Deep learning method for aortic root detection

Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. Methods: A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. Results: The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. Conclusions: From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentationThis work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19)S

Unstructured hexahedral mesh generation of complex vascular trees using a multi-block grid-based approach

The trend towards realistic numerical models of (pathologic) patient-specific vascular structures brings along larger computational domains and more complex geometries, increasing both the computation time and the operator time. Hexahedral grids effectively lower the computational run time and the required computational infrastructure, but at high cost in terms of operator time and minimal cell quality, especially when the computational analyses are targeting complex geometries such as aneurysm necks, severe stenoses and bifurcations. Moreover, such grids generally do not allow local refinements. As an attempt to overcome these limitations, a novel approach to hexahedral meshing is proposed in this paper, which combines the automated generation of multi-block structures with a grid-based method. The robustness of the novel approach is tested on common complex geometries, such as tree-like structures (including trifurcations), stenoses, and aneurysms. Additionally, the performance of the generated grid is assessed using two numerical examples. In the first example, a grid sensitivity analysis is performed for blood flow simulated in an abdominal mouse aorta and compared to tetrahedral grids with a prismatic boundary layer. In the second example, the fluid-structure interaction in a model of an aorta with aortic coarctation is simulated and the effect of local grid refinement is analyzed

Vascular Modeling from Volumetric Diagnostic Data: A Review

Reconstruction of vascular trees from digital diagnostic images is a challenging task in the development of tools for simulation and procedural planning for clinical use. Improvements in quality and resolution of acquisition modalities are constantly increasing the fields of application of computer assisted techniques for vascular modeling and a lot of Computer Vision and Computer Graphics research groups are currently active in the field, developing methodologies, algorithms and software prototypes able to recover models of branches of human vascular system from different kinds of input images. Reconstruction methods can be extremely different according to image type, accuracy requirements and level of automation. Some technologies have been validated and are available on medical workstation, others have still to be validated in clinical environments. It is difficult, therefore, to give a complete overview of the different approach used and results obtained, this paper just presents a short review including some examples of the principal reconstruction approaches proposed for vascular reconstruction, showing also the contribution given to the field by the Medical Application Area of CRS4, where methods to recover vascular models have been implemented and used for blood flow analysis, quantitative diagnosis and surgical planning tools based on Virtual Reality

Numerical Insights for AAA Growth Understanding and Predicting: Morphological and Hemodynamic Risk Assessment Features and Transient Coherent Structures Uncovering

Les anévrismes de l'aorte abdominale (AAA) sont des dilatations localisées et fréquentes de l'aorte. En cas de rupture, seul un traitement immédiat peut prévenir la morbidité et la mortalité. Le diamètre maximal AAA ($D_{max}$) et la croissance sont les paramètres actuels pour évaluer le risque associé et planifier l'intervention, avec des seuils inférieurs pour les femmes. Cependant, ces critères ne sont pas personnalisés ; la rupture peut se produire à un diamètre inférieur et les patients vivre avec un AAA important. Si l'on sait que la maladie est associée à une modification de la morphologie et de la circulation sanguine, à un dépôt de thrombus intra-luminal et à des symptômes cliniques, les mécanismes de croissance ne sont pas encore entièrement compris. Dans cette étude longitudinale, une analyse morphologique et des simulations de flux sanguins sont effectuées et comparées aux sujets témoins chez 32 patients ayant reçu un diagnostic clinique d'AAA et au moins 3 tomodensitogrammes de suivi par patient. L'objectif est d'abord d'examiner quels paramètres stratifient les patients entre les groupes sains, à faible risque et à risque élevé. Les corrélations locales entre les paramètres hémodynamiques et la croissance de l'AAA sont également explorées, car la croissance hétérogène de l'AAA n'est actuellement pas comprise. Enfin, les paramètres composites sont construits à partir de données cliniques, morphologiques et hémodynamiques et de leur capacité à prédire si un patient sera soumis à un test de risque. La performance de ces modèles construits à partir de l'apprentissage supervisé est évaluée par les ROC AUC : ils sont respectivement de 0.73 ± 0.09, 0.93 ± 0.08 et 0.96 ± 0.10 . En incorporant tous les paramètres, on obtient une AUC de 0.98 ± 0.06. Pour mieux comprendre les interactions entre la croissance et la topologie de l'écoulement de l'AAA, on propose un worflow spécifique au patient pour calculer les exposants de Lyapunov en temps fini et extraire les structures lagrangiennes-cohérentes (SLC). Ce modèle de calcul a d'abord été comparé à l'imagerie par résonance magnétique (IRM) par contraste de phase 4-D chez 5 patients. Pour mieux comprendre l'impact de la topologie de l'écoulement et du transport sur la croissance de l'AAA, des SLC hyperboliques répulsives ont été calculées chez un patient au cours d'un suivi de 8 ans, avec 9 mesures morphologiques volumétriques de l'AAA par tomographie-angiographie. Les SLC ont défini les frontières du jet entrant dans l'AAA. Les domaines situés entre le SLC et le mur aortique ont été considérés comme des zones de stagnation. Leur évolution a été étudiée lors de la croissance de l'AAA. En plus des SLC hyperboliques (variétés attractives et répulsives) découvertes par FTLE, les SLC elliptiques ont également été considérées. Il s'agit de régions dominées par la rotation, ou tourbillons, qui sont de puissants outils pour comprendre les phénomènes de transport dans les AAA.Abdominal aortic aneurysms (AAA) are localized, commonly-occurring dilations of the aorta. In the event of rupture only immediate treatment can prevent morbidity and mortality. The AAA maximal diameter ($D_{max}$) and growth are the current metrics to evaluate the associated risk and plan intervention, with lower thresholds for women. However, these criteria lack patient specificity; rupture may occur at lower diameter and patients may live with large AAA. If the disease is known to be associated with altered morphology and blood flow, intra-luminal thrombus deposit and clinical symptoms, the growth mechanisms are yet to be fully understood. In this longitudinal study, morphological analysis and blood flow simulations for 32 patients with clinically diagnosed AAA and at least 3 follow-up CT-scans per patient, are performed and compared to control subjects. The aim is first to investigate which metrics stratify patients between healthy, low risk and high risk groups. Local correlations between hemodynamical metrics and AAA growth are also explored, as AAA heterogeneous growth is currently not understood. Finally, composite metrics are built from clinical, morphological, and hemodynamical data, and their ability to predict if a patient will become at risk tested. Performance of these models built from supervised learning is assessed by ROC AUCs: they are respectively, 0.73 ± 0.09, 0.93 ± 0.08 and 0.96 ± 0.10. Mixing all metrics, an AUC of 0.98 ± 0.06 is obtained. For further insights into AAA flow topology/growth interaction, a workout of patient-specific computational flow dynamics (CFD) is proposed to compute finite-time Lyapunov exponents and extract Lagrangian-coherent structures (LCS). This computational model was first compared with 4-D phase-contrast magnetic resonance imaging (MRI) on 5 patients. To better understand the impact of flow topology and transport on AAA growth, hyperbolic, repelling LCS were computed in 1 patient during 8-years follow-up, including 9 volumetric morphologic AAA measures by computed tomography-angiography (CTA). LCS defined barriers to Lagrangian jet cores entering AAA. Domains enclosed between LCS and the aortic wall were considered to be stagnation zones. Their evolution was studied during AAA growth. In addition to hyperbolic (attracting and repelling) LCS uncovered by FTLE, elliptic LCS were also considered. Those encloses rotation-dominated regions, or vortices, which are powerful tools to understand the flow transport in AAA

Incorporating the Aortic Valve into Computational Fluid Dynamics Models using Phase-Contrast MRI and Valve Tracking

The American Heart Association states about 2% of the general population have a bicuspid aortic valve (BAV). BAVs exist in 80% of patients with aortic coarctation (CoA) and likely influences flow patterns that contribute to long-term morbidity post-surgically. BAV patients tend to have larger ascending aortic diameters, increased risk of aneurysm formation, and require surgical intervention earlier than patients with a normal aortic valve. Magnetic resonance imaging (MRI) has been used clinically to assess aortic arch morphology and blood flow in these patients. These MRI data have been used in computational fluid dynamics (CFD) studies to investigate potential adverse hemodynamics in these patients, yet few studies have attempted to characterize the impact of the aortic valve on ascending aortic hemodynamics. To address this issue, this research sought to identify the impact of aortic valve morphology on hemodynamics in the ascending aorta and determine the location where the influence is negligible. Novel tools were developed to implement aortic valve morphology into CFD models and compensate for heart motion in MRI flow measurements acquired through the aortic valve. Hemodynamic metrics such as blood flow velocity, time-averaged wall shear stress (TAWSS), and turbulent kinetic energy (TKE) induced by the valve were compared to values obtained using the current plug inflow approach. The influence of heart motion on these metrics was also investigated, resulting in the underestimation of TAWSS and TKE when heart motion was neglected. CFD simulations of CoA patients exhibiting bicuspid and tricuspid aortic valves were performed in models including the aortic sinuses and patient-specific valves. Results indicated the aortic valve impacted hemodynamics primarily in the ascending aorta, with the BAV having the greatest influence along the outer right wall of the vessel. A marked increase in TKE is present in aortic valve simulations, particularly in BAV patients. These findings suggest that future CFD studies investigating altered hemodynamics in the ascending aorta should accurately replicate aortic valve morphology. Further, aortic valve disease impacts hemodynamics in the ascending aorta that may be a predictor of the development or progression of ascending aortic dilation and possible aneurysm formation in this region

Tuning of boundary conditions parameters for hemodynamics simulation using patient data

This thesis describes an engineering workflow, which allows specification of boundary conditions and 3D simulation based on clinically available patient-specific data. A review of numerical models used to describe the cardiovascular system is provided, with a particular focus on the clinical target disease chosen for the toolkit, aortic coarctation. Aorta coarctation is the fifth most common congenital heart disease, characterized by a localized stenosis of the descending thoracic aorta. Current diagnosis uses invasive pressure measurement with rare but potential complications. The principal objective of this work was to develop a tool that can be translated into the clinic, requiring minimum operator input and time, capable of returning meaningful results from data typically acquired in clinical practice. Linear and nonlinear 1D modelling approaches are described, tested against full 3D solutions derived for idealized geometries of increasing complexityand for a patient-specific aortic coarctation. The 1D linear implementation is able to represent the fluid dynamic in simple idealized benchmarks with a limited effort in terms of computational time, but in a more complex case, such as a mild aortic coarctation, it is unable to predict well 3D fluid dynamic features. On the other side, the 1D nonlinear implementation showed a good agreement when compared to 3D pressure and flow waveforms, making it suitable to estimate outflow boundary conditions for subject-specific models. A cohort of 11 coarctation patients was initially used for a preliminary analysis using 0D models of increasing complexity to examine parameters derived when tuning models of the peripheral circulation. The first circuit represents the aortic coarctation as a nonlinear resistance, using the Bernoulli pressure drop equation, without considering the effect of downstream circulation. The second circuit include a peripheral resistance and compliance, and separate ascending and descending aortic pressure responses. In the third circuit a supra-aortic Windkessel model was added in order to include the supra-aortic circulation. The analysis detailed represents a first attempt to assess the interaction between local aortic haemodynamics and subject-specific parameterization of windkessel representations of the peripheral and supra-aortic circulation using clinically measured data. From the analysis of these 0D models, it is clear that the significance of the coarctation becomes less from the simple two resistance model to the inclusion of both the peripheral and supra-aortic circulation. These results provide a context within which to interpret outcomes of the tuning process reported for a more complex model of aortic haemodynamics using 1D and 3D model approaches. Earlier developments are combined to enable a multi-scale modelling approach to simulate fluid-dynamics. This includes non-linear 1D models to derive patient-specific parameters for the peripheral and supra-aortic circulation followed by transient analysis of a coupled 3D/0D system to estimate the coarctation pressure augmentation. These predictions are compared with invasively measured catheter data and the influence of uncertainty in measured data on the tuning process is discussed. This study has demonstrated the feasibility of constructing a workflow using non-invasive routinely collected clinical data to predict the pressure gradient in coarctation patients using patient specific CFD simulation, with relatively low levels of user interaction required. The results showed that the model is not suitable for the clinical use at this stage, thus further work is required to enhance the tuning process to improve agreement with measured catheter data. Finally, a preliminary approach for the assessment of change in haemodynamics following coarctation repair, where the coarctation region is enlarged through a virtual intervention process. The CFD approach reported can be expanded to explore the sensitivity of the peak ascending aortic pressure and descending aortic flow to the aortic diameter achieved following intervention, such an analysis would provide guidance for surgical intervention to target the optimal diameter to restore peripheral perfusion and reduce cerebral hypertension

Numerical modelling of the fluid-structure interaction in complex vascular geometries

A complex network of vessels is responsible for the transportation of blood throughout the body and back to the heart. Fluid mechanics and solid mechanics play a fundamental role in this transport phenomenon and are particularly suited for computer simulations. The latter may contribute to a better comprehension of the physiological processes and mechanisms leading to cardiovascular diseases, which are currently the leading cause of death in the western world. In case these computational models include patient-specific geometries and/or the interaction between the blood flow and the arterial wall, they become challenging to develop and to solve, increasing both the operator time and the computational time. This is especially true when the domain of interest involves vascular pathologies such as a local narrowing (stenosis) or a local dilatation (aneurysm) of the arterial wall. To overcome these issues of high operator times and high computational times when addressing the bio(fluid)mechanics of complex geometries, this PhD thesis focuses on the development of computational strategies which improve the generation and the accuracy of image-based, fluid-structure interaction (FSI) models. First, a robust procedure is introduced for the generation of hexahedral grids, which allows for local grid refinements and automation. Secondly, a straightforward algorithm is developed to obtain the prestress which is implicitly present in the arterial wall of a – by the blood pressure – loaded geometry at the moment of medical image acquisition. Both techniques are validated, applied to relevant cases, and finally integrated into a fluid-structure interaction model of an abdominal mouse aorta, based on in vivo measurements