Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics

• Published in: Computers in biology and medicine Vol. 100; pp. 209 - 220
• Main Authors: Ghaffari, Mahsa, Sanchez, Lea, Xu, Guoren, Alaraj, Ali, Zhou, Xiaohong Joe, Charbel, Fady T., Linninger, Andreas A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2018; Elsevier Limited
• Subjects: Angiography; Arteries; Bifurcations; Blood vessels; Cerebral arterial tree; Cerebral Arteries - diagnostic imaging; Cerebral Arteries - physiopathology; Cerebral blood flow; Cerebrovascular Circulation; Cerebrovascular system; Computational grids; Computer applications; Computer Simulation; Confidence; Correlation analysis; Data banks; Finite element method; Hausdorff distance; Hemodynamics; Human subjects; Humans; Image processing; Image reconstruction; Image segmentation; Magnetic fields; Magnetic resonance; Magnetic Resonance Angiography; Mathematical models; Mesh generation; Mesh validation; Model accuracy; Models, Cardiovascular; Morphological analysis; Morphology; Parametric structured mesh; Pointwise surface distance; Shape similarity index; Similarity measures; Statistical analysis; Time dependence; Trees; Pointwise surface distance; Shape similarity index; Morphological analysis; Mesh validation; Hausdorff distance; Cerebral arterial tree; Parametric structured mesh
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.07.004

Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation. [Display omitted] •Automatic parametric mesh generation method has been validated using statistical analysis and modified Hausdorff distance.•We reconstructed arterial trees meshes and validated the centerline and diameter accuracy of them against raw images.•Accurate computation is essential for high-fidelity CFD simulation, hemodynamic risk, and morphological analysis.