A stochastic approach for automatic registration and fusion of left atrial electroanatomic maps with 3D CT anatomical images

Multi-modal characterization of the left atrium by a fully automated integration of pre-procedural cardiac imaging and electro-anatomical mapping

Background

The combination of information obtained from pre-procedural cardiac imaging and electro-anatomical mapping (EAM) can potentially help to locate new ablation targets. In this study we developed and evaluated a fully automated technique to align left atrial (LA) anatomies obtained from CT- and MRI-scans with LA anatomies obtained from EAM.

Methods

Twenty-one patients scheduled for a pulmonary vein (PV) isolation with a pre-procedural MRI were enrolled. Additionally, a recent computed tomography (CT) scan was available in 12 patients. LA anatomies were segmented from MRI-scans using ADAS-AF (Galgo Medical, Barcelona) and from the CT-scans using Slicer3D. MRI and CT anatomies were aligned with the EAM anatomy using an iterative closest plane-to-plane algorithm. Initially, the algorithm included the PVs, LA appendage and mitral valve anulus as they are the most distinctive landmarks. Subsequently, the algorithm was applied again, excluding these structures, with only three iterative steps to refine the alignment of the true LA surface. The result of the alignments was quantified by the Euclidian distance between the aligned anatomies after excluding PVs, LA appendage and mitral anulus.

Results

Our algorithm successfully aligned 20/21 MRI anatomies and 11/12 CT anatomies with the corresponding EAM anatomies. The average median residual distances were 1.9 ± 0.6 mm and 2.5 ± 0.8 mm for MRI and CT anatomies respectively. The average LA surface with a residual distance less than 5.00 mm was 89 ± 9% and 89 ± 10% for MRI and CT anatomies respectively.

Conclusion

An iterative closest plane-to-plane algorithm is a reliable method to automatically align pre-procedural cardiac images with anatomies acquired during ablation procedures.

In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology

We previously reported the feasibility and efficacy of a simulation-guided clinical catheter ablation of atrial fibrillation (AF) in an in-silico AF model. We developed a highly efficient realistic AF model reflecting the patient endocardial voltage and local conduction and tested its clinical feasibility. We acquired > 500 endocardial bipolar electrograms during right atrial pacing at the beginning of the AF ablation procedures. Based on the clinical bipolar electrograms, we generated simulated voltage maps by applying fibrosis and local activation maps adjusted for the fiber orientation. The software's accuracy (CUVIA2.5) was retrospectively tested in 17 patients and feasibility prospectively in 10 during clinical AF ablation. Results: We found excellent correlations between the clinical and simulated voltage maps (R = 0.933, p < 0.001) and clinical and virtual local conduction (R = 0.958, p < 0.001). The proportion of virtual local fibrosis was 15.4, 22.2, and 36.9% in the paroxysmal AF, persistent AF, and post-pulmonary vein isolation (PVI) states, respectively. The reconstructed virtual bipolar electrogram exhibited a relatively good similarities of morphology to the local clinical bipolar electrogram (R = 0.60 ± 0.08, p < 0.001). Feasibility testing revealed an in situ procedural computing time from the clinical data acquisition to wave-dynamics analyses of 48.2 ± 4.9 min. All virtual analyses were successfully achieved during clinical PVI procedures. We developed a highly efficient, realistic, in situ procedural simulation model reflective of individual anatomy, fiber orientation, fibrosis, and electrophysiology that can be applied during AF ablation.

Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds

OBJECTIVE

Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols.

METHODS

A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols.

RESULTS

We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals.

SIGNIFICANCE

Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.

A patient-specific mass-spring model for biomechanical simulation of aortic root tissue during transcatheter aortic valve implantation

The success of transcatheter aortic valve implantation (TAVI) is highly dependent on the prediction of the interaction between the prosthesis and the aortic root anatomy. The simulation of the surgical procedure may be useful to guide artificial valve selection and delivery, nevertheless the introduction of simulation models into the clinical workflow is often hindered by model complexity and computational burden. To address this point, we introduced a patient-specific mass-spring model (MSM) with viscous damping, as a good trade-off between simulation accuracy and time-efficiency. The anatomical model consisted of a hexahedral mesh, segmented from pre-procedural patient-specific cardiac computer tomographic (CT) images of the aortic root, including valve leaflets and attached calcifications. Nodal forces were represented by linear-elastic springs acting on edges and angles. A fast integration approach based on the modulation of nodal masses was also tested. The model was validated on seven patients, comparing simulation results with post-procedural CT images with respect to calcification and aortic wall position. The validation showed that the MSM was able to predict calcification displacement with an average accuracy of 1.72 mm and 1.54 mm for the normal and fast integration approaches, respectively. Wall displacement root mean squared error after valve expansion was about 1 mm for both approaches, showing an improved matching with respect to the pre-procedural configuration. In terms of computational burden, the fast integration approach allowed a consistent reduction of the computational times, which decreased from 36 h to 21.8 min per 100 K hexahedra. Our findings suggest that the proposed linear-elastic MSM model may provide good accuracy and reduced computational times for TAVI simulations, fostering its inclusion in clinical routines.

An automatic and accurate registration method for electro-anatomical map and CT surface

BACKGROUND

Electro-anatomical maps (EAM) and CT surface registration are widely used for catheter navigation in atrial fibrillation ablations. However, few studies have investigated the registration algorithm. Moreover, some of them are semiautomatic, so that physicians must be proficient; some are inaccurate for catheter navigation. A both automatic and accurate registration method is needed.

METHOD

A Hausdorff distance based approach (HD) was proposed for EAM/CT registration. First, using principal axes based registration, EAM/CT pairs were coarsely aligned. Then, using Hausdorff distance as the fine objective function, EAM/CT pairs were finely aligned.

RESULTS

Six real EAM/CT pairs were collected from five patients and 38 simulated pairs were generated. Each pair was aligned using Carto-Merge, a stochastic approach (SA) and HD. Considering the balance of operability, accuracy and robustness, HD obtained the best EAM/CT registration results among the three approaches.

CONCLUSION

Experiments validate that the proposed method registers EAM and CT surface both automatically and accurately.

The logical operator map identifies novel candidate markers for critical sites in patients with atrial fibrillation

The identification of suitable markers for critical patterns during atrial fibrillation (AF) may be crucial to guide an effective ablation treatment. Single parameter maps, based on dominant frequency and complex fractionated electrograms, have been proposed as a tool for electrogram-guided ablation, however the specificity of these markers is debated. Experimental studies suggest that AF critical patterns may be identified on the basis of specific rate and organization features, where rapid organized and rapid fragmented activities characterize respectively localized sources and critical substrates. In this paper we introduce the logical operator map, a novel mapping tool for a point-by-point identification and localization of AF critical sites. Based on advanced signal and image processing techniques, the approach combines in a single map electrogram-derived rate and organization features with tomographic anatomical detail. The construction of the anatomically-detailed logical operator map is based on the time-domain estimation of atrial rate and organization in terms of cycle length and wave-similarity, the logical combination of these indexes to obtain suitable markers of critical sites, and the multimodal integration of electrophysiological and anatomical information by segmentation and registration techniques. Logical operator maps were constructed in 14 patients with persistent AF, showing the capability of the combined rate and organization markers to identify with high selectivity the subset of electrograms associated with localized sources and critical substrates. The precise anatomical localization of these critical sites revealed the confinement of rapid organized sources in the left atrium with organization and rate gradients towards the surrounding tissue, and the presence of rapid fragmented electrograms in proximity of the sources. By merging in a single map the most relevant electrophysiological and anatomical features of the AF process, the logical operator map may have significant clinical impact as a direct, comprehensive tool to understand arrhythmia mechanisms in the single patient and guide more conservative, step-wise ablation.

Electroanatomic mapping and late gadolinium enhancement MRI in a genetic model of arrhythmogenic atrial cardiomyopathy

INTRODUCTION

Although atrial arrhythmias may have genetic causes, very few data are available on evaluation of the arrhythmic substrate in genetic atrial diseases in humans. In this study, we evaluate the nature and evolution of the atrial arrhythmic substrate in a genetic atrial cardiomyopathy.

METHODS AND RESULTS

Repeated electroanatomic mapping and tomographic evaluations were used to investigate the evolving arrhythmic substrate in 5 patients with isolated arrhythmogenic atrial cardiomyopathy, caused by Natriuretic Peptide Precursor A (NPPA) gene mutation. Atrial fibrosis was assessed using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). The substrate of atrial tachycardia (AT) and atrial fibrillation (AF) was biatrial dilatation with patchy areas of low voltage and atrial wall scarring (in the right atrium: 68.5% ± 6.0% and 22.2% ± 10.2%, respectively). The evolution of the arrhythmic patterns to sinus node disease with atrial standstill (AS) was associated with giant atria with extensive low voltage and atrial scarring areas (in the right atrium: 99.5% ± 0.7% and 57.5% ± 33.2%, respectively). LGE-MRI-proven biatrial fibrosis (Utah stage IV) was associated with AS. Atrial conduction was slow and heterogeneous, with lines of conduction blocks. The progressive extension and spatial distribution of the scarring/fibrosis were strictly associated with the different types of arrhythmias.

CONCLUSION

The evolution of the amount and distribution of atrial scarring/fibrosis constitutes the structural substrate for the different types of atrial arrhythmias in a pure genetic model of arrhythmogenic atrial cardiomyopathy.

Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures

PURPOSE

In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated.

METHODS

Data from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamic in vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only.

RESULTS

The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone.

CONCLUSIONS

In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy.

Anatomic localization of rapid repetitive sources in persistent atrial fibrillation: fusion of biatrial CT images with wave similarity/cycle length maps

OBJECTIVES

The aim of this study was to investigate the anatomic distribution of critical sources in patients with atrial fibrillation (AF) by fusion of biatrial computed tomography (CT) images with cycle length (CL) and wave similarity (WS) maps.

BACKGROUND

Experimental and clinical studies show that atrial fibrillation (AF) may originate from rapid and repetitive (RR) sources of activation. Localization of RR sources may be crucial for an effective ablation treatment. Atrial electrograms showing rapid and repetitive activations can be identified by combining WS and CL analysis.

METHODS

Patients with persistent AF underwent biatrial electroanatomic mapping and pre-procedural CT cardiac imaging. WS and CL maps were constructed in 17 patients by calculating the degree of repetitiveness of activation waveforms (similarity index [S]) and the cycle length at each atrial site. WS/CL maps were then integrated with biatrial 3-dimensional CT reconstructions by a stochastic approach.

RESULTS

Repetitive sources of activation (S ≥ 0.5) were present in most patients with persistent AF (94%) and were mainly located at the pulmonary veins (82% of patients), at the superior caval vein (41%), on the anterior wall of the right atrium (23%), and at the left atrial appendage (23%). Potential driver sources showing both rapid and repetitive activations (CL = 140.7 ± 25.1 ms, S = 0.65 ± 0.15) were present only in a subset of patients (65%) and were confined to the pulmonary vein region (47% of patients) and left atrial appendage (12%). Differently, the repetitive activity of the superior caval vein was characterized by a slow activation rate (CL = 184.7 ± 14.6 ms).

CONCLUSIONS

The identification and localization of RR sources is feasible by fusion of biatrial anatomic images with WS/CL maps. Potential driver sources are present only in a subset of patients with persistent AF and are mainly located in the pulmonary vein region.

A time-domain approach for the identification of atrial fibrillation drivers

The localization of atrial fibrillation (AF) driver sources, characterized by rapid and regular electrical activity, is crucial for an effective ablation treatment. This work proposes a double-criteria approach for the identification of AF drivers based on a time-domain evaluation of atrial rate and AF organization. These two features are quantified by the measurement of atrial cycle length (ACL) and wave-similarity (WS). Based on ACL/WS formalism, AF drivers can be operatively defined as sites displaying electrical activity with high-rate and high-similarity (HR AND HS). The capability of ACL/WS analysis to identify AF driver sites and distinguish them from non-critical areas is shown in representative examples. The double-criteria evaluation for the identification of AF drivers, provided by our time-domain approach, might open new perspectives for the development of electrogram-guided ablation strategies in the single patient.