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

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...