A framework for automated and objective modification of tubular structures: Application to the internal carotid artery

Abstract

Patient-specific medical image-based computational fluid dynamics has been widely used to reveal fundamental insight into mechanisms of cardiovascular disease, for instance, correlating morphology to adverse vascular remodeling.However, segmentation of medical images is laborious, error-prone, and a bottleneck in the development of large databases that are needed to capture the natural variability in morphology. Instead, idealized models, where morphological features are parameterized, have been used to investigate the correlation with flow features, but at the cost of limited understanding of the complexity of cardiovascular flows. To combine the advantages of both approaches, we developed a tool that preserves the patient-specificness inherent in medical images while allowing for parametric alteration of the morphology. In our opensource framework morphMan we convert the segmented surface to a Voronoi diagram, modify the diagram to change the morphological features of interest, and then convert back to a new surface. In this paper, we present algorithms for modifying bifurcation angles, location of branches, cross-sectional area, vessel curvature, shape of bends, and surface roughness. We show qualitative and quantitative validation of the algorithms, performing with an accuracy exceeding 97% in general, and proof-of-concept on combining the tool with computational fluid dynamics. By combining morphMan with appropriate clinical measurements, one could explore the morphological parameter space and resulting hemodynamic response using only a handful of segmented surfaces, effectively minimizing the main bottleneck in image-based computational fluid dynamics. K E Y W O R D S

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery

“…We have included our implementation of the two landmarking algorithms in the open-source Python framework morphMan [ 30 ], which allows for manipulating morphological features in vascular geometries [ 31 ]. The morphMan framework is an extension of the vascular modeling toolkit (VMTK) [ 32 ], and inherits functionality such as computation of centerlines, curvature and torsion.…”

On the numerical treatment of viscous and convective effects in relative pressure reconstruction methods

“…Compare the boundary term in Equation (28) to the one in Equation ( 27). In the original vWERP estimator, that term is proportional to the full trace wj Γ , so that eliminating the boundary integral would require a wj Γ ¼ 0 conditionthereby also annihilating the pressure term on the left-hand side of (27).…”

“… 1 , 2 , 4 , 6 , 19 , 20 , 21 Because of the large flow velocities therein, the pressure gradient tends to be driven predominantly by convective effects. 1 On the other hand, advances in imaging techniques in the past decade have enabled accurate 4D flow measurements also in smaller vessels, 22 , 23 , 24 , 25 , 26 , 27 , 28 where viscous effects tend to be more significant. These effects may be even more pronounced in diseased arteries: for instance, typical flow structures in intracranial aneurysms resemble that of lid‐driven cavity flows with strong shear layers.…”