Higher-order block-structured hex meshing of tubular structures

Numerical simulations of the cardiovascular system are growing in popularity due to the increasing availability of computational power, and their proven contribution to the understanding of pathodynamics and validation of medical devices with in-silico trials as a potential future breakthrough. Such simulations are performed on volumetric meshes reconstructed from patient-specific imaging data. These meshes are most often unstructured, and result in a brutally large amount of elements, significantly increasing the computational complexity of the simulations, whilst potentially adversely affecting their accuracy. To reduce such complexity, we introduce a new approach for fully automatic generation of higher-order, structured hexahedral meshes of tubular structures, with a focus on healthy blood vessels. The structures are modeled as skeleton-based convolution surfaces. From the same skeleton, the topology is captured by a block-structure, and the geometry by a higher-order surface mesh. Grading may be induced to obtain tailored refinement, thus resolving, e.g., boundary layers. The volumetric meshing is then performed via transfinite mappings. The resulting meshes are of arbitrary order, their elements are of good quality, while the spatial resolution may be as coarse as needed, greatly reducing computing time. Their suitability for practical applications is showcased by a simulation of physiological blood flow modelled by a generalised Newtonian fluid in the human aorta

Image-based Quantification of 3D Morphology for Bifurcations in the Left Coronary Artery: Application to Stent Design

Background Improved strategies for stent‐based treatment of coronary artery disease at bifurcations require a greater understanding of artery morphology. Objective We developed a workflow to quantify morphology in the left main coronary (LMCA), left anterior descending (LAD), and left circumflex (LCX) artery bifurcations. Methods Computational models of each bifurcation were created for 55 patients using computed tomography images in 3D segmentation software. Metrics including cross‐sectional area, length, eccentricity, taper, curvature, planarity, branching law parameters, and bifurcation angles were assessed using open‐sources software and custom applications. Geometric characterization was performed by comparison of means, correlation, and linear discriminant analysis (LDA). Results Differences between metrics suggest dedicated or multistent approaches should be tailored for each bifurcation. For example, the side branch of the LCX (i.e., obtuse marginal; OM) was longer than that of the LMCA (i.e., LCXprox) and LAD (i.e., first diagonal; D1). Bifurcation metrics for some locations (e.g., LMCA Finet ratio) provide results and confidence intervals agreeing with prior findings, while revised metric values are presented for others (e.g., LAD and LCX). LDA revealed several metrics that differentiate between artery locations (e.g., LMCA vs. D1, LMCA vs. OM, LADprox vs. D1, and LCXprox vs. D1). Conclusions These results provide a foundation for elucidating common parameters from healthy coronary arteries and could be leveraged in the future for treating diseased arteries. Collectively the current results may ultimately be used for design iterations that improve outcomes following implantation of future dedicated bifurcation stents

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Bioresorbable coronary stents : non-invasive quantitative assessment of edge and intrastent plaque – a 256-slice computed tomography longitudinal study

Les bioresorbable stents (BRS), en français intitulés tuteurs coronariens biorésorbables, sont constitués d’un polymère biorésorbable, plutôt que de métal, et ne créent pas d’artéfacts métalliques significatifs en tomodensitométrie (TDM). Cela permet une meilleure évaluation de la plaque coronarienne sous ces tuteurs en TDM qu’avec les anciens tuteurs qui sont en métal. OBJECTIF: Évaluer l’évolution de la composition de la plaque, sa fraction lipidique (FL)— marqueur de vulnérabilité de la plaque, dans les 3 zones pré-tuteur (bord proximal), intra-tuteur et post-tuteur (bord distal), et le volume de la plaque entre 1 et 12 mois post-implantation de BRS. MÉTHODOLOGIE: Il s’agit d’une étude observationnelle longitudinale réalisée chez 27 patients consécutifs (âge moyen 59,7 +/- 8,6 ans) et recrutés prospectivement pour une imagerie par TDM 256-coupes à 1 et 12 mois post-implantation de BRS (35 tuteurs total). Les objectifs primaires sont: volume de plaque totale et de FL (mm3) comparés entre 1 et 12 mois. Afin de tenir compte de la corrélation intra-patient, des analyses de variance des modèles linéaires mixtes avec ou sans spline sont utilisés avec deux facteurs répétés temps et zone/bloc (1 bloc= 5 mm en axe longitudinal). La valeur % FL= volume absolu du FL/ volume total de la plaque. RÉSULTATS: Notre analyse par bloc ou par spline n’a pas démontré une différence significative dans les volumes de plaque ou des FL dans les zones pre- intra- and post-tuteur entre 1 et 12 mois. CONCLUSION: Notre étude a réussi à démontrer la faisabilité d’une analyse non-invasive quantitative répétée de la plaque coronarienne et de la lumière intra-tuteur avec l’utilisation de TDM 256 coupes. Cette étude pilote n’a pas démontré de différence significative dans les volumes des plaques et atténuation entre 1- et 12- mois de follow-up post-implantation de BRS. Notre méthode pourrait être appliquée à l’évaluation des différents structures ou profils pharmacologiques de ces tuteurs.Coronary bioresorbable stents (BRS) are made of a bioresorbable polymer rather than metal. Unlike metallic stents, BRS do not produce significant artifacts in computed tomography (CT) and are radiolucent in CT, making it possible to evaluate coronary plaque beneath an implanted stent. PURPOSE: The purpose of our study was to evaluate the volumes of plaque and low attenuation plaque components (LAP —a marker of plaque vulnerability) of pre-, intra- and post-stent plaque location between 1 and 12 months post-implantation. METHODS: In our prospective longitudinal study, we recruited 27 consecutive patients (mean age 59.7 +/- 8.6 years) with bioresorbable stents (n=35) for a 256-slice ECG-synchronized CT evaluation at 1 month and at 12 months post stent implantation. Total plaque volume (mm3) as well as absolute and relative (%) LAP volume per block in the pre-, intra- and post-stent zones were analyzed; comparison of 1 and 12 months post BRS implantation. Changes in these variables were assessed using mixed effects models with and without spline, which also accounted for correlation between repeated measurements with factors such as time and zone/block (1 block = 5 mm in longitudinal axis). The value % LAP= LAP absolute volume/ total plaque volume. RESULTS: Our block or spline model analysis showed no significant difference in plaque or LAP volumes in pre-, intra- and post-stent zones measured at 1 month and at 12 months. CONCLUSION: Our study demonstrates the feasibility of repeated non-invasive quantitative analysis of intrastent coronary plaque and in-stent lumen using a 256-channel CT scan. This pilot study did not show significant differences in plaque volume and attenuation between 1- and 12-month follow-up from stent implantation. The method we used could be applied to the evaluation of different stent structures or different pharmacological profiles of bioresorbable stents