Finite element simulation of the insertion of guidewires during an EVAR procedure: example of a complex patient case, a first step toward patient-specific parameterized models

Aortic roadmapping during EVAR: a combined FEM-EM tracking feasibility study

PURPOSE

Currently, the intra-operative visualization of vessels during endovascular aneurysm repair (EVAR) relies on contrast-based imaging modalities. Moreover, traditional image fusion techniques lack a continuous and automatic update of the vessel configuration, which changes due to the insertion of stiff guidewires. The purpose of this work is to develop and evaluate a novel approach to improve image fusion, that takes into account the deformations, combining electromagnetic (EM) tracking technology and finite element modeling (FEM).

METHODS

To assess whether EM tracking can improve the prediction of the numerical simulations, a patient-specific model of abdominal aorta was segmented and manufactured. A database of simulations with different insertion angles was created. Then, an ad hoc sensorized tool with three embedded EM sensors was designed, enabling tracking of the sensors' positions during the insertion phase. Finally, the corresponding cone beam computed tomography (CBCT) images were acquired and processed to obtain the ground truth aortic deformations of the manufactured model.

RESULTS

Among the simulations in the database, the one minimizing the in silico versus in vitro discrepancy in terms of sensors' positions gave the most accurate aortic displacement results.

CONCLUSIONS

The proposed approach suggests that the EM tracking technology could be used not only to follow the tool, but also to minimize the error in the predicted aortic roadmap, thus paving the way for a safer EVAR navigation.

Towards a reduced order model for EVAR planning and intra-operative navigation

INTRODUCTION

The pre-operative planning and intra-operative navigation of the endovascular aneurysm repair (EVAR) procedure are currently challenged by the aortic deformations that occur due to the insertion of a stiff guidewire. Hence, a fast and accurate predictive tool may help clinicians in the decision-making process and during surgical navigation, potentially reducing the radiations and contrast dose. To this aim, we generated a reduced order model (ROM) trained on parametric finite element simulations of the aortic wall-guidewire interaction.

METHOD

A Design of Experiments (DOE) consisting of 300 scenarios was created spanning over seven parameters. Radial basis functions were used to achieve a morphological parametrization of the aortic geometry. The ROM was built using 200 scenarios for training and the remaining 100 for validation.

RESULTS

The developed ROM estimated the displacement of aortic nodes with a relative error below 5.5% for all the considered validation cases. From a preliminary analysis, the aortic elasticity, the stiffness of the guidewire and the tortuosity of the cannulated iliac artery proved to be the most influential parameters.

CONCLUSIONS

Once built, the ROM provided almost real-time and accurate estimations of the guidewire-induced aortic displacement field, thus potentially being a promising pre- and intra-operative tool for clinicians.

A two-scale numerical study on the mechanobiology of abdominal aortic aneurysms

Abdominal aortic aneurysms (AAAs) are a serious condition whose pathophysiology is related to phenomena occurring at different length scales. To gain a better understanding of the disease, this work presents a multi-scale computational study that correlates AAA progression with microstructural and mechanical alterations in the tissue. Macro-scale geometries of a healthy aorta and idealized aneurysms with increasing diameter are developed on the basis of existing experimental data and subjected to physiological boundary conditions. Subsequently, microscopic representative volume elements of the abluminal side of each macro-model are employed to analyse the local kinematics at the cellular scale. The results suggest that the formation of the aneurysm disrupts the micromechanics of healthy tissue, which could trigger collagen growth and remodelling by mechanosensing cells. The resulting changes to the macro-mechanics and microstructure of the tissue seem to establish a new homeostatic state at the cellular scale, at least for the diameter range investigated.

Prediction of guidewire-induced aortic deformations during EVAR: a finite element and in vitro study

Introduction and aims: During an Endovascular Aneurysm Repair (EVAR) procedure a stiff guidewire is inserted from the iliac arteries. This induces significant deformations on the vasculature, thus, affecting the pre-operative planning, and the accuracy of image fusion. The aim of the present work is to predict the guidewire induced deformations using a finite element approach validated through experiments with patient-specific additive manufactured models. The numerical approach herein developed could improve the pre-operative planning and the intra-operative navigation. Material and methods: The physical models used for the experiments in the hybrid operating room, were manufactured from the segmentations of pre-operative Computed Tomography (CT) angiographies. The finite element analyses (FEA) were performed with LS-DYNA Explicit. The material properties used in finite element analyses were obtained by uniaxial tensile tests. The experimental deformed configurations of the aorta were compared to those obtained from FEA. Three models, obtained from Computed Tomography acquisitions, were investigated in the present work: A) without intraluminal thrombus (ILT), B) with ILT, C) with ILT and calcifications. Results and discussion: A good agreement was found between the experimental and the computational studies. The average error between the final in vitro vs. in silico aortic configurations, i.e., when the guidewire is fully inserted, are equal to 1.17, 1.22 and 1.40 mm, respectively, for Models A, B and C. The increasing trend in values of deformations from Model A to Model C was noticed both experimentally and numerically. The presented validated computational approach in combination with a tracking technology of the endovascular devices may be used to obtain the intra-operative configuration of the vessels and devices prior to the procedure, thus limiting the radiation exposure and the contrast agent dose.

Patient-Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives

The endovascular technique is used in the first line treatment in many complex aortic pathologies. Its clinical outcome is mostly determined by the appropriate selection of a stent-graft for a specific patient and the operator's experience. New tools are still needed to assist practitioners with decision making before and during procedures. For this purpose, numerical simulation enables the digital reproduction of an endovascular intervention with various degrees of accuracy. In this review, we introduce the basic principles and discuss the current literature regarding the use of numerical simulation for endovascular management of complex aortic diseases. Further, we give the future direction of everyday clinical applications, showing that numerical simulation is about to revolutionize how we plan and carry out endovascular interventions.

Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations

Three-dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients' needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.

Impact of calcification modeling to improve image fusion accuracy for endovascular aortic aneurysm repair

Since the 1990s, endovascular aortic aneurysm repair (EVAR) has become a common alternative to open surgery for the treatment of abdominal aortic aneurysms (AAAs). To aid the deployment of stent-grafts, fluoroscopic image guidance can be enhanced using preoperative simulation and intraoperative image fusion techniques. However, the impact of calcification (Ca) presence on the guidance accuracy of such techniques is yet to be considered. In the present work, we introduce a guidance tool that accounts for patient-specific Ca presence. Numerical simulations of EVAR were developed for 12 elective AAA patients, both with (With-Ca) and without (No-Ca) Ca consideration. To assess the accuracy of the simulations, the image results were overlaid on corresponding intraoperative images and the overlay error was measured at selected anatomical landmarks. With this approach we gained insight into the impact of Ca presence on image fusion accuracy. Inclusion of Ca improved mean image fusion accuracy by 8.68 ± 4.59%. In addition, a positive correlation between the relative Ca presence and the image fusion accuracy was found (R = .753, p < .005). Our results suggest that considering Ca presence in patient-specific EVAR simulations increases the reliability of EVAR image guidance techniques that utilize numerical simulation, especially for patients with severe aortic Ca presence.

Evaluation and Verification of Fast Computational Simulations of Stent-Graft Deployment in Endovascular Aneurysmal Repair

Fenestrated Endovascular Aortic Repair, also known as FEVAR, is a minimally invasive procedure that allows surgeons to repair the aorta while still preserving blood flow to kidneys and other critical organs. Given the high complexity of FEVAR, there is a pressing need to develop numerical tools that can assist practitioners at the preoperative planning stage and during the intervention. The aim of the present study is to introduce and to assess an assistance solution named Fast Method for Virtual Stent-graft Deployment for computer assisted FEVAR. This solution, which relies on virtual reality, is based on a single intraoperative X-ray image. It is a hybrid method that includes the use of intraoperative images and a simplified mechanical model based on corotational beam elements. The method was verified on a phantom and validated on three clinical cases, including a case with fenestrations. More specifically, we quantified the errors induced by the different simplifications of the mechanical model, related to fabric simulation and aortic wall mechanical properties. Overall, all errors for both stent and fenestration positioning were less than 5 mm, making this method compatible with clinical expectations. More specifically, the errors related to fenestration positioning were less than 3 mm. Although requiring further validation with a higher number of test cases, our method could achieve an accuracy compatible with clinical specifications within limited calculation time, which is promising for future implementation in a clinical context.

Patient-specific computational modeling of endovascular aneurysm repair: State of the art and future directions

Endovascular aortic repair (EVAR) has become the preferred intervention option for aortic aneurysms and dissections. This is because EVAR is much less invasive than the alternative open surgery repair. While in-hospital mortality rates are smaller for EVAR than open repair (1%-2% vs. 3%-5%), the early benefits of EVAR are lost after 3 years due to larger rates of complications in the EVAR group. Clinicians follow instructions for use (IFU) when possible, but are left with personal experience on how to best proceed and what choices to make with respect to stent-graft (SG) model choice, sizing, procedural options, and their implications on long-term outcomes. Computational modeling of SG deployment in EVAR and tissue remodeling after intervention offers an alternative way of testing SG designs in silico, in a personalized way before intervention, to ultimately select the strategies leading to better outcomes. Further, computational modeling can be used in the optimal design of SGs in cases of complex geometries. In this review, we address some of the difficulties and successes associated with computational modeling of EVAR procedures. There is still work to be done in all areas of EVAR in silico modeling, including model validation, before models can be applied in the clinic, but much progress has already been made. Critical to clinical implementation are current efforts focusing on developing fast algorithms that can achieve (near) real-time solutions, as well as ways of dealing with inherent uncertainties related to patient aortic wall degradation on an individualized basis. We are optimistic that EVAR modeling in the clinic will soon become a reality to help clinicians optimize EVAR interventions and ultimately reduce EVAR-associated complications.

A new software tool for planning interventional procedures in liver cancer

INTRODUCTION

Intra-arterial therapy is an effective way of performing chemotherapy or radiation therapy in patients with primary liver cancer (i.e. hepatocellular carcinoma). Although this minimally invasive approach is now an established treatment option, support tools for pre-operative planning and intra-operative assistance might be helpful.

MATERIAL AND METHODS

We developed an approach for semi-automatic segmentation of computed tomography angiography images of the main arterial branches (required for access path to the treatment site), automatic segmentation of the liver, arterial and venous tree, and interactive segmentation of the tumors (required for procedure-specific planning). This approach was then integrated into a liver-specific workflow within EndoSize^® solution, a planning software for endovascular procedures. The main branches extraction approach was qualitatively evaluated inside the software, while the automatic segmentation methods were quantitatively assessed.

RESULTS

Main branches extraction provides a success rate of 85% (i.e. all arteries correctly extracted) in a dataset of 172 patients. On public databases, a mean DICE of 0.91, 0.47 and 0.92 was obtained for liver, venous and arterial trees segmentation, respectively.

CONCLUSIONS

This pipeline is suitable for directly accessing the treatment site, giving anatomic measurements, and visualizing the hepatic trees, liver, and surrounding arteries during the pre-operative planning.

ABBREVIATIONS

HCC: hepatocellular carcinoma; TACE: transarterial chemoembolization; SIRT: selective internal radiation therapy; CT: computed tomography; CTA: computed tomography angiography; AMS: superior mesenteric artery; LGA: left gastric artery; RHA: right hepatic artery; LHA: left hepatic artery; rbHA: right branch of the hepatic artery; lbHA: left branch of the hepatic artery; GDA: gastroduodenal artery; VOI: volume of interest; SD: standard deviation; MICCAI: medical image computing and computer assisted interventions; MR: magnetic resonance.

Accuracy of registration techniques and vascular imaging modalities in fusion imaging for aortic endovascular interventions: a phantom study

Background

This study aimed to assess the error of different registration techniques and imaging modalities for fusion imaging of the aorta in a standardized setting using a anthropomorphic body phantom.

Materials and methods

A phantom with the 3D printed vasculature of a patient suffering from an infrarenal aortic aneurysm was constructed. Pulsatile flow was generated via an external pump. CTA/MRA of the phantom was performed, and a virtual 3D vascular model was computed. Subsequently, fusion imaging was performed employing 3D-3D and 2D-3D registration techniques. Accuracy of the registration was evaluated from 7 right/left anterior oblique c-arm angulations using the agreement of centerlines and landmarks between the phantom vessels and the virtual 3D virtual vascular model. Differences between imaging modalities were assessed in a head-to-head comparison based on centerline deviation. Statistics included the comparison of means ± standard deviations, student’s t-test, Bland-Altman analysis, and intraclass correlation coefficient for intra- and inter-reader analysis.

Results

3D-3D registration was superior to 2D-3D registration, with the highest mean centerline deviation being 1.67 ± 0.24 mm compared to 4.47 ± 0.92 mm. The highest absolute deviation was 3.25 mm for 3D-3D and 6.25 mm for 2D-3D registration. Differences for all angulations between registration techniques reached statistical significance. A decrease in registration accuracy was observed for c-arm angulations beyond 30° right anterior oblique/left anterior oblique. All landmarks (100%) were correctly positioned using 3D-3D registration compared to 81% using 2D-3D registration. Differences in accuracy between CT and MRI were acceptably small. Intra- and inter-reader reliability was excellent.

Conclusion

In the realm of registration techniques, the 3D-3D method proved more accurate than did the 2D-3D method. Based on our data, the use of 2D-3D registration for interventions with high registration quality requirements (e.g., fenestrated aortic repair procedures) cannot be fully recommended. Regarding imaging modalities, CTA and MRA can be used equivalently.

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, the early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging only incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs—identified by parameter sensitivity analysis—in an existing database of in silico PWs representative of subjects without AAAs. Then, a machine learning architecture for AAA detection was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties considerably influencing the PWs. However, AAA detection by PW indexes was compromised by other non-AAA related cardiovascular parameters. The proposed machine learning model produced a sensitivity of 86.8 % and a specificity of 86.3 % in early detection of AAA from the photoplethysmogram PW signal measured in the digital artery with added random noise. The number of false positive and negative results increased with increasing age and decreasing AAA size, respectively. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

Numerical simulation of fenestrated graft deployment: Anticipation of stent graft and vascular structure adequacy

Fenestrated endovascular aneurism repair (FEVAR) is a minimally invasive technique, and its success depends on the adequacy of the correspondence between the visceral arteries ostia and position of the fenestrations of the stent graft (SG) during its deployment in juxtarenal aneurisms. However, the fenestration position is generally determined from a preoperative computerised tomography (CT) scan, without considering the vascular deformation induced by the insertion of the endovascular tools. Catheterisation difficulties may occur during clinical procedures. Accordingly, the objective of this work is to present an initial proof of concept aimed at anticipating and optimising the position of the fenestrations, while considering the vascular deformation induced by the insertion of the endovascular tools. The proposed method relies on the finite element method to simulate the SG deployment in a vascular structure (VS), and considers the vascular deformation induced by the tools. After determining the optimal simulation parameters for a patient-specific case, the robustness of the method is demonstrated on six other representative anatomies. The simulated SG is also compared with post-deployment CT observations, and demonstrates good adequacy. The results show that the numerically corrected fenestration positions, as determined from the simulated results following the insertion of the endovascular tools, deviate from those of the standard plan (as determined from the preoperative CT scan). This indicates that the SG-VS adequacy could be improved via simulation-based planning, to anticipate potential catheterisation difficulties.

Analysis of the effect of guidewire position on stenosis diagnosis using computational fluid dynamics

BACKGROUND

Fractional flow reserve is an accurate method for diagnosing stenosis. The difficulty in using this procedure lies in placing the guidewire precisely at the blood vessel centerline. Owing to the long distance between the insertion point and the stenosis, a guidewire inclination can occur. Therefore, the main objective of this study is to investigate how the measured pressure in a blood vessel varies with the guidewire position.

METHODS

A three-dimensional model of blood flow is developed and numerically simulated. Two positions and two inclination angles from the blood vessel centerline and three throat diameters are investigated. The predicted results are validated using the available experimental data. The predicted results and actual measurements are observed to agree well with each other.

RESULTS

The pressure drop coefficient (CDP) increases because of guidewire insertion. When the guidewire is placed at inclined positions in moderate stenosis, the values of CDP are 66 and 68, depending on the inclination angle; the errors in CDP are 69% and 76%, respectively. At a high flow rate, the errors are reduced to 67% and 70%, respectively. The error in the CDP ranges from 42% to 61% when the guidewire is placed parallel to the centerline. For severe stenosis, the CDP is nearly the same at all positions and varies between 240 and 250; without a guidewire, the CDP is 163.

CONCLUSIONS

The findings confirmed that practitioners should be aware of the guidewire position during the operation. The displacement of the guidewire should be estimated, and the corresponding error must be considered.

Use of Numerical Simulation to Predict Iliac Complications During Placement of An Aortic Stent Graft

BACKGROUND

During endovascular aneurysm repair (EVAR), complex iliac anatomy is a source of complications such as unintentional coverage of the hypogastric artery. The aim of our study was to evaluate ability to predict coverage of the hypogastric artery using a biomechanical model simulating arterial deformations caused by the delivery system.

METHODS

The biomechanical model of deformation has been validated by many publications. The simulations were performed on 38 patients included retrospectively, for a total of 75 iliac arteries used for the study. On the basis of objective measurements, two groups were formed: one with "complex" iliac anatomy (n = 38 iliac arteries) and the other with "simple" iliac anatomy (n = 37 iliac arteries). The simulation enabled measurement of the lengths of the aorta and the iliac arteries once deformed by the device. Coverage of the hypogastric artery was predicted if the deformed renal/iliac bifurcation length (L_pre) was less than the length of the implanted device (L_stent-measured on the postoperative computed tomography [CT]) and nondeformed L_pre was greater than L_stent.

RESULTS

Nine (12%) internal iliac arteries were covered unintentionally. Of the coverage attributed to perioperative deformations, 1 case (1.3%) occurred with simple anatomy and 6 (8.0%) with complex anatomy (P = 0.25). All cases of unintentional coverage were predicted by the simulation. The simulation predicted hypogastric coverage in 35 cases (46.7%). There were therefore 26 (34.6%) false positives. The simulation had a sensitivity of 100% and a specificity of 60.6%. On multivariate analysis, the factors significantly predictive of coverage were the iliac tortuosity index (P = 0.02) and the predicted margin between the termination of the graft limb and the origin of the hypogastric artery in nondeformed (P = 0.009) and deformed (P = 0.001) anatomy.

CONCLUSIONS

Numerical simulation is a sensitive tool for predicting the risk of hypogastric coverage during EVAR and allows more precise preoperative sizing. Its specificity is liable to be improved by using a larger cohort.

Predictive Numerical Simulations of Double Branch Stent-Graft Deployment in an Aortic Arch Aneurysm

Total endovascular repair of the aortic arch represents a promising option for patients ineligible to open surgery. Custom-made design of stent-grafts (SG), such as the Terumo Aortic^® RelayBranch device (DB), requires complex preoperative measures. Accurate SG deployment is required to avoid intraoperative or postoperative complications, which is extremely challenging in the aortic arch. In that context, our aim is to develop a computational tool able to predict SG deployment in such highly complex situations. A patient-specific case is performed with complete deployment of the DB and its bridging stents in an aneurysmal aortic arch. Deviations of our simulation predictions from actual stent positions are estimated based on post-operative scan and a sensitivity analysis is performed to assess the effects of material parameters. Results show a very good agreement between simulations and post-operative scan, with especially a torsion effect, which is successfully reproduced by our simulation. Relative diameter, transverse and longitudinal deviations are of 3.2 ± 4.0%, 2.6 ± 2.9 mm and 5.2 ± 3.5 mm respectively. Our numerical simulations show their ability to successfully predict the DB deployment in complex anatomy. The results emphasize the potential of computational simulations to assist practitioners in planning and performing complex and secure interventions.

Artificial vascular models for endovascular training (3D printing)

The endovascular technique has led to a revolution in the care of patients with vascular disease; however, acquiring and maintaining proficiency over a broad spectrum of procedures is challenging. Three-dimensional (3D) printing technology allows the production of models that can be used for endovascular training. This article aims to explain the process and technologies available to produce vascular models for endovascular training, using 3D printing technology. The data are based on the group experience and a review of the literature. Different 3D printing methods are compared, describing their advantages, disadvantages and potential roles in surgical training. The process of 3D printing a vascular model based on an imaging examination consists of the following steps: image acquisition, image post-processing, 3D printing and printed model post-processing. The entire process can take a week. Prospective studies have shown that 3D printing can improve surgical planning, especially in complex endovascular procedures, and allows the production of efficient simulators for endovascular training, improving residents’ surgical performance and self-confidence.

Characterization of Vessel Deformations During EVAR: A Preliminary Retrospective Analysis to Improve Fidelity of Endovascular Simulators

OBJECTIVE

During endovascular aneurysm repair (EVAR), vessel deformations occur due to the insertion of tools and deployment of stent grafts in the arteries. We present a method for the characterization of vessel deformations during EVAR, and its application on patient datasets for a preliminary retrospective analysis that may be used to improve fidelity of endovascular simulators.

DESIGN

The method provides the extraction of vessel profiles from intraoperative fluoroscopic images and the calculation of a tortuosity index in the 2D fluoroscopy view (τ_2D) used to quantify the vessel deformations (δ%) during EVAR caused by the stiff guidewire insertion (δ%_Stiff) and the stent graft deployment (δ%_Graft), when compared with the undeformed vessel configuration (no device inserted). We applied the method to analyze retrospectively 7 EVAR patient datasets, including vasculature anatomies with different grades of vessel tortuosity or calcification: 2 patients (Pts) with absent tortuosity and mild calcification, 2 with mild tortuosity and mild calcification, 2 with severe tortuosity and mild calcification, and 1 with severe tortuosity and severe calcification. The analysis was focused on deformations of the left common iliac artery (LCIA), which is one of the arterial segments most affected by deformations.

RESULTS

In patients with mild LCIA calcification, the vessel straightening effect due to the stiff guidewire insertion increases as the severity of LCIA tortuosity increases (δ%_Stiff = 0 ± 2%, -19 ± 2%, -45 ± 2% for absent, mild, and severe tortuosity, respectively). In patients with mild/severe LCIA tortuosity, the artery with the deployed graft seems to retain part of the straightening effect caused by the stiff guidewire (δ%_Graft = -9 ± 3%, -31 ± 2%, for mild and severe tortuosity, respectively). In case of severe LCIA calcification, the stiff guidewire causes only a slight straightening effect (δ%_Stiff = -12%) despite the severe vessel tortuosity.

CONCLUSION

The method was effective in characterizing real vessel deformations during EVAR. Results gave evidence of a relationship between the obtained deformations and the anatomical vessel conformation. These results may be useful to drive predictive models of vessel deformations during EVAR to be implemented in endovascular patient-specific simulators for improving their fidelity.

Patient-specific simulation of guidewire deformation during transcatheter aortic valve implantation

Transcatheter aortic valve implantation is a recent mini-invasive procedure to implant an aortic valve prosthesis. Prosthesis positioning in transcatheter aortic valve implantation appears as an important aspect for the success of the intervention. Accordingly, we developed a patient-specific finite element framework to predict the insertion of the stiff guidewire, used to position the aortic valve. We simulated the guidewire insertion for 2 patients based on their pre-operative CT scans. The model was designed to primarily predict the position and the angle of the guidewires in the aortic valve, and the results were successfully compared with intraoperative images. The present paper describes extensively the numerical model, which was solved by using the ANSYS software with an implicit resolution scheme, as well as the stabilization techniques which were used to overcome numerical instabilities. We performed sensitivity analysis on the properties of the guidewire (curvature angle, curvature radius, and stiffness) and the conditions of insertion (insertion force and orientation). We also explored the influence of the model parameters. The accuracy of the model was quantitatively evaluated as the distance and the angle difference between the simulated guidewires and the intraoperative ones. A good agreement was obtained between the model predictions and intraoperative views available for 2 patient cases. In conclusion, we showed that the shape of the guidewire in the aortic valve was mainly determined by the geometry of the patient's aorta and by the conditions of insertion (insertion force and orientation).

A methodology for in silico endovascular repair of abdominal aortic aneurysms

Endovascular aneurysm repair (EVAR) can involve some unfavorable complications such as endoleaks or stent-graft (SG) migration. Such complications, resulting from the complex mechanical interaction of vascular tissue, SG and blood flow or incompatibility of SG design and vessel geometry, are difficult to predict. Computational vascular mechanics models can be a predictive tool for the selection, sizing and placement process of SGs depending on the patient-specific vessel geometry and hence reduce the risk of potential complications after EVAR. In this contribution, we present a new in silico EVAR methodology to predict the final state of the deployed SG after intervention and evaluate the mechanical state of vessel and SG, such as contact forces and wall stresses. A novel method to account for residual strains and stresses in SGs, resulting from the precompression of stents during the assembly process of SGs, is presented. We suggest a parameter continuation approach to model various different sizes of SGs within one in silico EVAR simulation which can be a valuable tool when investigating the issue of SG oversizing. The applicability and robustness of the proposed methods are demonstrated on the example of a synthetic abdominal aortic aneurysm geometry.

Endovascular Management of Abdominal Aortic Aneurysms: the Year in Review

OPINION STATEMENT

Endovascular aneurysm repair (EVAR) has become the predominant method of treatment for abdominal aortic aneurysms (AAA). The use of conscious sedation with local anesthesia and percutaneous femoral access has further decreased the morbidity of the procedure. Current devices can more effectively manage increasingly "hostile" aneurysm necks, while chimney grafts or dedicated fenestrated stent-grafts can be used for juxta-renal disease with favorable results. However, endovascular repair does present a new set of challenges, and endoleaks remain an area of concern. While there is general consensus that type I and type III endoleaks require treatment, type II endoleaks are the topic of ongoing research and debate. Development of devices and techniques to prevent and treat endoleak continues to progress. Advances in contrast-enhanced ultrasound are reducing reliance on computed tomography for post-operative monitoring. This is an important step in this population at high risk for the development of kidney failure. Despite these many innovations, further research is needed to optimize the care of patients with AAA.