Computational analysis of aortic haemodynamics in the presence of ascending aortic aneurysm

BACKGROUND

The usefulness of numerical modelling of a patient's cardiovascular system is growing in clinical treatment. Understanding blood flow mechanics can be crucial in identifying connections between haemodynamic factors and aortic wall pathologies.

OBJECTIVE

This work investigates the haemodynamic parameters of an ascending aorta and ascending aortic aneurysm in humans.

METHODS

Two aortic models were constructed from medical images using the SimVascular software. FEM blood flow modelling of cardiac cycle was performed using CFD and CMM-FSI at different vascular wall parameters.

RESULTS

The results showed that highest blood velocity was 1.18 m/s in aorta with the aneurysm and 1.9 m/s in healthy aorta model. The largest displacements ware in the aorta with the aneurysm (0.73 mm). In the aorta with the aneurysm, time averaged WSS values throughout the artery range from 0 Pa to 1 Pa. In the healthy aorta, distribution of WSS values changes from 0.3 Pa to 0.6 Pa.

CONCLUSIONS

In the case of an ascending aortic aneurysm, the maximum blood velocity was found to be 1.6 times lower than in the healthy aorta. The aneurysm-based model demonstrates a 45% greater wall displacement, while the oscillatory shear index decreased by 30% compared to healthy aortic results.

Realistic boundary conditions in SimVascular through inlet catheter modeling

OBJECTIVE

This study aims at developing a pipeline that provides the capability to include the catheter effect in the computational fluid dynamics (CFD) simulations of the cardiovascular system and other human vascular flows carried out with the open-source software SimVascular. This tool is particularly useful for CFD simulation of interventional radiology procedures such as tumor embolization where estimation of a therapeutic agent distribution is of interest.

RESULTS

A pipeline is developed that generates boundary condition files which can be used in SimVascular CFD simulations. The boundary condition files are modified such that they simulate the effect of catheter presence on the flow field downstream of the inlet. Using this pipeline, the catheter flow, velocity profile, radius, wall thickness, and deviation from the vessel center can be defined. Since our method relies on the manipulation of the boundary condition that is imposed on the inlet, it is sensitive to the mesh density. The finer the mesh is (especially around the catheter wall), the more accurate the velocity estimations are. In this study, we also utilized this pipeline to qualitatively investigate the effect of catheter presence on the flow field in a truncated right hepatic arterial tree of a liver cancer patient.

Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling

PURPOSE

We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data.

METHODS

We formulate vessel lumen detection as a regression task using a polar coordiantes representation.

RESULTS

Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy.

CONCLUSION

By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.

Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device

OBJECTIVE

Aortic incompetence (AI) is observed to be accelerated in the continuous-flow left ventricular assist device (LVAD) population and is related to increased mortality. Using computational fluid dynamics (CFD), we investigated the hemodynamic conditions related to the orientation of the LVAD outflow in these patients.

METHOD

We identified 10 patients with new aortic regurgitation, and 20 who did not, after LVAD implantation between 2009 and 2018. Three-dimensional models of patients' aortas were created from their computed tomography scans. The geometry of the LVAD outflow graft in relation to the aorta was quantified using azimuth angles (AA), polar angles (PAs), and distance from aortic root. The models were used to run CFD simulations, which calculated the pressures and wall shear stress (rWSS) exerted on the aortic root.

RESULTS

The AA and PA were found to be similar. However, for combinations of high values of AA and low values of PA, there were no patients with AI. The distance from aortic root to the outflow graft was also smaller in patients who developed AI (3.39 ± 0.7 vs 4.07 ± 0.77 cm, P = .04). There was no significant difference in aortic root pressures in the 2 groups. The rWSS was greater in AI patients (4.60 ± 5.70 vs 2.37 ± 1.20 dyne/cm², P < .001). Qualitatively, we observed a trend of greater perturbations, regions of high rWSS, and flow eddies in the AI group.

CONCLUSIONS

Using CFD simulations, we demonstrated that patients who developed de novo AI have greater rWSS at the aortic root, and their outflow grafts were placed closer to the aortic roots than those patients without de novo AI.

A protocol for automated a posteriori adaptive meshing with SimVascular: a test case

OBJECTIVE

Operational details regarding the use of the adaptive meshing (AM) algorithm available in the SimVascular package are scarce despite its application in several studies. Lacking these details, novice users of the AM algorithm may experience undesirable outcomes post-adaptation such as increases in mesh error metrics, unpredictable increases in mesh size, and losses in geometric fidelity. Here we present a test case using our proposed iterative protocol that will help prevent these undesirable outcomes and enhance the utility of the AM algorithm. We present three trials (conservative, moderate, and aggressive settings) applied to a scenario modelling a Fontan junction with a patient-specific geometry and physiologically realistic boundary conditions.

RESULTS

In all three trials, an overall reduction in mesh error metrics is observed (range 47%-86%). The increase in the number of elements through each adaptation never exceeded the mesh size of the pre-adaptation mesh by one order of magnitude. In all three trials, the protocol resulted in consistent, repeatable improvements in mesh error metrics, no losses of geometric fidelity and steady increments in the number of elements in the mesh. Our proposed protocol prevented the aforementioned undesirable outcomes and can potentially save new users considerable effort and computing resources.

Accelerating cardiovascular model building with convolutional neural networks

The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional neural network (FCNN) to generate 2D cardiovascular segmentations. Given vessel pathlines, the neural network generates 2D vessel enhancement images along the pathlines. Thereafter, vessel segmentations are extracted using the marching-squares algorithm, which are then used to construct 3D cardiovascular models. The neural network is trained using a novel loss function, tailored for partially labeled segmentation data. An automated quality control method is also developed, allowing promising segmentations to be selected. Compared with a threshold and level set algorithm, the FCNN method improved 2D segmentation accuracy across several metrics. The proposed quality control approach further improved the average DICE score by 25.8%. In tests with users of SimVascular, when using quality control, users accepted 80% of segmentations produced by the best performing FCNN. The FCNN cardiovascular model building method reduces the amount of manual segmentation effort required for patient-specific model construction, by as much as 73%. This leads to reduced turnaround time for cardiovascular simulations. While the method was used for cardiovascular model building, it is applicable to general tubular structures. Graphical Abstract Proposed FCNN-based cardiovascular model building pipeline. a.) Image data and vessel pathline supplied by the user. b.) Path information is used to extract image pixel intensities in plane perpendicular to the vessel path. c.) 2D images extracted along vessel pathlines are input to the FCNN. d.) FCNN acts on the input images to compute local vessel enhancement images. e.) Vessel enhancement images computed by the FCNN, the pixel values are between 0 and 1 indicating vessel tissue likelihood. f.) The marching-squares algorithm is appliedto each enhanced image to extract the central vessel segmentation. g.) 2D extracted vessel surface points overlayed on original input images. h.) The 2D vessel surface points are transformed back to 3D space. i.) 3D crosssectional vessel surfaces are interpolated along the pathline to form the final vessel model.