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Introduction

Aortic dissection (AD) is a life-threatening vascular condition with high morbidity and mortality rates. In AD, a tear in the intimal layer of the aorta leads the blood to flow within the vessel wall, resulting in two or more flow channels, the true - physiological - lumen (TL) and one or more false - pathological - lumina (FL), separated by an intimal flap (IF). The morphology of a dissected aorta is highly complex and patient-specific, oftentimes characterized by a tortuous FL interconnected with the TL via multiple IF tears. This complex environment results in abnormal hemodynamic forces (e.g. pressure and shear stresses) that drives the evolution of the disease. Currently, imaging techniques are unable to provide an accurate assessment of the in vivo hemodynamics. However, the combination of clinical images and computational fluid dynamics (CFD) can provide important prognostic insight and therefore support the clinical-decision making process of this life-threatening disease.

Description of the Workflow

A workflow for the implementation and personalization of CFD models of aortic dissections was developed. The steps involved:

1. Reconstruction of the patient-specific geometry of the aorta from CT scans with Simpleware ScanIP,
2. Tuning of the CFD-model boundary conditions in order to mimic the haemodynamic envelope of the aorta of the specific patient, and
3. Solving of the computational model and post-processing of the results in ANSYS^? software

Geometry Segmentation with Simpleware ScanIP

The DICOM files of the clinical CT scans were directly imported into Simpleware ScanIP and processed with a median filter to reduce the ¡®salt-and-pepper¡¯ noise of the images. The region of interest, represented by the dissected aorta and its main branches, was first segmented using automatic thresholding algorithms (i.e. floodfill) and then manually refined to accurately describe the IF and its tears. In order to eliminate pixilation artefacts, smoothing algorithms were applied to the resulting mask which was then cropped perpendicularly to the vessel centerlines - via an automatic tool within Simpleware ScanIP - so as to create the inflow and outflow boundaries of the CFD model. A surface model of patient-specific dissected aorta was then exported to ANSYS^? software for the set-up of the model.

CFD Model Set-up and Personalization

The CFD models featured Windkessel boundary conditions whose parameters were tuned according to the specific patient with a procedure involving lumped-parameter models of the simulated aortae. Geometrical data (i.e. length and cross-sectional area of the vessel segments) necessary for the implementation of these models were obtained with the centerline toolbox available in Simpleware ScanIP. The personalized CFD models were solved in ANSYS^? CFX and the obtained results were post-processed in ANSYS^? CFD-Post.

Results

The workflow was tested on three complex cases of AD, and the results were successfully compared against invasive blood pressure measurements, validating the simulations. Hemodynamic results (e.g. intraluminal pressures, flow partition between the lumina, wall shear-stress based indices) provided information that could not be obtained using imaging alone, giving insight into the state of the disease. It was noted that small tears in the distal intimal flap induce disturbed flow in both lumina. Moreover, oscillatory pressures across the intimal flap were often observed in proximity to the tears in the abdominal region, which could indicate a risk of dynamic obstruction of the true lumen.

Conclusions

The study presented a new approach for the implementation of personalized CFD models using non-invasive, and oftentimes minimal, datasets commonly collected for AD monitoring and demonstrated how combining commonly available clinical data with computational modelling can be a powerful tool to enhance clinical understanding of AD.