Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Statistical shape representation of the thoracic aorta: accounting for major branches of the aortic arch

Statistical shape modeling (SSM) is an emerging tool for risk assessment of thoracic aortic aneurysm. However, the head branches of the aortic arch are often excluded in SSM. We introduced an SSM strategy based on principal component analysis that accounts for aortic branches and applied it to a set of patient scans. Computational fluid dynamics were performed on the reconstructed geometries to identify the extent to which branch model accuracy affects the calculated wall shear stress (WSS) and pressure. Surface-averaged and location-specific values of pressure did not change significantly, but local WSS error was high near branches when inaccurately modeled.

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning

OBJECTIVE

In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure.

METHODS

Automatic segmentation of the aorta was performed with methods based on deep learning and multi-atlas using the systolic phase in the 4D flow MRI magnitude image of 36 patients. Using mesh morphing, isotopological meshes were generated, and CFD was performed to calculate the aortic wall pressure. Node-to-node comparisons of the pressure results were made to identify the most robust automatic method respect to the pressures obtained with a manually segmented model.

RESULTS

Deep learning approach presented the best segmentation performance with a mean Dice similarity coefficient and a mean Hausdorff distance (HD) equal to 0.92+/- 0.02 and 21.02+/- 24.20 mm, respectively. At the global level HD is affected by the performance in the abdominal aorta. Locally, this distance decreases to 9.41+/- 3.45 and 5.82+/- 6.23 for the ascending and descending thoracic aorta, respectively. Moreover, with respect to the pressures from the manual segmentations, the differences in the pressures computed from deep learning were lower than those computed from multi-atlas method.

CONCLUSION

To reduce biases in the calculation of aortic wall pressure, accurate segmentation is needed, particularly in regions with high blood flow velocities. Thus, the deep learning segmen-tation method should be preferred.

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.

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

Objective

The 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.

Methods

Computed 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.

Results

The 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).

Conclusions

The 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.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

BACKGROUND AND OBJECTIVE

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA.

METHODS

Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated.

RESULTS

Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors.

CONCLUSIONS

We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

Analysing functional implications of differences in left ventricular morphology using statistical shape modelling

Functional implications of left ventricular (LV) morphological characterization in congenital heart disease are not widely explored. This study qualitatively and quantitatively assessed LV shape associations with a) LV function and b) thoracic aortic morphology in patients with aortic coarctation (CoA) with/without bicuspid aortic valve (BAV), and healthy controls. A statistical shape modelling framework was employed to analyse three-dimensional (3D) LV shapes from cardiac magnetic resonance (CMR) data in isolated CoA (n = 25), CoA + BAV (n = 30), isolated BAV (n = 30), and healthy controls (n = 25). Average 3D templates and deformations were computed. Correlations between shape data and CMR-derived morphometric parameters (i.e., sphericity, conicity) or global and apical strain values were assessed to elucidate possible functional implications. The relationship between LV shape features and arch architecture was also explored. The LV template was shorter and more spherical in CoA patients. Sphericity was overall associated with global and apical radial (p = 0.001, R² = 0.09; p < 0.0001, R² = 0.17) and circumferential strain (p = 0.001, R² = 0.10; p = 0.04, R² = 0.04), irrespective of the presence of aortic stenosis and/or regurgitation and controlling for age and hypertension status. LV strain was not associated with arch architecture. Differences in LV morphology were observed between CoA and BAV patients. Increasing LV sphericity was associated with reduced strain, independent of aortic arch architecture and functional aortic valve disease.

Existing and Emerging Approaches to Risk Assessment in Patients with Ascending Thoracic Aortic Dilatation

Ascending thoracic aortic aneurysm is a life-threatening disease, which is difficult to detect prior to the occurrence of a catastrophe. Epidemiology patterns of ascending thoracic aortic dilations/aneurysms remain understudied, whereas the risk assessment of it may be improved. The electronic databases PubMed/Medline 1966–2022, Web of Science 1975–2022, Scopus 1975–2022, and RSCI 1994–2022 were searched. The current guidelines recommend a purely aortic diameter-based assessment of the thoracic aortic aneurysm risk, but over 80% of the ascending aorta dissections occur at a size that is lower than the recommended threshold of 55 mm. Moreover, a 55 mm diameter criterion could exclude a vast majority (up to 99%) of the patients from preventive surgery. The authors review several visualization-based and alternative approaches which are proposed to better predict the risk of dissection in patients with borderline dilated thoracic aorta. The imaging-based assessments of the biomechanical aortic properties, the Young’s elastic modulus, the Windkessel function, compliance, distensibility, wall shear stress, pulse wave velocity, and some other parameters have been proposed to improve the risk assessment in patients with ascending thoracic aortic aneurysm. While the authors do not argue for shifting the diameter threshold to the left, they emphasize the need for more personalized solutions that integrate the imaging data with the patient’s genotypes and phenotypes in this heterogeneous pathology.

A Population-Based 3D Atlas of the Pathological Lumbar Spine Segment

The spine is the load-bearing structure of human beings and may present several disorders, with low back pain the most frequent problem during human life. Signs of a spine disorder or disease vary depending on the location and type of the spine condition. Therefore, we aim to develop a probabilistic atlas of the lumbar spine segment using statistical shape modeling (SSM) and then explore the variability of spine geometry using principal component analysis (PCA). Using computed tomography (CT), the human spine was reconstructed for 24 patients with spine disorders and then the mean shape was deformed upon specific boundaries (e.g., by ±3 or ±1.5 standard deviation). Results demonstrated that principal shape modes are associated with specific morphological features of the spine segment such as Cobb’s angle, lordosis degree, spine width and height. The lumbar spine atlas here developed has evinced the potential of SSM to investigate the association between shape and morphological parameters, with the goal of developing new treatments for the management of patients with spine disorders.

The Use of Digital Coronary Phantoms for the Validation of Arterial Geometry Reconstruction and Computation of Virtual FFR

We present computational fluid dynamics (CFD) results of virtual fractional flow reserve (vFFR) calculations, performed on reconstructed arterial geometries derived from a digital phantom (DP). The latter provides a convenient and parsimonious description of the main vessels of the left and right coronary arterial trees, which, crucially, is CFD-compatible. Using our DP, we investigate the reconstruction error in what we deem to be the most relevant way—by evaluating the change in the computed value of vFFR, which results from varying (within representative clinical bounds) the selection of the virtual angiogram pair (defined by their viewing angles) used to segment the artery, the eccentricity and severity of the stenosis, and thereby, the CFD simulation’s luminal boundary. The DP is used to quantify reconstruction and computed haemodynamic error within the VIRTUheartTM software suite. However, our method and the associated digital phantom tool are readily transferable to equivalent, clinically oriented workflows. While we are able to conclude that error within the VIRTUheartTM workflow is suitably controlled, the principal outcomes of the work reported here are the demonstration and provision of a practical tool along with an exemplar methodology for evaluating error in a coronary segmentation process.