calculated from biplane X-ray images and mapped to the 3D roadmap. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory and cardiac motions. As the coronary sinus (CS) catheter is not routinely altered during the procedure, tracking the CS catheter in real-time can be used as means of motion correction to improve the accuracy of registration between live X-ray images and a 3D roadmap. To achieve a fast and automatic ablation point tagging system from biplane images, we developed a novel tracking method for real-time simultaneous detection of the ablation catheter and the CS catheter from fluoroscopic X-ray images. We tested our tracking method on 1083 fluoroscopy frames from 16 patients and achieved a success rate of 97.5% and an average 2D tracking error of 0.5 mm ± 0.3 mm. In order to achieve tagging using a monoplane X-ray image system, we proposed a novel motion gating method to select a pair of images from two short image sequences acquired from two different views. Both respiratory and cardiac motion phases are matched by selecting the pair of images with the minimum reconstruction error of the CS catheter electrodes. Finally, the 3D position of the ablation catheter tip was calculated using the epipolar constraint from the multiview images. We validated our automatic ablation point tagging strategy by computing the reconstruction error of the ablation catheter tip and achieved an error of 1.1 mm ± 0.5 mm.

Cardiac unfold: a novel technique for image-guided cardiac catheterization procedures

X-ray fluoroscopically-guided cardiac catheterization procedures are commonly carried out for the treatment of cardiac arrhythmias, such as atrial fibrillation (AF) and cardiac resynchronization therapy (CRT). X-ray images have poor soft tissue contrast and, for this reason, overlay of a 3D roadmap derived from pre-procedure volumetric image data can be used to add anatomical information. However, current overlay technologies have the limitation that 3D information is displayed on a 2D screen. Therefore, it is not possible for the cardiologist to appreciate the true positional relationship between anatomical/functional data and the position of the interventional devices. We prose a navigation methodology, called cardiac unfold, where an entire cardiac chamber is unfolded from 3D to 2D along with all relevant anatomical and functional information and coupled to real-time device tracking. This would allow more intuitive navigation since the entire 3D scene is displayed simultaneously on a 2D plot. A real-time unfold guidance platform for CRT was developed, where navigation is performed using the standard AHA 16-segment bulls-eye plot for the left ventricle (LV). The accuracy of the unfold navigation was assessed in 13 patient data sets by computing the registration errors of the LV pacing lead electrodes and was found to be 2.2 ± 0.9 mm. An unfold method was also developed for the left atrium (LA) using trimmed B-spline surfaces. The method was applied to 5 patient data sets and its utility was demonstrated for displaying information from delayed enhancement MRI of patients that had undergone radio-frequency ablation.

Comparing image-based respiratory motion correction methods for anatomical roadmap guided cardiac electrophysiology procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. Xray images have poor soft tissue contrast and, for this reason, overlay of static 3D roadmaps derived from pre-procedural volumetric data can be used to add anatomical information. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory motion. Three methods were evaluated to correct for respiratory motion using features in the Xray image data. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3D model derived from pre-operative image data. The third method is based on tracking the coronary sinus (CS) catheter. All three methods were applied to Xray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking; 1.9 mm ± 1.0 mm for trachea tracking and 1.8 mm ± 0.9 mm for CS catheter tracking. We also present a comparison between our techniques with other published image-based motion correction strategies.