Aortic Shape Research Articles

Generative 3D Cardiac Shape Modelling for in-silico Trials.

We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution.

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area (δ𝒮 vs. ) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in δ𝒮 closely parallels the true geometric progression of aortic disease patients.

Abstract 2024: Elucidating The Role Of Shape In Aortic Disease State Pathology With Machine Learning

Maximum aortic diameter has been the mainstay metric for aortic disease management and surgical intervention for decades, specifically acute aortic dissection and aneurysm. While this size metric has been effective at separating patient populations into groupings associated with required intervention and monitoring for eventual intervention respectively, size alone ignores the role of complex geometric shape and how it may be further indicative of disease state. Analyses of aortic shape in addition to size have been discussed at length in literature, however the representations and methods to convey shape are siloed and often highly subjective. Machine learning methodologies offer a means to explore high dimensional feature spaces and can identify a sparse set of metrics which result in a division of patient responses parametrized by physical and interpretable variables. Additionally, by introducing a modeling workflow which contains engineered parameters designed to describe aortic shape, in addition to traditional size metrics, the exploration of shape dependence on intervention outcomes becomes more comprehensive across size-shape parameter spaces. The engineered parameters presented primarily incorporate estimations of integrated Gaussian surface curvature into features that contain both the magnitude and spatial variation in shape of the aortas. The models discussed accurately classify over two hundred aortic disease patients spanning aneurysm, dissection, and other modalities with accuracies above 90% and performance across multiple modeling methodologies is discussed. The classification success of the geometrically informed feature space suggests the underlying dependence on pathology shape on the success of the intervention outcome. The most clinically effective models will preserve the wealth of information in modern medical diagnostics and imaging into descriptive, and physically interpretable, parameters to aid in clinical decision making.

Aortic geometry and long-term outcome in patients with a repaired coarctation

ObjectiveThis study aims to compare aortic morphology between repaired coarctation patients and controls, and to identify aortic morphological risk factors for hypertension and cardiovascular events (CVEs) in coarctation patients.MethodsRepaired coarctation...

Abstract 3008: Aortic Arch Endovascular Stent Grafts With Zone 0 Proximal Landing Zones For Geometrically Complex Aortas

Objectives: Fluctuation in total curvature (δK) significantly outperforms all other available measurements of shape in predicting clinical outcomes after thoracic endovascular aortic repair (TEVAR). We aim to identify if the Nexus® Aortic Arch Stent Graft System is a suitable and safe treatment modality for aortas with high δK. Methods: Twenty-five patients that underwent transcatheter aortic arch repair using the Nexus stent graft were included. 3D aortic models were segmented from the CTA image data using Simpleware ScanIP. The aorta segmentations were then smoothed and the outer surface isolated. A triangular mesh from the outer surface was generated in Python, and the mesh was divided into a number of partitions. δK was calculated by the sum of per partition total curvatures and the fluctuation in total curvature across the entire manifold surface. Failed TEVAR was defined by a) any device-related mortality or b) unplanned or device-related secondary surgical intervention 30 days from index TEVAR. This data was plotted into a pre-defined (δK,L –1 )-feature space with pre-established machine learning-derived boundary conditions. Results: Within a (δK,L –1 )-feature space defined by patients with aortic stent grafts with zone 2 or 3 proximal landing zones, our group used predictive modeling to classify patients into three zones (normal, successful TEVAR, and unsuccessful TEVAR) with 92.8% mean accuracy. Within this pre-defined boundary zone of unsuccessful TEVAR, 72.2% (13/18) of patients with the Nexus stent graft with zone 0 proximal landing zone underwent a successful TEVAR without need for re-intervention. Conclusions: Transcatheter aortic arch repair with a single branch, two-stent graft system with a zone 0 proximal landing zone can offer patients with a high degree of aortic shape fluctuation a greater chance at successful TEVAR than traditional aortic stent grafts with zone 2 or 3 landing zones.

Kiosk 3R-TB-03 - Association Between Aortic Shape, Energy Loss, and Exercise Capacity in Repaired Tetralogy of Fallot Patients

Kiosk 3R-TB-03 - Association Between Aortic Shape, Energy Loss, and Exercise Capacity in Repaired Tetralogy of Fallot Patients

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Objective:ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. Material and methods:70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results:the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion:global shape features might provide an important contribution for predicting the aneurysm growth.

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

Aortic shape variation after frozen elephant trunk procedure predicts aortic events: Principal component analysis study

ObjectiveThe frozen elephant trunk procedure is a well-established technique for the repair of type A ascending aortic dissection and complex aortic arch pathology. The ultimate shape created by the repair may have consequences in long-term complications. The purpose of this study was to apply a machine learning technique to comprehensively describe 3-dimensional aortic shape variations after the frozen elephant trunk procedure and associate these variations with aortic events. MethodsComputed tomography angiography acquired before discharge of patients (n = 93) who underwent the frozen elephant trunk procedure for type A ascending aortic dissection or ascending aortic arch aneurysm was preprocessed to yield patient-specific aortic models and centerlines. Aortic centerlines were subjected to principal component analysis to describe principal components and aortic shape modulators. Patient-specific shape scores were correlated with outcomes defined by composite aortic event, including aortic rupture, aortic root dissection or pseudoaneurysm, new type B dissection, new thoracic or thoracoabdominal pathologies, residual descending aortic dissection with residual false lumen flow, or thoracic endovascular aortic repair complications. ResultsThe first 3 principal components accounted for 36.4%, 26.4%, and 11.6% of aortic shape variance, respectively, and cumulatively for 74.5% of the total shape variation in all patients. The first principal component described variation in arch height-to-length ratio, the second principal component described angle at the isthmus, and the third principal component described variation in anterior-to-posterior arch tilt. Twenty-one aortic events (22.6%) were encountered. The degree of aortic angle at the isthmus described by the second principal component was associated with aortic events in logistic regression (hazard ratio, 0.98; 95% confidence interval, 0.97-0.99; P = .046). ConclusionsThe second principal component, describing angulation at the region of the aortic isthmus, was associated with adverse aortic events. Observed shape variation should be evaluated in the context of aortic biomechanical properties and flow hemodynamics.

Echocardiographic and radiographic aortic remodeling in cats with confirmed systemic hypertension.

Systemic hypertension (SH) in cats may occur secondary to renal disease, hyperthyroidism, or idiopathic causes. Asymmetric dilation of the sinuses of Valsalva has been recognized in people with systemic hypertension as target organ damage (TOD). Aortic knob formation (lateral bowing of the aortic arch to proximal descending aorta on the posteroanterior/anteroposterior radiographic projection) is recognized as TOD in people due to SH. None of these changes have been reported in feline patients. The first objective of this retrospective case-controlled study was to compare echocardiographic changes in the aorta of 76 cats with systemic hypertension (SH) to those seen in 76 cats with normal blood pressure (NBP). Our second objective was to have blinded reviewers assess heart size and aortic shape and size from available thoracic radiographs of 49/76 cats with SH and 46/76 cats with NBP. A two-way unpaired t-test with significance set at alpha=0.01 was used to evaluate aortic echocardiographic parameters. The mean of the aortic diameter and length of each aortic cusp was significantly larger in cats with SH than in cats with NBP. The aortic cusps were considered altered in size in 62 of 76 cats with SH, 12/62 ≥ with 0.5mm, and 50/62 ≥ 1.0mm. No significant difference in the vertebral heart score (VHS) was noted between groups. The aortic knob to lateral margin of the trachea in cats with SH had an area under the curve (AUC) of 0.74 (95% CI:0.61-0.87) with best cutoff of 1.12cm with sensitivity of 81% and specificity of 69%.

Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease

Abstract Background Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodelling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). Purpose A novel methodological framework for analysing three-dimensional (3D) shape changes over time (“growth”) has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Methods Three-dimensional aortic shapes (n=94) reconstructed as surface meshes from cardiovascular magnetic resonance imaging (MRI) data represented the input for a longitudinal shape atlas model, using multiple scans over time (n=2–4 scans per patient). This model relies on diffeomorphic transformations in the absence of point-to-point correspondence, and on the correct combination of initialization, estimation, and registration parameters. Results We computed the 3D shape trajectory of an average disease progression over time in our cohort (Picture 1, grey to blue shapes), as well as time-dependent parameters, geometric variations and the average shape of the population (Picture 1, red shape). Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to investigate clinical associations and stratify patients, as such capturing aortic remodelling in the presence of BAV aortopathy. Conclusion This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): British Heart Foundation and NIHR BRC

Adverse Events Following TEVAR Correlated To Novel Shape Metric

The prophylactic treatment of uncomplicated type B aortic dissection with thoracic endovascular aneurysm repair (TEVAR) is controversial. This warrants the identification of a high-risk category of dissections that may particularly benefit from endovascular repair. Due to the aortic size paradox, maximum diameter alone is insufficient as a marker for intervention. We studied measurements of aortic shape and developed a novel metric of total curvature.

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves.

We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier–Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.

Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions.

The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.

Isolating the Effect of Arch Architecture on Aortic Hemodynamics Late After Coarctation Repair: A Computational Study.

ObjectivesEffective management of aortic coarctation (CoA) affects long-term cardiovascular outcomes. Full appreciation of CoA hemodynamics is important. This study aimed to analyze the relationship between aortic shape and hemodynamic parameters by means of computational simulations, purposely isolating the morphological variable.MethodsComputational simulations were run in three aortic models. MRI-derived aortic geometries were generated using a statistical shape modeling methodology. Starting from n = 108 patients, the mean aortic configuration was derived in patients without CoA (n = 37, “no-CoA”), with surgically repaired CoA (n = 58, “r-CoA”) and with unrepaired CoA (n = 13, “CoA”). As such, the aortic models represented average configurations for each scenario. Key hemodynamic parameters (i.e., pressure drop, aortic velocity, vorticity, wall shear stress WSS, and length and number of strong flow separations in the descending aorta) were measured in the three models at three time points (peak systole, end systole, end diastole).ResultsComparing no-CoA and CoA revealed substantial differences in all hemodynamic parameters. However, simulations revealed significant increases in vorticity at the site of CoA repair, higher WSS in the descending aorta and a 12% increase in power loss, in r-CoA compared to no-CoA, despite no clinically significant narrowing (CoA index >0.8) in the r-CoA model.ConclusionsSmall alterations in aortic morphology impact on key hemodynamic indices. This may contribute to explaining phenomena such as persistent hypertension in the absence of any clinically significant narrowing. Whilst cardiovascular events in these patients may be related to hypertension, the role of arch geometry may be a contributory factor.

Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research. Thus, there is a high demand to estimate the individual shape of inassessible structures given only information on the geometry of the surrounding tissue. This leads to a domain adaptation problem, where the domain gap could be very large while typically only small datasets are available. Hence, classical approaches for domain adaptation are not capable of providing sufficient predictions. In this work, we present a new framework for bridging this domain gap in the scope of estimating anatomical shapes based on the surrounding tissue's morphology. Thus, we propose deep representation learning to not map from one image to another but to predict a latent shape representation. We formalize this framework and present two different approaches to solve the given problem. Furthermore, we perform a proof-of-concept study for estimating the individual shape of the aortic valve leaflets based on a volumetric ultrasound image of the aortic root. Therefore, we collect an ex-vivo porcine data set consisting of both, ultrasound volume images as well as high-resolution leaflet images, evaluate both approaches on it and perform an analysis of the model's hyperparameters. Our results show that using deep representation learning and domain mapping between the identified latent spaces, a robust prediction of the unknown leaflet shape only based on surrounding tissue information is possible, even in limited data scenarios. The concept can be applied to a wide range of modeling tasks, not only in the scope of heart modeling but also for all kinds of inassessible structures within the human body.

Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease

Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodeling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). A novel methodological framework for three-dimensional shape analysis has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Three-dimensional aortic shapes (n = 94) reconstructed from cardiovascular magnetic resonance imaging (MRI) data as surface meshes represented the input for a longitudinal atlas model, using multiple scans over time (n = 2–4 per patient). This model relies on diffeomorphism transformations in the absence of point-to-point correspondence, and on the right combination of initialization, estimation and registration parameters. We computed the shape trajectory of an average disease progression in our cohort, as well as time-dependent parameters, geometric variations and the average shape of the population. Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to run clinical associations. This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD.

Magnetic resonance angiography derived predictors of progressive dilatation and surgery of the aortic root in Marfan syndrome.

BackgroundTo identify magnetic resonance (MR) angiography derived predictors of progressive dilatation and surgery of the aortic root in Marfan syndrome.Material and methodsWe retrospectively included 111 patients (32.7±16.5 years, range: 7–75 years) with a total of 446 MR angiographies. Aortic diameter growth rates of the entire thoracic aorta and Z-scores were estimated from annual diameter measurements. Aortic root shape was subdivided into three different types: (T0) normal; (T1) localized dilatation; (T2) generalized aortic root dilatation. Aortic diameter, Z-score, age, and aortic root shape at baseline were tested as predictors of aortic root dilatation using a multivariate logistic regression model.ResultsThe highest aortic growth rate was observed at the level of the sinuses of Valsalva. Higher aortic root diameters and Z-scores at baseline predicted an increased growth of the aortic root (p = 0.003 and p<0.001). Young age (<30 years) was a predictor for the increase of Z-scores when compared to patients ≥30 years (p = 0.019). 25/111 patients (22.5%) had a T0 aortic root shape, 59/111 patients (53.2%) had a T1 aortic root shape, and 27/111 patients (24.3%) had a T2 aortic root shape. Aortic root shape did not predict further aortic growth (p>0.05). However, significantly more patients undergoing surgery had a generalized aortic dilatation (19/28, 76.9%) than a localized aortic root dilatation (9/28, 32.1%) (p = 0.001).ConclusionLarger baseline aortic root diameter and Z-score as well as young age predict solely progressive aortic root dilatation in Marfan patients. MR angiography derived type of aortic root shape does not predict aortic growth, but patients with generalized aortic root dilatation are referred more frequently for aortic surgery.

An observational thoracic radiographic study of aortic remodeling in dogs with confirmed systemic hypertension.

Sustained systemic hypertension (SH) has been shown to cause target organ damage to various tissues in dogs and cats, including the aorta. Aortic dilatation occurs most commonly secondary to SH in people and develops prior to an aortic aneurysm. Our hypothesis was that blinded reviewers could be trained to recognize variable alterations of aortic shape and size on thoracic radiographs of canine patients with SH. A retrospective, observational, cross-sectional study was performed with three blinded reviewers evaluating thoracic radiographic images of 21 dogs with normal blood pressure compared to 145 dogs with system hypertension. Lateral radiographs showed variable aortic undulation and disproportionate enlargement of a portion of the aorta between the ascending and proximal descending aorta compared to the descending aorta just cranial to the diaphragm. On orthogonal projections, the aortic arch to proximal descending aorta bowed laterally similar to changes reported in people with the formation of an aortic "knob." After completing a training module, reviewers of the thoracic images had a 74% agreement with Fleiss' Kappa of 0.50 indicating moderate agreement recognizing SH changes to the thoracic aorta. The more experienced blinded reviewers had accuracies of 85% and 80% for identifying systemic hypertension, slightly better than the less experienced reviewer at 76%. The ratio of thoracic cavity width to aortic knob width was significantly different between the groups (median ratio 3.4 SH vs 4.1 normal). Evidence of target organ damage (TOD) to the thoracic aorta may prompt earlier recognition and treatment for systemic hypertension.

Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.