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

• Published in: International journal for numerical methods in biomedical engineering Vol. 36; no. 5; pp. e3330 - n/a
• Main Authors: Bergersen, Aslak W., Kjeldsberg, Henrik A., Valen‐Sendstad, Kristian
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2020; Wiley Subscription Services, Inc
• Subjects: Algorithms; Bends; Bifurcations; Blood vessels; Cardiovascular diseases; Carotid artery; Computational fluid dynamics; computational geometry; Computer applications; Fluid dynamics; geometric modification; Hemodynamic responses; Hydrodynamics; Image processing; Image segmentation; Medical imaging; Morphology; Patients; patient‐specific modeling; Software; Surface roughness; synthetic data generation; uncertainty quantification; vascular morphology; Voronoi graphs; patient-specific modeling; computational geometry; uncertainty quantification; geometric modification; synthetic data generation; vascular morphology
• ISSN: 2040-7939; 2040-7947; 2040-7947
• DOI: 10.1002/cnm.3330

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 open‐source 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. We present algorithms for robust and objective modification of six morphological features in segmented models of the vasculature from patient‐specific medical images. We show qualitative and quantitative validated results and demonstrate how the algorithms can be combined with computational fluid dynamics for rigorous investigation of the link between morphology and hemodynamics. With a handful of patient‐specific models, we can mimic the variability in the extensive databases, circumventing laborious image segmentation that is a major bottleneck in image‐based computational fluid dynamics.