Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography

Subject-specific one-dimensional fluid dynamics model of chronic thromboembolic pulmonary hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) develops due to the accumulation of blood clots in the lung vasculature that obstructs flow and increases pressure. The mechanobiological factors that drive progression of CTEPH are not understood, in part because mechanical and hemodynamic changes in the small pulmonary arteries due to CTEPH are not easily measurable. Using previously published hemodynamic measurements and imaging from a large animal model of CTEPH, we applied a subject-specific one-dimensional (1D) computational fluid dynamic (CFD) approach to investigate the impact of CTEPH on pulmonary artery stiffening, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) in extralobar (main, right, and left) pulmonary arteries and intralobar (distal to the extralobar) arteries. Our results demonstrate that CTEPH increases pulmonary artery wall stiffness and decreases TAWSS in extralobar and intralobar arteries. Moreover, CTEPH increases the percentage of the intralobar arterial network with both low TAWSS and high OSI, quantified by the novel parameter φ , which is related to thrombogenicity. Our analysis reveals a strong positive correlation between increases in mean pulmonary artery pressure (mPAP) and φ from baseline to CTEPH in individual subjects, which supports the suggestion that increased φ drives disease severity. This subject-specific experimental-computational framework shows potential as a predictor of the impact of CTEPH on pulmonary arterial hemodynamics and pulmonary vascular mechanics. By leveraging advanced modeling techniques and calibrated model parameters, we predict spatial distributions of flow and pressure, from which we can compute potential physiomarkers of disease progression. Ultimately, this approach can lead to more spatially targeted interventions that address the needs of individual CTEPH patients.

Deep learning-based workflow for hip joint morphometric parameter measurement from CT images

Objective.Precise hip joint morphometry measurement from CT images is crucial for successful preoperative arthroplasty planning and biomechanical simulations. Although deep learning approaches have been applied to clinical bone surgery planning, there is still a lack of relevant research on quantifying hip joint morphometric parameters from CT images.Approach.This paper proposes a deep learning workflow for CT-based hip morphometry measurement. For the first step, a coarse-to-fine deep learning model is designed for accurate reconstruction of the hip geometry (3D bone models and key landmark points). Based on the geometric models, a robust measurement method is developed to calculate a full set of morphometric parameters, including the acetabular anteversion and inclination, the femoral neck shaft angle and the inclination, etc. Our methods were validated on two datasets with different imaging protocol parameters and further compared with the conventional 2D x-ray-based measurement method.Main results. The proposed method yields high bone segmentation accuracies (Dice coefficients of 98.18% and 97.85%, respectively) and low landmark prediction errors (1.55 mm and 1.65 mm) on both datasets. The automated measurements agree well with the radiologists' manual measurements (Pearson correlation coefficients between 0.47 and 0.99 and intraclass correlation coefficients between 0.46 and 0.98). This method provides more accurate measurements than the conventional 2D x-ray-based measurement method, reducing the error of acetabular cup size from over 2 mm to less than 1 mm. Moreover, our morphometry measurement method is robust against the error of the previous bone segmentation step. As we tested different deep learning methods for the prerequisite bone segmentation, our method produced consistent final measurement results, with only a 0.37 mm maximum inter-method difference in the cup size.Significance. This study proposes a deep learning approach with improved robustness and accuracy for pelvis arthroplasty planning.

Subject-specific one-dimensional fluid dynamics model of chronic thromboembolic pulmonary hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) develops due to the accumulation of blood clots in the lung vasculature that obstruct flow and increase pressure. The mechanobiological factors that drive progression of CTEPH are not understood, in part because mechanical and hemodynamic changes in the pulmonary vasculature due to CTEPH are not easily measurable. Using previously published hemodynamic measurements and imaging from a large animal model of CTEPH, we developed a subject-specific one-dimensional (1D) computational fluid dynamic (CFD) models to investigate the impact of CTEPH on pulmonary artery stiffening, time averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). Our results demonstrate that CTEPH increases pulmonary artery wall stiffness and decreases TAWSS in extralobar (main, right and left pulmonary arteries) and intralobar vessels. Moreover, CTEPH increases the percentage of the intralobar arterial network with both low TAWSS and high OSI. This subject-specific experimental-computational framework shows potential as a predictor of the impact of CTEPH on pulmonary arterial hemodynamics and pulmonary vascular mechanics. By leveraging advanced modeling techniques and calibrated model parameters, we predict spatial distributions of flow and pressure, from which we can compute potential physiomarkers of disease progression, including the combination of low mean wall shear stress with high oscillation. Ultimately, this approach can lead to more spatially targeted interventions that address the needs of individual CTEPH patients.

Fluid-structure interaction analysis of transcatheter aortic valve implantation

Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re-stenosis 5-10 years after the implantation. Based solely on pre-implantation data, the aim of this work is to identify fluid-dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision-making phase and in the intervention design. Patient-specific, pre-implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid-structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI - three with prosthetic valve degeneration and two without degeneration - and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre-implantation data and without requiring additional peri-operative or follow-up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient-specific timing of follow-up.

A Bayesian approach to blood rheological uncertainties in aortic hemodynamics

Computational hemodynamics has received increasing attention recently. Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty quantification. In this contribution, we investigated the impact of blood rheological models on wall shear stress uncertainties in aortic hemodynamics obtained in numerical simulations. Based on shear-rheometric experiments, we compare the non-Newtonian Carreau model to a simple Newtonian model and a Reynolds number-equivalent Newtonian model. Bayesian Probability Theory treats uncertainties consistently and allows to include elusive assumptions such as the comparability of flow regimes. We overcome the prohibitively high computational cost for the simulation with a surrogate model, and account for the uncertainties of the surrogate model itself, too. We have two main findings: (1) The Newtonian models mostly underestimate the uncertainties as compared to the non-Newtonian model. (2) The wall shear stresses of specific persons cannot be distinguished due to largely overlapping uncertainty bands, implying that a more precise determination of person-specific blood rheological properties is necessary for person-specific simulations. While we refrain from a general recommendation for one rheological model, we have quantified the error of the uncertainty quantification associated with these modeling choices.

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.

Blood flow analysis with computational fluid dynamics and 4D-flow MRI for vascular diseases

Both computational fluid dynamics (CFD) and time-resolved, three-dimensional, phase-contrast, magnetic resonance imaging (4D-flow MRI) enable visualization of time-varying blood flow structures and quantification of blood flow in vascular diseases. However, they are totally different. CFD is a method to calculate blood flow by solving the governing equations of fluid mechanics, so the obtained flow field is somewhat virtual. On the other hand, 4D-flow MRI measures blood flow in vivo, thus the flow is real. Recently, with the development and enhancement of computers, medical imaging techniques, and related software, blood flow analysis has become more accessible to clinicians and its usefulness in vascular diseases has been demonstrated. In this review, we have outlined the methods and characteristics of CFD and 4D-flow MRI, respectively. We have discussed the differences in the characteristics between both methods; reviewed the milestones achieved by blood flow analysis in various vascular diseases; and discussed the usefulness, challenges, and limitations of blood flow analysis. We have discussed the difficulties and limitations of current blood flow analysis. We have also discussed our views on future directions.

Open-source analysis and visualization of segmented vasculature datasets with VesselVio

Vascular networks are fundamental components of biological systems. Quantitative analysis and observation of the features of these networks can improve our understanding of their roles in health and disease. Recent advancements in imaging technologies have enabled the generation of large-scale vasculature datasets, but barriers to analyzing these datasets remain. Modern analysis options are mainly limited to paid applications or open-source terminal-based software that requires programming knowledge with high learning curves. Here, we describe VesselVio, an open-source application developed to analyze and visualize pre-binarized vasculature datasets and pre-constructed vascular graphs. Vasculature datasets and graphs can be loaded with annotations and processed with custom parameters. Here, the program is tested on ground-truth datasets and is compared with current pipelines. The utility of VesselVio is demonstrated by the analysis of multiple formats of 2D and 3D datasets acquired with several imaging modalities, including annotated mouse whole-brain vasculature volumes.

Semi-Automatic Reconstruction of Patient-Specific Stented Coronaries based on Data Assimilation and Computer Aided Design

PURPOSE

The interplay between geometry and hemodynamics is a significant factor in the development of cardiovascular diseases. This is particularly true for stented coronary arteries. To elucidate this factor, an accurate patient-specific analysis requires the reconstruction of the geometry following the stent deployment for a computational fluid dynamics (CFD) investigation. The image-based reconstruction is troublesome for the different possible positions of the stent struts in the lumen and the coronary wall. However, the accurate inclusion of the stent footprint in the hemodynamic analysis is critical for detecting abnormal stress conditions and flow disturbances, particularly for thick struts like in bioresorbable scaffolds. Here, we present a novel reconstruction methodology that relies on Data Assimilation and Computer Aided Design.

METHODS

The combination of the geometrical model of the undeployed stent and image-based data assimilated by a variational approach allows the highly automated reconstruction of the skeleton of the stent. A novel approach based on computational mechanics defines the map between the intravascular frame of reference (called L-view) and the 3D geometry retrieved from angiographies. Finally, the volumetric expansion of the stent skeleton needs to be self-intersection free for the successive CFD studies; this is obtained by using implicit representations based on the definition of Nef-polyhedra.

RESULTS

We assessed our approach on a vessel phantom, with less than 10% difference (properly measured) vs. a customized manual (and longer) procedure previously published, yet with a significant higher level of automation and a shorter turnaround time. Computational hemodynamics results were even closer. We tested the approach on two patient-specific cases as well.

CONCLUSIONS

The method presented here has a high level of automation and excellent accuracy performances, so it can be used for larger studies involving patient-specific geometries.

Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient-specific data assimilation

Coronary artery bypass grafts (CABG) surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease. In this work, we apply a numerical optimal flow control model to patient-specific geometries of CABG, reconstructed from clinical images of real-life surgical cases, in parameterized settings. The aim of these applications is to match known physiological data with numerical hemodynamics corresponding to different scenarios, arisen by tuning some parameters. Such applications are an initial step toward matching patient-specific physiological data in patient-specific vascular geometries as best as possible. Two critical challenges that reportedly arise in such problems are: (a) lack of robust quantification of meaningful boundary conditions required to match known data as best as possible and (b) high computational cost. In this work, we utilize unknown control variables in the optimal flow control problems to take care of the first challenge. Moreover, to address the second challenge, we propose a time-efficient and reliable computational environment for such parameterized problems by projecting them onto a low-dimensional solution manifold through proper orthogonal decomposition-Galerkin.

High Channel Temperature Mapping Electronics in a Thin, Soft, Wireless Format for Non-Invasive Body Thermal Analysis

Hemodynamic status has been perceived as an important diagnostic value as fundamental physiological health conditions, including decisive signs of fatal diseases like arteriosclerosis, can be diagnosed by monitoring it. Currently, the conventional hemodynamic monitoring methods highly rely on imaging techniques requiring inconveniently large numbers of operation procedures and equipment for mapping and with a high risk of radiation exposure. Herein, an ultra-thin, noninvasive, and flexible electronic skin (e-skin) hemodynamic monitoring system based on the thermal properties of blood vessels underneath the epidermis that can be portably attached to the skin for operation is introduced. Through a series of thermal sensors, the temperatures of each subsection of the arrayed sensors are observed in real-time, and the measurements are transmitted and displayed on the screen of an external device wirelessly through a Bluetooth module using a graphical user interface (GUI). The degrees of the thermal property of subsections are indicated with a spectrum of colors that specify the hemodynamic status of the target vessel. In addition, as the sensors are installed on a soft substrate, they can operate under twisting and bending without any malfunction. These characteristics of e-skin sensors exhibit great potential in wearable and portable diagnostics including point-of-care (POC) devices.

Dynamic alterations of H3K4me3 and H3K27me3 at ADAM17 and Jagged-1 gene promoters cause an inflammatory switch of endothelial cells

Histone protein modifications control the inflammatory state of many immune cells. However, how dynamic alteration in histone methylation causes endothelial inflammation and apoptosis is not clearly understood. To examine this, we explored two contrasting histone methylations; an activating histone H3 lysine 4 trimethylation (H3K4me3) and a repressive histone H3 lysine 27 trimethylation (H3K27me3) in endothelial cells (EC) undergoing inflammation. Through computer-aided reconstruction and 3D printing of the human coronary artery, we developed a unique model where EC were exposed to a pattern of oscillatory/disturbed flow as similar to in vivo conditions. Upon induction of endothelial inflammation, we detected a significant rise in H3K4me3 caused by an increase in the expression of SET1/COMPASS family of H3K4 methyltransferases, including MLL1, MLL2, and SET1B. In contrast, EC undergoing inflammation exhibited truncated H3K27me3 level engendered by EZH2 cytosolic translocation through threonine 367 phosphorylation and an increase in the expression of histone demethylating enzyme JMJD3 and UTX. Additionally, many SET1/COMPASS family of proteins, including MLL1 (C), MLL2, and WDR5, were associated with either UTX or JMJD3 or both and such association was elevated in EC upon exposure to inflammatory stimuli. Dynamic enrichment of H3K4me3 and loss of H3K27me3 at Notch-associated gene promoters caused ADAM17 and Jagged-1 derepression and abrupt Notch activation. Conversely, either reducing H3K4me3 or increasing H3K27me3 in EC undergoing inflammation attenuated Notch activation, endothelial inflammation, and apoptosis. Together, these findings indicate that dynamic chromatin modifications may cause an inflammatory and apoptotic switch of EC and that epigenetic reprogramming can potentially improve outcomes in endothelial inflammation-associated cardiovascular diseases.

Effect of Patient-Specific Coronary Flow Reserve Values on the Accuracy of MRI-Based Virtual Fractional Flow Reserve

The purpose of this study is to investigate the effect of varying coronary flow reserve (CFR) values on the calculation of computationally-derived fractional flow reserve (FFR). CFR reflects both vessel resistance due to an epicardial stenosis, and resistance in the distal microvascular tissue. Patients may have a wide range of CFR related to the tissue substrate that is independent of epicardial stenosis levels. Most computationally based virtual FFR values such as FFR_CT do not measure patient specific CFR values but use a population-average value to create hyperemic flow conditions. In this study, a coronary arterial computational geometry was constructed using magnetic resonance angiography (MRA) data acquired in a patient with moderate CAD. Coronary flow waveforms under rest and stress conditions were acquired in 13 patients with phase-contrast magnetic resonance (PCMR) to calculate CFR, and these flow waveforms and CFR values were applied as inlet flow boundary conditions to determine FFR based on computational fluid dynamics (CFD) simulations. The stress flow waveform gave a measure of the functional significance of the vessel when evaluated with the physiologically-accurate behavior with the patient-specific CFR. The resting flow waveform was then scaled by a series of CFR values determined in the 13 patients to simulate how hyperemic flow and CFR affects FFR values. We found that FFR values calculated using non-patient-specific CFR values did not accurately predict those calculated with the true hyperemic flow waveform. This indicates that both patient-specific anatomic and flow information are required to accurately non-invasively assess the functional significance of coronary lesions.

Coronary arterial geometry: A comprehensive comparison of two imaging modalities

The characterization of vascular geometry is a fundamental step towards the correct interpretation of coronary artery disease. In this work, we report a comprehensive comparison of the geometry featured by coronary vessels as obtained from coronary computed tomography angiography (CCTA) and the combination of intravascular ultrasound (IVUS) with bi-plane angiography (AX) modalities. We analyzed 34 vessels from 28 patients with coronary disease, which were deferred to CCTA and IVUS procedures. We discuss agreement and discrepancies between several geometric indexes extracted from vascular geometries. Such an analysis allows us to understand to which extent the coronary vascular geometry can be reliable in the interpretation of geometric risk factors, and as a surrogate to characterize coronary artery disease.

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad-hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh-size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms-implemented in the open-source software vmtk-can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed-up the mesh generation process for a large range of cardiac applications, from single-chamber single-physics simulations to multi-chambers multi-physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

Path planning for endovascular catheterization under curvature constraints via two-phase searching approach

Purpose

Planning a safe path for flexible catheters is one of the major challenges of endovascular catheterization. State-of-the-art methods rarely consider the catheter curvature constraint and reduced computational time of path planning which guarantees the possibility to re-plan the path during the actual operation.

Methods

In this manuscript, we propose a fast two-phase path planning approach under the robot curvature constraint. Firstly, the vascular structure is extracted and represented by vascular centerlines and corresponding vascular radii. Then, the path is searched along the vascular centerline using breadth first search (BFS) strategy and locally optimized via the genetic algorithm (GA) to satisfy the robot curvature constraint. This approach (BFS-GA) is able to respect the robot curvature constraint while keeping it close to the centerlines as much as possible. We can also reduce the optimization search space and perform parallel optimization to shorten the computational time.

Results

We demonstrate the method’s high efficiency in two-dimensional and three-dimensional space scenarios. The results showed the planner’s ability to satisfy the robot curvature constraint while keeping low computational time cost compared with sampling-based methods. Path replanning in femoral arteries can reach an updating frequency at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.4\pm 2.3$$\end{document}6.4±2.3Hz.

Conclusion

The presented work is suited for surgical procedures demanding satisfying curvature constraints while optimizing specified criteria. It is also applicable for curvature constrained robots in narrow passages.

An image-based computational hemodynamics study of the Systolic Anterior Motion of the mitral valve

Systolic Anterior Motion (SAM) of the mitral valve - often associated with Hypertrophic Obstructive Cardiomyopathy (HOCM) - is a cardiac pathology in which a functional subaortic stenosis is induced during systole by the mitral leaflets partially obstructing the outflow tract of the left ventricle. Its assessment by diagnostic tests is often difficult, possibly underestimating its severity and thus increasing the risk of heart failure. In this paper, we propose a new computational pipeline, based on cardiac cine Magnetic Resonance Imaging (cine-MRI) data, for the assessment of SAM. The pipeline encompasses image processing of the left ventricle and the mitral valve, and numerical investigation of cardiac hemodynamics by means of Computational Fluid Dynamics (CFD) in a moving domain with image-based prescribed displacement. Patient-specific geometry and motion of the left ventricle are considered in view of an Arbitrary Lagrangian-Eulerian approach for CFD, while the reconstructed mitral valve is immersed in the computational domain by means of a resistive method. We assess clinically relevant flow and pressure indicators in a parametric study for different degrees of SAM severity, in order to provide a better quantitative evaluation of the pathological condition. Moreover, we provide specific indications for its possible surgical treatment, i.e. septal myectomy.

Gender differences of morphological and hemodynamic characteristics of abdominal aortic aneurysm

BACKGROUND

Gender difference in cardiovascular diseases (CVDs) is an important topic in the field of cardiovascular medicine. In this study, we focused on the mortality difference of abdominal aortic aneurysms (AAA), which is higher for female than that of male. The aim of this study was to verify whether morphological and hemodynamic factors play their roles in this phenomenon.

METHODS

Patient-specific AAA models of 11 females and 23 males with similar age and body mass index (BMI) have been reconstructed based on clinical computed tomography (CT) data. Firstly, the morphological parameters (diameters, curvature, intraluminal thrombus volume, etc.) of AAA models and lumbar vertebrae models were collected and analyzed. Then, based on statistical results of morphological parameters, uniformed male and female AAA models were reconstructed, and hemodynamic simulations were conducted respectively. In post-processing, the hemodynamic performances induced by gender-different morphological geometries were analyzed and compared.

RESULTS

The comparison of morphological parameters revealed that the average curvature of lumbar vertebrae and AAA centerline of female AAA models were obviously higher than that of the male. The amount of intraluminal thrombus in female AAA models was relatively lower than that of the male. According to the hemodynamic simulation, the uniform female AAA model has higher peak pressure, lower oscillatory shear stress index (OSI), and lower relative residence time (RRT) than that of the male model, all of which put female AAA to a relatively higher risk hemodynamic situation.

CONCLUSIONS

The morphological and hemodynamic features of AAA have very obvious gender differences that would induce higher risk of rupture for female AAA biomechanically. These findings would help to explore the mechanism of gender differences in AAA and draw attention to gender-specific consideration for AAA treatment. More morphological and hemodynamic indictors are suggested to be involved in the future guidelines.

Experimental Assessment of Two Non-Contrast MRI Sequences Used for Computational Fluid Dynamics: Investigation of Consistency Between Techniques

Purpose

Recent studies have noted a degree of variance between the geometries segmented by different groups from 3D medical images that are used in computational fluid dynamics (CFD) simulations of patient-specific cardiovascular systems. The aim of this study was to determine if the applied sequence of magnetic resonance imaging (MRI) also introduced observable variance in CFD results.

Methods

Using a series of phantoms MR images of vessels of known diameter were assessed for the time-of-flight and multi-echo data image combination sequences. Following this, patient images of arterio-venous fistulas were acquired using the same sequences. Comparisons of geometry were made using the phantom and patient images, and of wall shear stress quantities using the CFD results from the patient images.

Results

Phantom images showed deviations in diameter between 0 and 15% between the sequences, depending on vessel diameter. Patient images showed different geometrical features such as narrowings that were not present on both sequences. Distributions of wall shear stress (WSS) quantities differed from simulations between the geometries obtained from the sequences.

Conclusion

In conclusion, choosing different MRI sequences resulted in slightly different geometries of the same anatomy, which led to compounded errors in WSS quantities from CFD simulation.

Electronic supplementary material

The online version of this article (10.1007/s13239-020-00473-z) contains supplementary material, which is available to authorized users.

Non-invasive coronary CT angiography-derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies

Non-invasive coronary computed tomography (CT) angiography-derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced-order modelling and one based on a 3D rigid-wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced-order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced-order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced-order model did not include a lumped pressure-drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure-drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online.

Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve

In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.

Computational Fluid Dynamic Study for aTAA Hemodynamics: An Integrated Image-Based and Radial Basis Functions Mesh Morphing Approach

We present a novel framework for the fluid dynamics analysis of healthy subjects and patients affected by ascending thoracic aorta aneurysm (aTAA). Our aim is to obtain indications about the effect of a bulge on the hemodynamic environment at different enlargements. Three-dimensional (3D) surface models defined from healthy subjects and patients with aTAA, selected for surgical repair, were generated. A representative shape model for both healthy and pathological groups has been identified. A morphing technique based on radial basis functions (RBF) was applied to mold the shape relative to healthy patient into the representative shape of aTAA dataset to enable the parametric simulation of the aTAA formation. Computational fluid dynamics (CFD) simulations were performed by means of a finite volume solver using the mean boundary conditions obtained from three-dimensional (PC-MRI) acquisition. Blood flow helicity and flow descriptors were assessed for all the investigated models. The feasibility of the proposed integrated approach pertaining the coupling between an RBF morphing technique and CFD simulation for aTAA was demonstrated. Significant hemodynamic changes appear at the 60% of the bulge progression. An impingement of the flow toward the bulge was observed by analyzing the normalized flow eccentricity (NFE) index.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications.

Coronary Heart Disease Preoperative Gesture Interactive Diagnostic System Based on Augmented Reality

Coronary heart disease preoperative diagnosis plays an important role in the treatment of vascular interventional surgery. Actually, most doctors are used to diagnosing the position of the vascular stenosis and then empirically estimating vascular stenosis by selective coronary angiography images instead of using mouse, keyboard and computer during preoperative diagnosis. The invasive diagnostic modality is short of intuitive and natural interaction and the results are not accurate enough. Aiming at above problems, the coronary heart disease preoperative gesture interactive diagnostic system based on Augmented Reality is proposed. The system uses Leap Motion Controller to capture hand gesture video sequences and extract the features which that are the position and orientation vector of the gesture motion trajectory and the change of the hand shape. The training planet is determined by K-means algorithm and then the effect of gesture training is improved by multi-features and multi-observation sequences for gesture training. The reusability of gesture is improved by establishing the state transition model. The algorithm efficiency is improved by gesture prejudgment which is used by threshold discriminating before recognition. The integrity of the trajectory is preserved and the gesture motion space is extended by employing space rotation transformation of gesture manipulation plane. Ultimately, the gesture recognition based on SRT-HMM is realized. The diagnosis and measurement of the vascular stenosis are intuitively and naturally realized by operating and measuring the coronary artery model with augmented reality and gesture interaction techniques. All of the gesture recognition experiments show the distinguish ability and generalization ability of the algorithm and gesture interaction experiments prove the availability and reliability of the system.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

Aortic arch compliance and idiopathic unilateral vocal fold paralysis

Unilateral vocal fold paralysis (UVP) occurs related to recurrent laryngeal nerve (RLN) impairment associated with impaired swallowing, voice production, and breathing functions. The majority of UVP cases occur subsequent to surgical intervention with approximately 12-42% having no known cause for the disease (i.e., idiopathic). Approximately two-thirds of those with UVP exhibit left-sided injury with the average onset at ≥50 yr of age in those diagnosed as idiopathic. Given the association between the RLN and the subclavian and aortic arch vessels, we hypothesized that changes in vascular tissues would result in increased aortic compliance in patients with idiopathic left-sided UVP compared with those without UVP. Gated MRI data enabled aortic arch diameter measures normalized to blood pressure across the cardiac cycles to derive aortic arch compliance. Compliance was compared between individuals with left-sided idiopathic UVP and age- and sex-matched normal controls. Three-way factorial ANOVA test showed that aortic arch compliance (P = 0.02) and aortic arch diameter change in one cardiac cycle (P = 0.04) are significantly higher in patients with idiopathic left-sided UVP compared with the controls. As previously demonstrated by other literature, our finding confirmed that compliance decreases with age (P < 0.0001) in both healthy individuals and patients with idiopathic UVP. Future studies will investigate parameters of aortic compliance change as a potential contributor to the onset of left-sided UVP.NEW & NOTEWORTHY Unilateral vocal fold paralysis results from impaired function of the recurrent laryngeal nerve (RLN) impacting breathing, swallowing, and voice production. A large proportion of adults suffering from this disorder have an idiopathic etiology (i.e., unknown cause). The current study determined that individuals diagnosed with left-sided idiopathic vocal fold paralysis exhibited significantly greater compliance than age- and sex-matched controls. These seminal findings suggest a link between aortic arch compliance levels and RLN function.

Transversally enriched pipe element method (TEPEM): An effective numerical approach for blood flow modeling

In this work, we present a novel approach tailored to approximate the Navier-Stokes equations to simulate fluid flow in three-dimensional tubular domains of arbitrary cross-sectional shape. The proposed methodology is aimed at filling the gap between (cheap) one-dimensional and (expensive) three-dimensional models, featuring descriptive capabilities comparable with the full and accurate 3D description of the problem at a low computational cost. In addition, this methodology can easily be tuned or even adapted to address local features demanding more accuracy. The numerical strategy employs finite (pipe-type) elements that take advantage of the pipe structure of the spatial domain under analysis. While low order approximation is used for the longitudinal description of the physical fields, transverse approximation is enriched using high order polynomials. Although our application of interest is computational hemodynamics and its relevance to pathological dynamics like atherosclerosis, the approach is quite general and can be applied in any internal fluid dynamics problem in pipe-like domains. Numerical examples covering academic cases as well as patient-specific coronary arterial geometries demonstrate the potentialities of the developed methodology and its performance when compared against traditional finite element methods. Copyright © 2016 John Wiley & Sons, Ltd.

A computational framework to characterize and compare the geometry of coronary networks

This work presents a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo medical images. The methodology embraces image segmentation, arterial vessel representation, characterization and comparison, data storage, and finally analysis. Validation is performed using a sample of 48 patients. Data mining of morphometric information of several coronary arteries is presented. Results agree to medical reports in terms of basic geometric and anatomical variables. Concerning geometric descriptors, inter-artery and intra-artery correlations are studied. Data reported here can be useful for the construction and setup of blood flow models of the coronary circulation. Finally, as an application example, similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test geometric similarity among sibling patients. Results indicate that likelihood, measured through geometric descriptors, is stronger between siblings compared with non-relative patients. Copyright © 2016 John Wiley & Sons, Ltd.

Reconstruction of Intima and Adventitia Models into a State Undeformed by a Catheter by Using CT, IVUS, and Biplane X-Ray Angiogram Images

The number of studies on blood flow analysis using fluid-structure interaction (FSI) analysis is increasing. Though a 3D blood vessel model that includes intima and adventitia is required for FSI analysis, there are difficulties in generating it using only one type of medical imaging. In this paper, we propose a 3D modeling method for accurate FSI analysis. An intravascular ultrasound (IVUS) image is used with biplane X-ray angiogram images to calculate the position and orientation of the blood vessel. However, these images show that the blood vessel is deformed by the catheter inserted into the blood vessel for IVUS imaging. To eliminate such deformation, a CT image was added and the two models were registered. First, a 3D model of the undeformed intima was generated using a CT image. In the second stage, a model of intima and adventitia deformed by the catheter was generated by combining the IVUS image and the X-ray angiogram images. A 3D model of intima and adventitia with the deformation caused by insertion of the catheter eliminated was generated by matching these 3D blood vessel models in different states. In addition, a 3D blood vessel model including bifurcation was generated using the proposed method.

Understanding the fluid mechanics behind transverse wall shear stress

The patchy distribution of atherosclerosis within arteries is widely attributed to local variation in haemodynamic wall shear stress (WSS). A recently-introduced metric, the transverse wall shear stress (transWSS), which is the average over the cardiac cycle of WSS components perpendicular to the temporal mean WSS vector, correlates particularly well with the pattern of lesions around aortic branch ostia. Here we use numerical methods to investigate the nature of the arterial flows captured by transWSS and the sensitivity of transWSS to inflow waveform and aortic geometry. TransWSS developed chiefly in the acceleration, peak systolic and deceleration phases of the cardiac cycle; the reverse flow phase was too short, and WSS in diastole was too low, for these periods to have a significant influence. Most of the spatial variation in transWSS arose from variation in the angle by which instantaneous WSS vectors deviated from the mean WSS vector rather than from variation in the magnitude of the vectors. The pattern of transWSS was insensitive to inflow waveform; only unphysiologically high Womersley numbers produced substantial changes. However, transWSS was sensitive to changes in geometry. The curvature of the arch and proximal descending aorta were responsible for the principal features, the non-planar nature of the aorta produced asymmetries in the location and position of streaks of high transWSS, and taper determined the persistence of the streaks down the aorta. These results reflect the importance of the fluctuating strength of Dean vortices in generating transWSS.

A head-to-head comparison between CT- and IVUS-derived coronary blood flow models

The goal of this work is to compare coronary hemodynamics as predicted by computational blood flow models derived from two imaging modalities: coronary computed tomography angiography (CCTA) and intravascular ultrasound integrated with angiography (IVUS). Criteria to define boundary conditions are proposed to overcome the dissimilar anatomical definition delivered by both modalities. The strategy to define boundary conditions is novel in the present context, and naturally accounts for the flow redistribution induced by the resistance of coronary vessels. Hyperemic conditions are assumed to assess model predictions under stressed hemodynamic environments similar to those encountered in Fractional Flow Reserve (FFR) calculations. As results, it was found that CCTA models predict larger pressure drops, higher average blood velocity and smaller FFR. Concerning the flow rate at distal locations in the major vessels of interest, it was found that CCTA predicted smaller flow than IVUS, which is a consequence of a larger sensitivity of CCTA models to coronary steal phenomena. Comparisons to in-vivo measurements of FFR are shown.

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

BACKGROUND AND OBJECTIVE

Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation.

METHODS

This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan-Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model.

RESULTS

The accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method.

CONCLUSIONS

We demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.

Assessment of image-derived risk factors for natural course of unruptured cerebral aneurysms

OBJECT

The goal of this prospective longitudinal study was to test whether image-derived metrics can differentiate unruptured aneurysms that will become unstable (grow and/or rupture) from those that will remain stable.

METHODS

One hundred seventy-eight patients harboring 198 unruptured cerebral aneurysms for whom clinical observation and follow-up with imaging surveillance was recommended at 4 clinical centers were prospectively recruited into this study. Imaging data (predominantly CT angiography) at initial presentation was recorded. Computational geometry was used to estimate numerous metrics of aneurysm morphology that described the size and shape of the aneurysm. The nonlinear, finite element method was used to estimate uniform pressure-induced peak wall tension. Computational fluid dynamics was used to estimate blood flow metrics. The median follow-up period was 645 days. Longitudinal outcome data on these aneurysm patients—whether their aneurysms grew or ruptured (the unstable group) or remained unchanged (the stable group)—was documented based on follow-up at 4 years after the beginning of recruitment.

RESULTS

Twenty aneurysms (10.1%) grew, but none ruptured. One hundred forty-nine aneurysms (75.3%) remained stable and 29 (14.6%) were lost to follow-up. None of the metrics—including aneurysm size, nonsphericity index, peak wall tension, and low shear stress area—differentiated the stable from unstable groups with statistical significance.

CONCLUSIONS

The findings in this highly selected group do not support the hypothesis that image-derived metrics can predict aneurysm growth in patients who have been selected for observation and imaging surveillance. If aneurysm shape is a significant determinant of invasive versus expectant management, selection bias is a key limitation of this study.