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.

Functional measurements of the left atrium (LA) in atrial fibrillation (AF) patients is limited to a single CINE slice midway through the LA.Nonetheless, a full 3D characterization of atrial functional measurements would provide more insights into LA function.But this improved modeling capacity comes at a price of requiring LA segmentation of each 3D time point,a time-consuming and expensive task that requires anatomy-specific expertise.We propose an efficient pipeline which requires ground truth segmentation of a single (or limited) CINE time point to accurately propagate it throughout the sequence.This method significantly saves human effort and enable better characterization of LA anatomy.From a gated cardiac CINE MRI sequence we select a single CINE time point with ground truth segmentation, and assuming cyclic motion, we register other images corresponding to all time points using diffeomorphic registration in ANTs.The diffeomorphic registration fields allow us to map a given anatomical shape (segmentation) to each CINE time point, facilitating the construction of a 4D shape model.

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation.

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.

There is growing interest in the assessment of function of the left atrium (LA) in patients with atrial fibrillation (AF).Existing methods of LA functional measurement only quantify a limited subset of the functional parameters from a single or biplane CINE-MRI scan through the LA.Here, we propose an image-based method for comprehensive evaluation of the function of the entire LA in 3D.4D LA images were reconstructed from a series of CINE image stack covering the whole LA with small or no gap between thin slices.A segmentation from a high-resolution Magnetic Resonance Angiography (MRA) was registered and propagated through pairwise deformable registrations covering the cardiac cycle.Volume, LA ejection fraction and surface strain were computed for each timepoint and registered to Late Gadolinium Enhancement (LGE) scans for each of 52 patient scans.A correlation coefficient of -0.11 was calculated between LGE and strain, indicating that fibrotic tissue correlates with reduced elasticity.

Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distribution is multimodal. Nonparametric density estimation is more general, but has a ravenous appetite for training samples and poses serious challenges in optimization, especially in high dimensional spaces. Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors. We use deep autoencoders to capture the complex intensity distribution while avoiding the careful selection of hand-crafted features. We formulate the shape prior as a mixture of Gaussians and learn the corresponding parameters in a high-dimensional shape space rather than pre-projecting onto a low-dimensional subspace. In segmentation, we treat the identity of the mixture component as a latent variable and marginalize it within a generalized expectation-maximization framework. We present a conditional maximization-based scheme that alternates between a closed-form solution for component-specific shape parameters that provides a global update-based optimization strategy, and an intensity-based energy minimization that translates the global notion of a nonlinear shape prior into a set of local penalties. We demonstrate our approach on the left atrial segmentation from gadolinium-enhanced MRI, which is useful in quantifying the atrial geometry in patients with atrial fibrillation.

Left atrial appendage (LAA) closure is performed in atrial fibrillation (AF) patients to help prevent stroke. LAA closure using an occlusion implant is performed under imaging guidance. However, occlusion can be a complicated process due to the highly variable and heterogeneous LAA shapes across patients. Patient-specific implant selection and insertion processes are keys to the success of the procedure, yet subjective in nature. A population study of the angle of entry at the interatrial septum relative to the appendage can assist in both catheter design and patient-specific implant choice. In our population study, we analyzed the inherent clusters of the angles that were obtained between the septum normal and the LAA ostium plane. The number of inherent angle clusters matched the LAA four morphological classifications reported in the literature. Further, our exploratory analysis revealed that the normal from the ostium plane does not intersect the septum in all the samples under study. The insights gained from this study can help assist in making objective decisions during LAA closure.

Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statistics. Nonetheless, population-level shape representations in the form of image segmentation and correspondence models derived from cardiac MRI require significant human resources with sufficient anatomy-specific expertise. In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved. We also propose a novel data augmentation scheme to effectively train a deep network in a limited training data setting. We compare this new method of estimating shape descriptors from images with the state-of-the-art correspondence-based shape modeling that requires image segmentation and correspondence optimization. Results show that the proposed method and the current state-of-the-art produce statistically similar outcomes on AF recurrence, eliminating the need for expensive pre-processing pipelines and associated human labor.

Introduction: Multiple markers left atrium (LA) remodeling, including LA shape, correlate with outcomes in atrial fibrillation (AF). Catheter ablation is an important treatment of AF, but better tools are needed to determine which patients will benefit. In this study, we use particle-based modeling to quantitatively assess LA shape, and determine to what degree it predicts AF recurrence after catheter ablation. Methods and results: There were 254 patients enrolled in the DECAAF study who underwent cardiac magnetic resonance imaging of the LA prior to AF ablation and were followed for recurrence for up to 475 days. We performed particle-based shape modeling on each patient's LA shape. We selected shape parameters using the LASSO method and factor analysis, and then added them to a Cox regression model, which included multiple clinical parameters and LA fibrosis. We computed Harrell's C-statistic with and without shape in the model. We used the model to stratify patients into recurrence risk classes by both shape and shape and fibrosis combined. Three shape parameters were selected for inclusion. The C-statistic increased from 0.68 to 0.72 when shape was added to the model (P<0.05). Visualized shapes showed that a more round LA shape with a shorter, more laterally rotated appendage was predictive of recurrence. Conclusion: LA shape is an independent predictor of recurrence after AF ablation. When combined with LA fibrosis, shape analysis using PBM may improve patient selection for ablation.

We have developed computational methods for interactively exploring the shape of the left-atrium in a population of atrial fibrillation patients. We analyze the LA shape through a shape-learning algorithm termed as particle-based modeling (PBM), in which we extract surface contours from a population of images and then parameterize population-level shape statistics through the automatic placement of a dense set of homologous landmark positions (aka correspondences) using an optimization on information content. We then generate a 2-D embedding of the resulting high-dimensional dataset which allows us to visualize the data on a scatter plot, with each data point representing a single sample. This parameterization of the shape characteristics of samples collapsed onto a single plot gives us a visual representation of the population-level morphology of the data. Cardiac MR angiography data from 212 AF patients was collected retrospectively from a database of AF patients at the University of Utah. From the 2-D scatter plot, we were able to interactively select individual samples, view their shapes, and see associated clinical data. We can also map new patients to infer their relations to other patients in the population via querying nearby samples and viewing their clinical data.

Introduction: Adequate catheter/atrial tissue contact is critical for lesion formation during radiofrequency (RF) ablation of atrial fibrillation (AF). Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is a unique tool for the evaluation of lesion formation and detection of acute esophageal injury. Methods: LGE-MRIs were obtained prior, within 24 hours of, and at 115 +/- 62 days after first AF ablation in 36 patients. The Visitag module of CARTO3 was used to collect contact force (CF) and duration from a CF sensing ablation catheter for each registered ablation point. The minimum CF resulting in permanent lesions was determined. Esophageal enhancement detected by acute LGE-MRI was classified as mild, moderate, and severe. The CF resulting in esophageal enhancement was determined. Results: A total of 4,642 registered ablation tags at 50 W power were analyzed. The mean RF duration (5.9 +/- 3.7 vs. 5.6 +/- 3.2 seconds, P < 0.05), CF (11.5 +/- 5.6 vs. 10.9 +/- 5.4 g, P < 0.001), and force time integral (FTI) (67.3 +/- 54.5 vs. 62.2 +/- 52.7 gs, P<0.01) were significantly higher between ablation tagswith and without associated LGE-MRI detected scar. The mean CF(15.7 +/- 6.1 vs. 12.6 +/- 5.9 g, P < 0.05, n = 17 patients) in areas of esophageal enhancement was greater than areas without. Conclusion: Left atrial short duration ablation lesions with a CF greater than 12 g are more likely to be associated with permanent lesion formation. Ablating on top of the esophagus, CF less than 15 g would help minimize esophageal wall injury.

OBJECTIVES The purpose of this study is to evaluate the spatial distribution of late gadolinium enhancement (LGE) of the left atrium (LA) by LGE-magnetic resonance imaging in an atrial fibrillation (AF) population. BACKGROUND LGE of the LA can be a surrogate of pre-existing structural remodeling of LA. METHODS LGE-magnetic resonance imaging scans were used for 160 patients with AF (mean age 66 +/- 11 years) before AF ablation. To know the spatial distribution of LGE, the extent of LGE in 6 LA subregions was examined. Overall LGE distribution was also summarized as a spatial frequency histogram using an atlas of LA shape. These data were also compared between paroxysmal AF (87 patients) and persistent AF (73 patients). RESULTS LGE coverage (%) in each subregion was as follows: 41.8 +/- 18.9% in the left pulmonary vein (PV) antrum, 27.1 +/- 16.7% in the left lateral wall, 25.8 +/- 15.3% in the posterior wall, 19.7 +/- 15.3% in the anterior wall, 17.1 +/- 15.0% in the right PV antrum, and 12.0 +/- 13.2% in the septum wall. LGE was heterogeneously distributed in the LA and was found with the highest frequency in the posterior wall near the inferior left PV antrum by the LGE histogram. A comparison of paroxysmal AF with persistent AF suggests that LGE was more expected in persistent AF compared with paroxysmal AF, particularly with a spread on the posterior and the anterior wall. CONCLUSIONS LGE in the LA was heterogeneously distributed. LGE was highly distributed in the inferior left PV antrum near the posterior wall side, and spread on the posterior and anterior wall with AF progression. (C) 2018 by the American College of Cardiology Foundation.

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.

Discectomy procedure simulations require patient-specific and robust three-dimensional representation of vertebral and intervertebral disc structures, as well as existing pathology, of the lumbar spine. Prior knowledge, such as expected shape and variation within a sample population, can be incorporated through statistical shape models to optimize the image segmentation process. This paper describes a framework for construction of statistical shape models (SSMs) of nine L1 vertebrae and eight L1-L2 intervertebral discs from computed tomography and magnetic resonance (MR) images respectively. The generated SSMs are utilized as a reference for knowledge-based priors to optimize coarse-to-fine multi-surface segmentation of vertebrae and intervertebral discs in volumetric MR images. Correspondence between instances within each model has been established using entropy-based energy minimization of particles on the image surfaces, which is independent of any reference bias or surface parameterization techniques. The resulting shape models faithfully capture variability within the first seven principal modes.

Purpose Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. Methods In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. Results We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. Conclusions Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.

Methods Eighty-eight patients (58% male, mean age 60.4+/-14.7) with AF who underwent late gadolinium enhancement MRI (LGE-MRI) to assess the degree of atrial tissue fibrotic changes were included in this study. All patients underwent at least 2 or more LGE-MRI separated by more than 3 months of follow up. Progression of fibrosis was defined as an increase in fibrosis area by more than 5% (Figure 1). Demographic patient data as well as comorbidities and medications were collected from chart revisions.