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

Abstract

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.

Metaball skinning of synthetic astroglial morphologies into realistic mesh models for in silico simulations and visual analytics

“…The decimation does not impact the simulation mesh; it is merely used to create low-tessellated visualization-specific meshes where we can load thousands of them in a single scene to visualize a full circuit. This does not mean that those meshes are inaccurate; even with a decimation factor of 0.1, the RMS value of the Hausdorff distance ( Ghaffari et al , 2018 ) is ∼0.03, which is within acceptable range as shown in Supplementary Figure S40 .…”

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“…As mentioned in Ghaffari's paper [29], "HD suffers from the so-called panhandle problem, which occurs when one object exhibiting a sudden local shape deviation causes an unrealistically large HD value." In order to prevent the panhandle problem, we prefer to use the Dice coefficient.…”

“…In order to prevent the panhandle problem, we prefer to use the Dice coefficient. However, the Dice similarity has another problem, as mentioned by Ghaffari et al [29]: "this assessment does not allow direct comparison over different length-scales". In other words, the same Dice coefficient value for a large area does not have the same accuracy as in a small area.…”

“…As mentioned in Ghaffari’s paper [ 29 ], “HD suffers from the so-called panhandle problem, which occurs when one object exhibiting a sudden local shape deviation causes an unrealistically large HD value.” In order to prevent the panhandle problem, we prefer to use the Dice coefficient. However, the Dice similarity has another problem, as mentioned by Ghaffari et al [ 29 ]: “this assessment does not allow direct comparison over different length-scales”. In other words, the same Dice coefficient value for a large area does not have the same accuracy as in a small area.…”

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