Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures

Biplane 3D overlay guidance for congenital heart disease to assist cardiac catheterization interventions-A pilot study

Cardiac catheterization for congenital heart disease (CHD) performed under fluoroscopic guidance still lacks definition and requires exposure to ionizing radiation and contrast agents, with most patients needing multiple procedures through their lifetime, leading to cumulative radiation risks. While fusion overlay techniques have been employed in the past to aid, these have been limited to a single plane, while interventions are traditionally performed under biplane fluoroscopy. We describe our initial experience performing cardiac catheterizations guided by an enhanced biplane GuideCCI system© (Siemens Healthcare, Germany) augmented by 3D magnetic resonance imaging and computed tomography modeling. Twenty-one children and young adults with CHD undergoing catheterization procedures between October 2019 and May 2021 were chosen based on their degree of complexity of cardiac anatomy. 3D stereolithography models were generated, overlayed, and displayed in real time, alongside angiographs in both planes on the screen during these procedures. We report successful implementation of this novel technology for performance of 26 interventions including stent placements, balloon dilations, vessel occlusion and percutaneous valve and transvenous pacemaker implantation all in patients with various complex cardiac anatomies. A statistically significant reduction in radiation and contrast use was noted for coarctation of the aorta stent angioplasty and transcatheter pulmonary valve replacement when compared with national benchmarks and local institutional metrics (with and without single plane overlay). No complications were encountered with the use of this technology. Use of a tracheal registration technique provided very good correlation in most cases. Operators preferred using biplane augmented catheterization over traditional fluoroscopy in patients with complex cardiac anatomy undergoing interventions.

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

OBJECTIVE

Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion. We investigate the feasibility of motion detection between two fluoroscopic frames by applying a convolutional neural network (CNN). Its integration in the existing open-source software framework 3D-XGuide is demonstrated, such extending its functionality to automatic motion detection and compensation.

METHODS

The CNN is trained on reference data generated from tracking of the rapid pacing catheter tip by applying template matching with normalized cross-correlation (CC). The developed CNN motion compensation model is packaged in a standalone web service, allowing for independent use via a REST API. For testing and demonstration purposes, we have extended the functionality of 3D-XGuide navigation framework by an additional motion compensation module, which uses the displacement predictions of the standalone CNN model service for motion compensation of the static 3D model overlay. We provide the source code on GitHub under BSD license.

RESULTS

The performance of the CNN motion compensation model was evaluated on a total of 1690 fluoroscopic image pairs from ten clinical datasets. The CNN model-based motion compensation method clearly overperformed the tracking of the rapid pacing catheter tip with CC with prediction frame rates suitable for live application in the clinical setting.

CONCLUSION

A novel CNN model-based method for automatic motion compensation during fusion of 3D anatomic models with XR fluoroscopy is introduced and its integration with a real software application demonstrated. Automatic motion extraction from 2D XR images using a CNN model appears as a substantial improvement for reliable augmentation during catheter interventions.

An Adaptive Respiratory Motion Compensation Algorithm with Singular Value Decomposition for Intracardiac Catheter Tracking. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:5065-5068. [PMID: 33019125 DOI: 10.1109/embc44109.2020.9176152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

During radiofrequency catheterization for atrial fibrillation, how to accurately obtain non-X-ray intracardiac catheter position is crucial to successful endocardial mapping and ablation treatment. The major limitation of the cost-effective intracardiac catheter tracking with transthoracic electrical-fields is that the distribution of electrical conductivity within the volume torso remains dynamics and nonlinear and changes with the patient's respiratory motion. Studies have shown respiratory motion-induced catheter localization error over 20 mm. In this study, we present a novel adaptive respiratory motion compensation algorithm with singular value decomposition for reducing the interference of respiration to ensure the accuracy of intracardiac catheter localization. Animal experiments in swine were carried out for assessing the performance of the propose method through a comparison with a traditional filtering method. The obtained results demonstrate that the proposed adaptive filter based on the SVD performed well to track the original information of catheter position by accurately and timely removing the respiratory interference in case of either a fast- or slow- moving catheter operation. Future applications of this algorithm would be potentially useful for intracardiac catheter localization and real-time tracking.

A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures

Purpose

Catheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X‐ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction.

Methods

For the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire‐like structures in the X‐ray images. After applying adaptive binarization method, the centerlines of wire‐like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire‐like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire‐like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases.

Results

Detection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%.

Conclusion

The proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real‐time. The accuracy of the proposed framework is sub‐mm and the methods are robust toward low‐dose X‐ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.

A Hidden Markov Model for 3D Catheter Tip Tracking With 2D X-ray Catheterization Sequence and 3D Rotational Angiography

In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.

GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions

BACKGROUND AND OBJECTIVES

The use of pre-procedural magnetic resonance (MR) roadmap images for interventional guidance has limited anatomical accuracy due to intra-procedural respiratory motion of the heart. Therefore, the objective of this study is to explore the use of a rapidly updated dynamic motion model to correct for respiratory motion induced errors during MRI-guided cardiac interventions. The motivation for the proposed technique is to improve the accuracy of MRI guidance by taking advantage of the anatomical context provided by the high resolution prior images and the respiratory motion information present in a series of realtime MR images.

METHODS

We implemented a GPU accelerated image registration algorithm to derive the respiratory motion information and used the resulting transformation parameters to update an adaptive motion model once every heart cycle. In the subsequent heart cycle, the dynamic motion model could be used to predict the respiratory motion and provide a motion estimate to realign the prior volume with the realtime MR image. This iterative update and prediction process is then continuously repeated.

RESULTS

The GPU accelerated image registration algorithm could be completed in an average of 176.9 ± 14.0 ms, which is 139× faster than a CPU implementation. Thus, it was feasible to update the dynamic model once every heart cycle. The proposed dynamic model was also able to improve the registration accuracy from 86.0 ± 7.5% to 93.0 ± 3.3% in case of variable breathing patterns, as evaluated by the dice similarity coefficient of the left ventricular border overlap between the prior and realtime images.

CONCLUSIONS

The feasibility of a dynamic motion correction framework was demonstrated. The resulting improvements may lead to more accurate MRI-guided cardiac interventions in the future.

Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures

GOAL

The purpose of this study is to improve the accuracy of interventional catheter guidance during intracardiac procedures. Specifically, the use of preprocedural magnetic resonance roadmap images for interventional guidance has limited anatomical accuracy due to intraprocedural respiratory motion of the heart. Therefore, we propose to build a novel respiratory motion model to compensate for this motion-induced error during magnetic resonance imaging (MRI)-guided procedures.

METHODS

We acquire 2-D real-time free-breathing images to characterize the respiratory motion, and build a smooth motion model via registration of 3-D prior roadmap images to the real-time images within a novel principal axes frame of reference. The model is subsequently used to correct the interventional catheter positions with respect to the anatomy of the heart.

RESULTS

We demonstrate that the proposed modeling framework can lead to smoother motion models, and potentially lead to more accurate motion estimates. Specifically, MRI-guided intracardiac ablations were performed in six preclinical animal experiments. Then, from retrospective analysis, the proposed motion modeling technique showed the potential to achieve a 27% improvement in ablation targeting accuracy.

CONCLUSION

The feasibility of a respiratory motion model-based correction framework has been successfully demonstrated.

SIGNIFICANCE

The improvement in ablation accuracy may lead to significant improvements in success rate and patient outcomes for MRI-guided intracardiac procedures.

Image-based view-angle independent cardiorespiratory motion gating and coronary sinus catheter tracking for x-ray-guided cardiac electrophysiology procedures

Determination of the cardiorespiratory phase of the heart has numerous applications during cardiac imaging. In this article we propose a novel view-angle independent near-real time cardiorespiratory motion gating and coronary sinus (CS) catheter tracking technique for x-ray fluoroscopy images that are used to guide cardiac electrophysiology procedures. The method is based on learning CS catheter motion using principal component analysis and then applying the derived motion model to unseen images taken at arbitrary projections, using the epipolar constraint. This method is also able to track the CS catheter throughout the x-ray images in any arbitrary subsequent view. We also demonstrate the clinical application of our model on rotational angiography sequences. We validated our technique in normal and very low dose phantom and clinical datasets. For the normal dose clinical images we established average systole, end-expiration and end-inspiration gating success rates of 100%, 85.7%, and 92.3%, respectively. For very low dose applications, the technique was able to track the CS catheter with median errors not exceeding 1 mm for all tracked electrodes. Average gating success rates of 80.3%, 71.4%, and 69.2% were established for the application of the technique on clinical datasets, even with a dose reduction of more than 10 times. In rotational sequences at normal dose, CS tracking median errors were within 1.2 mm for all electrodes, and the gating success rate was 100%, for view angles from RAO 90° to LAO 90°. This view-angle independent technique can extract clinically useful cardiorespiratory motion information using x-ray doses significantly lower than those currently used in clinical practice.

Continuous roadmapping in liver TACE procedures using 2D-3D catheter-based registration

Purpose

Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention.

Methods

In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data.

Results

The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7–5.4 mm when using the brute force optimizer and 5.2–6.6 mm when using the Powell optimizer.

Conclusion

We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity.

PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions

Purpose

Intraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.

Methods

We propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.

Results

This breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p>0.05$$\end{document}p>0.05), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.

Conclusions

we developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.

Analysis of Left Atrial Respiratory and Cardiac Motion for Cardiac Ablation Therapy

Cardiac ablation therapy is often guided by models built from preoperative computed tomography (CT) or magnetic resonance imaging (MRI) scans. One of the challenges in guiding a procedure from a preoperative model is properly synching the preoperative models with cardiac and respiratory motion through computational motion models. In this paper, we describe a methodology for evaluating cardiac and respiratory motion in the left atrium and pulmonary veins of a beating canine heart. Cardiac catheters were used to place metal clips within and near the pulmonary veins and left atrial appendage under fluoroscopic and ultrasound guidance and a contrast-enhanced, 64-slice multidetector CT scan was collected with the clips in place. Each clip was segmented from the CT scan at each of the five phases of the cardiac cycle at both end-inspiration and end-expiration. The centroid of each segmented clip was computed and used to evaluate both cardiac and respiratory motion of the left atrium. A total of three canine studies were completed, with 4 clips analyzed in the first study, 5 clips in the second study, and 2 clips in the third study. Mean respiratory displacement was 0.2±1.8 mm in the medial/lateral direction, 4.7±4.4 mm in the anterior/posterior direction (moving anterior on inspiration), and 9.0±5.0 mm superior/inferior (moving inferior with inspiration). At end inspiration, the mean left atrial cardiac motion at the clip locations was 1.5±1.3 mm in the medial/lateral direction, and 2.1±2.0 mm in the anterior/posterior and 1.3±1.2 mm superior/inferior directions. At end expiration, the mean left atrial cardiac motion at the clip locations was 2.0±1.5 mm in the medial/lateral direction, 3.0±1.8 mm in the anterior/posterior direction, and 1.5±1.5 mm in the superior/inferior directions.

Characterization of respiratory and cardiac motion from electro-anatomical mapping data for improved fusion of MRI to left ventricular electrograms

Accurate fusion of late gadolinium enhancement magnetic resonance imaging (MRI) and electro-anatomical voltage mapping (EAM) is required to evaluate the potential of MRI to identify the substrate of ventricular tachycardia. However, both datasets are not acquired at the same cardiac phase and EAM data is corrupted with respiratory motion limiting the accuracy of current rigid fusion techniques. Knowledge of cardiac and respiratory motion during EAM is thus required to enhance the fusion process. In this study, we propose a novel approach to characterize both cardiac and respiratory motion from EAM data using the temporal evolution of the 3D catheter location recorded from clinical EAM systems. Cardiac and respiratory motion components are extracted from the recorded catheter location using multi-band filters. Filters are calibrated for each EAM point using estimates of heart rate and respiratory rate. The method was first evaluated in numerical simulations using 3D models of cardiac and respiratory motions of the heart generated from real time MRI data acquired in 5 healthy subjects. An accuracy of 0.6–0.7 mm was found for both cardiac and respiratory motion estimates in numerical simulations. Cardiac and respiratory motions were then characterized in 27 patients who underwent LV mapping for treatment of ventricular tachycardia. Mean maximum amplitude of cardiac and respiratory motion was 10.2±2.7 mm (min = 5.5, max = 16.9) and 8.8±2.3 mm (min = 4.3, max = 14.8), respectively. 3D Cardiac and respiratory motions could be estimated from the recorded catheter location and the method does not rely on additional imaging modality such as X-ray fluoroscopy and can be used in conventional electrophysiology laboratory setting.

Integration of cardiac and respiratory motion into MRI roadmaps fused with x-ray

PURPOSE

Volumetric roadmaps overlaid on live x-ray fluoroscopy may be used to enhance image guidance during interventional procedures. These roadmaps are often static and do not reflect cardiac or respiratory motion. In this work, the authors present a method for integrating cardiac and respiratory motion into magnetic resonance imaging (MRI)-derived roadmaps to fuse with live x-ray fluoroscopy images, and this method was tested in large animals.

METHODS

Real-time MR images were used to capture cardiac and respiratory motion. Nonrigid registration was used to calculate motion fields to deform a reference end-expiration, end-diastolic image to different cardiac and respiratory phases. These motion fields were fit to separate affine motion models for the aorta and proximal right coronary artery. Under x-ray fluoroscopy, an image-based navigator and ECG signal were used as inputs to deform the roadmap for live overlay. The in vivo accuracy of motion correction was measured in four swine as the ventilator tidal volume was varied.

RESULTS

Motion correction reduced the root-mean-square error between the roadmaps and manually drawn centerlines, even under high tidal volume conditions. For the aorta, the error was reduced from 2.4 ± 1.5 mm to 2.2 ± 1.5 mm (p < 0.05). For the proximal right coronary artery, the error was reduced from 8.8 ± 16.2 mm to 4.3 ± 5.2 mm (p < 0.001). Using real-time MRI and an affine motion model it is feasible to incorporate physiological cardiac and respiratory motion into MRI-derived roadmaps to provide enhanced image guidance for interventional procedures.

CONCLUSIONS

A method has been presented for creating dynamic 3D roadmaps that incorporate cardiac and respiratory motion. These roadmaps can be overlaid on live X-ray fluoroscopy to enhance image guidance for cardiac interventions.