Computational Shape Models Characterize Shape Change of the Left Atrium in Atrial Fibrillation

History of stroke as a predictor of high left atrial fibrosis in patients with persistent atrial fibrillation-insight from the DECAAF II randomized trial

BACKGROUND

There is a strong relationship between left atrial (LA) remodeling and ischemic stroke (IS) risk in atrial fibrillation (AF) patients. The Efficacy of Delayed Enhancement MRI-Guided Ablation vs. Conventional Catheter Ablation of Atrial Fibrillation (DECAAF-II) is the biggest MRI-based, randomized, multicenter clinical trial performed on persistent AF patients. The aim of this study is to evaluate the relationship between history of stroke and atrial fibrosis in the DECAAF II population.

METHODS

Persistent AF patients who underwent Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE-MRI) were included in the study and divided into two different groups: those with a history of stroke and those without. Propensity score matching was performed to adjust for covariates. Atrial fibrosis was compared in both groups. Then, patients were divided into different fibrosis groups, using three different cut-offs of baseline atrial fibrosis: ≥ 15%, ≥ 20%, and ≥ 25%. Univariate logistic regression and adjusted multivariate analysis were performed to assess the effect of clinical characteristics and risk factors on baseline fibrosis.

RESULTS

Eight-hundred forty-three patients were recruited in DECAAF II, of whom 70 (8.3%) had a history of stroke. Patients with history of stroke had a higher prevalence of hypertension (p = 0.043), diabetes (p = 0.014), and hyperlipidemia (p = 0.001). Seventy patients with no history of strokes were matched with patients with history of stroke to adjust for covariates using propensity score analysis. Patients in the stroke group had a significantly higher level of fibrosis than those without (20.2% vs. 8.1%, p = 0.017). Increased age was a significant predictor of all three baseline fibrosis classes (≥ 15%, ≥ 20%, and ≥ 25%). Additionally, history of stroke was found to be a predictor of baseline fibrosis ≥ 25% even after adjusting for other clinical characteristics and risk factors (OR = 1.98 [1.14-3.43], p = 0.01).

CONCLUSIONS

Left atrial fibrosis level greater than 25% correlates with the history of previous stroke episodes in patients with persistent atrial fibrillation.

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways

Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological features. We propose an extension to particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest. Existing methods to define regions of interest are computationally expensive and have topological limitations. To address these shortcomings, we use mesh fields to define free-form constraints, which allow for delimiting arbitrary regions of interest on shape surfaces. Furthermore, we add a quadratic penalty method to the model optimization to enable computationally efficient enforcement of any combination of cutting-plane and free-form constraints. We demonstrate the effectiveness of this method on a challenging synthetic dataset and two medical datasets.

Statistical shape modeling of multi-organ anatomies with shared boundaries

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.

Left atrial shape is independent predictor of arrhythmia recurrence after catheter ablation for atrial fibrillation: A shape statistics study

Background

Markers of left atrial (LA) shape may improve the prediction of postablation outcomes in atrial fibrillation (AF). Correlations to LA volume and AF persistence limit their incremental value over current clinical predictors.

Objective

To develop a shape score independent from AF persistence and LA volume using shape-based statistics, and to test its ability to predict postablation outcome.

Methods

Preablation computed tomography (CT) images from 141 patients with paroxysmal (57%) or persistent (43%) AF were segmented. Deformation of an average LA shape into each patient encoded patient-specific shape. Local analysis investigates regional differences between patient groups. Linear regression was used to remove shape variations related to LA volume and AF persistence, and to build a shape score to predict postablation outcome. Cross-validation was performed to evaluate its accuracy.

Results

Ablation failure rate was 23% over a median 12-month follow-up. Regions associated with ablation failure mostly consisted of a large area on posteroinferior LA, mitral isthmus, and left inferior vein. On univariate analysis, strongest predictors were AF persistence (P = .005), LA indexed volume (P = .02), and the proposed shape score (P = .001). On multivariate analysis, all 3 were independent predictors of ablation failure, with the LA shape score showing the highest predictive value (odds ratio [OR] = 6.2 [2.5–15.8], P < .001), followed by LA indexed volume (OR = 3.1 [1.2–7.9], P = .019) and AF persistence (OR = 2.9 [1.2–7.6], P = .022).

Conclusion

Posteroinferior LA, mitral isthmus, and left inferior vein are the regions whose shape have the highest impact on outcome. LA shape predicts AF ablation failure independently from, and more accurately than, atrial volume and AF persistence.

Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation

The shape of the left atrium (LA) and left atrial appendage (LAA) have been shown to predict stroke in patients with atrial fibrillation (AF). Prior studies rely on qualitative assessment of shape, which limits reproducibility and clinical utility. Statistical shape analysis (SSA) allows for quantitative assessment of shape. We use this method to assess the shape of the LA and LAA and predict stroke in patients with AF. From a database of AF patients who had previously undergone MRI of the LA, we identified 43 patients with AF who subsequently had an ischemic stroke. We also identified a cohort of 201 controls with AF who did not have a stroke after the MRI. We performed SSA of the LA and LAA shape to quantify the shape of these structures. We found three of the candidate LAA shape parameters to be predictive of stroke, while none of the LA shape parameters predicted stroke. When the three predictive LAA shape parameters were added to a logistic regression model that included the CHA₂DS₂-VASc score, the area under the ROC curve increased from 0.640 to 0.778 (p = .003). The shape of the LA and LAA can be assessed quantitatively using SSA. LAA shape predicts stroke in AF patients, while LA shape does not. Additionally, LAA shape predicts stroke independent of CHA₂DS₂-VASc score. SSA for assessment of LAA shape may improve stroke risk stratification and clinical decision making for AF patients.

Cardiac MRI to Manage Atrial Fibrillation

AF is the most common arrhythmia in clinical practice. In addition to the severe effect on quality of life, patients with AF are at higher risk of stroke and mortality. Recent studies have suggested that atrial and ventricular substrate play a major role in the development and maintenance of AF. Cardiac MRI has emerged as a viable tool for interrogating the underlying substrate in AF patients. Its advantage includes localisation and quantification of structural remodelling. Cardiac MRI of the atrial substrate is not only a tool for management and treatment of arrhythmia, but also to individualise the prevention of stroke and major cardiovascular events. This article provides an overview of atrial imaging using cardiac MRI and its clinical implications in the AF population.

Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints

Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks. Nonetheless, DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. Hence, conveying what DeepSSM does not know, via quantifying granular estimates of uncertainty, is critical for its direct clinical application as an on-demand diagnostic tool to determine how trustworthy the model output is. Here, we propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance, and model-dependent epistemic uncertainty via a Monte Carlo dropout sampling to approximate a variational distribution over the network parameters. Experiments show an accuracy improvement over DeepSSM while maintaining the same benefits of being end-to-end with little pre-processing.

Left main bronchus as a guide for individualized transseptal puncture using a conventional fluoroscopic approach in cryoballoon ablation of atrial fibrillation

Objective:

Although imaging modalities, such as transesophageal and intracardiac echocardiography, have helped to improve the safety of atrial transseptal puncture (TSP), fluoroscopy is still traditionally and widely used in TSP. The aim of the present study was to evaluate an individual knack for TSP during cryoballoon ablation of atrial fibrillation (AF) under fluoroscopy.

Methods:

Through the prospective study of 72 cases of patients with paroxysmal or persistent AF admitted for cryoablation in our center, 46 cases using a puncture site toward the bifurcation of the left main bronchus (LMB group) and 26 cases using an anterior–inferior puncture site (AI group) were included in the study. The acute pulmonary vein (PV) isolation success rate, single-procedure success rate, and time-to-effect (TTE) between the two groups were analyzed.

Results:

All PVs were identified and successfully isolated, and there are no differences in the two groups. However, the mean TTE was shorter in the LMB group than in the AI group. Moreover, a higher single-procedure success rate was observed in the LMB group.

Conclusion:

The bifurcation of the LMB can be clearly evaluated in each patient under fluoroscopy and is an anatomical landmark for the location of the left PV. TSP guided by the LMB is a new practical method for choosing individualized transseptal sites for catheter ablation of AF, which can help to shorten TTE and procedure time.

Does Alignment in Statistical Shape Modeling of Left Atrium Appendage Impact Stroke Prediction?

Evidence suggests that the shape of left atrium appendages (LAA) is a primary indicator in predicting stroke for patients diagnosed with atrial fibrillation (AF). Statistical shape modeling tools used to represent (i.e., parameterize) the underlying LAA variability are of crucial importance to learn shape-based predictors of stroke. Most shape modeling techniques use some form of alignment either as a data pre-processing step or during the modeling step. However, the LAA is a joint anatomy along with left atrium (LA), and the relative position and alignment plays a crucial part in determining risk of stroke. In this paper, we explore different alignment strategies for statistical shape modeling and how each strategy affects the stroke prediction capability. This allows for identifying a unified approach of alignment while analyzing the LAA anatomy for stroke. Here, we study three different alignment strategies, (i) global alignment, (ii) global translational alignment and (iii) cluster based alignment. Our results show that alignment strategies that take into account LAA orientation, i.e., (ii), or the inherent natural clustering of the population under study, i.e., (iii), provide significant improvement over global alignment in both qualitative as well as quantitative measures.

Shape analysis of the basioccipital bone in Pax7-deficient mice

We compared the cranial base of newborn Pax7-deficient and wildtype mice using a computational shape modeling technology called particle-based modeling (PBM). We found systematic differences in the morphology of the basiooccipital bone, including a broadening of the basioccipital bone and an antero-inferior inflection of its posterior edge in the Pax7-deficient mice. We show that the Pax7 cell lineage contributes to the basioccipital bone and that the location of the Pax7 lineage correlates with the morphology most effected by Pax7 deficiency. Our results suggest that the Pax7-deficient mouse may be a suitable model for investigating the genetic control of the location and orientation of the foramen magnum, and changes in the breadth of the basioccipital.

Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF.