Reconstruction and finite element mesh generation of abdominal aortic aneurysms from computerized tomography angiography data with minimal user interactions

Peak wall rupture index is associated with risk of rupture of abdominal aortic aneurysms, independent of size and sex

BACKGROUND

Information on the predictive determinants of abdominal aortic aneurysm rupture from CT angiography are scarce. The aim of this study was to investigate biomechanical parameters in abdominal aortic aneurysms and their association with risk of subsequent rupture.

METHODS

In this retrospective study, the digital radiological archive was searched for 363 patients with ruptured abdominal aortic aneurysms. All patients who underwent at least one CT angiography examination before aneurysm rupture were included. CT angiography results were analysed to determine maximum aneurysm diameter, aneurysm volume, and biomechanical parameters (peak wall stress and peak wall rupture index). In the primary survival analysis, patients with abdominal aortic aneurysms less than 70 mm were considered. Sensitivity analyses including control patients and abdominal aortic aneurysms of all sizes were performed.

RESULTS

A total of 67 patients who underwent 109 CT angiography examinations before aneurysm rupture were identified. The majority were men (47, 70%) and the median age at the time of CTA examination was 77 (71-83) years. The median maximum aneurysm diameter was 56 (interquartile range 46-65) mm and the median time to rupture was 2.13 (interquartile range 0.64-4.72) years. In univariable analysis, maximum aneurysm diameter, aneurysm volume, peak wall stress, and peak wall rupture index were all associated with risk of rupture. Women had an increased HR for rupture when adjusted for maximum aneurysm diameter or aneurysm volume (HR 2.16, 95% c.i. 1.23 to 3.78 (P = 0.007) and HR 1.92, 95% c.i. 1.06 to 3.50 (P = 0.033) respectively). In multivariable analysis, the peak wall rupture index was associated with risk of rupture. The HR for peak wall rupture index was 1.05 (95% c.i. 1.03 to 1.08) per % (P < 0.001) when adjusted for maximum aneurysm diameter and 1.05 (95% c.i. 1.02 to 1.08) per % (P < 0.001) when adjusted for aneurysm volume.

CONCLUSION

Biomechanical factors appear to be important in the prediction of abdominal aortic aneurysm rupture. Women are at increased risk of rupture when adjustments are made for maximum aneurysm diameter alone.

Correlation of four-dimensional ultrasound strain analysis with computed tomography angiography wall stress simulations in abdominal aortic aneurysms

Objective

Biomechanical modeling of infrarenal aortic aneurysms seeks to predict ruptures in advance, thereby reducing aneurysm-related deaths. As individual methods focusing on strain and stress analysis lack adequate discretization power, this study aims to explore multifactorial characterization for progressive aneurysmal degeneration. The study's objective is to compare stress- and strain-related parameters in infrarenal aortic aneurysms.

Methods

Twenty-two patients with abdominal aortic aneurysms (AAAs) (mean maximum diameter, 53.2 ± 7.2 mm) were included in the exploratory study, examined by computed tomography angiography (CTA) and three-dimensional real-time speckle tracking ultrasound (4D-US). The conformity of aneurysm anatomy in 4D-US and CTA was determined with the mean point-to-point distance (MPPD). CTA was employed for each AAA to characterize stress-related indices using the semi-automated A4-clinics RE software. Five segmentations from one 4D-US examination were fused into one averaged model for strain analysis using MATLAB and the Abaqus solver.

Results

The mean MPPD between the adjacent points of the 4D-US and CTA-derived geometry was 1.8 ± 0.4 mm. The interclass correlation coefficients for all raters and all measurements for the maximum AAA diameter in 2D, 4D ultrasound, and CTA indicate moderate to good reliability (interclass correlation coefficient1 0.69 with 95% confidence interval [CI], 0.49-0.84; P < .001). The peak wall stress (PWS) correlates fairly with the maximum AAA diameter in 2D-US (r = 0.54; P < .01) and 4D-US (r = 0.53; P < .05) and moderately strongly with the maximum exterior AAA diameter (r = 0.63; P < .01). The peak wall rupture risk index shows a strong correlation with the PWS (ρ > 0.9; P < .001) and is influenced by anatomical parameters with equal strength. Isolated observation of the intraluminal thrombus does not provide significant information in the determination of PWS. The maximum AAA diameter in 2D-US shows a fair negative correlation with the mean circumferential, longitudinal and in-plane shear strain (ρ = -0.46; r = -0.45; ρ = -0.47; P < .05 for all). The circumferential strain ratio as an indicator of wall motion heterogeneity increases with the aneurysm diameter (r = 0.47; P < .05). The direct comparison of wall strain and wall stress indices shows no quantitative correlation.

Conclusions

The strain and stress analyses provide independent biomechanical information of AAAs. At the current stage of development, the two methods are considered complementary and may optimize a more patient-specific rupture risk prediction in the future.

Computational Study of Abdominal Aortic Aneurysm Walls Accounting for Patient-Specific Non-Uniform Intraluminal Thrombus Thickness and Distinct Material Models: A Pre- and Post-Rupture Case

An intraluminal thrombus (ILT) is present in the majority of abdominal aortic aneurysms, playing a crucial role in their growth and rupture. Although most computational studies do not include the ILT, in the present study, this is taken into account, laying out the whole simulation procedure, namely, from computed tomography scans to medical image segmentation, geometry reconstruction, mesh generation, biomaterial modeling, finite element analysis, and post-processing, all carried out in open software. By processing the tomography scans of a patient's aneurysm before and after rupture, digital twins are reconstructed assuming a uniform aortic wall thickness. The ILT and the aortic wall are assigned different biomaterial models; namely, the first is modeled as an isotropic linear elastic material, and the second is modeled as the Mooney-Rivlin hyperelastic material as well as the transversely isotropic hyperelastic Holzapfel-Gasser-Ogden nonlinear material. The implementation of the latter requires the designation of local Cartesian coordinate systems in the aortic wall, suitably oriented in space, for the proper orientation of the collagen fibers. The composite aneurysm geometries (ILT and aortic wall structures) are loaded with normal and hypertensive static intraluminal pressure. Based on the calculated stress and strain distributions, ILT seems to be protecting the aneurysm from a structural point of view, as the highest stresses appear in the thrombus-free areas of the aneurysmal wall.

Three-dimensional growth and biomechanical risk progression of abdominal aortic aneurysms under serial computed tomography assessment

Growth of abdominal aortic aneurysms (AAAs) is often described as erratic and discontinuous. This study aimed at describing growth patterns of AAAs with respect to maximal aneurysm diameter (Dmax) and aneurysm volume, and to characterize changes in the intraluminal thrombus (ILT) and biomechanical indices as AAAs grow. 384 computed tomography angiographies (CTAs) from 100 patients (mean age 70.0, standard deviation, SD = 8.5 years, 22 women), who had undergone at least three CTAs, were included. The mean follow-up was 5.2 (SD = 2.5) years. Growth of Dmax was 2.64 mm/year (SD = 1.18), volume 13.73 cm³/year (SD = 10.24) and PWS 7.3 kPa/year (SD = 4.95). For Dmax and volume, individual patients exhibited linear growth in 87% and 77% of cases. In the tertile of patients with the slowest Dmax-growth (< 2.1 mm/year), only 67% belonged to the slowest tertile for volume-growth, and 52% and 55% to the lowest tertile of PWS- and PWRI-increase, respectively. The ILT-ratio (ILT-volume/aneurysm volume) increased with time (2.6%/year, p < 0.001), but when adjusted for volume, the ILT-ratio was inversely associated with biomechanical stress. In contrast to the notion that AAAs grow in an erratic fashion most AAAs displayed continuous and linear growth. Considering only change in Dmax, however, fails to capture the biomechanical risk progression, and parameters such as volume and the ILT-ratio need to be considered.

Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease

BACKGROUND

Non-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians' experience.

PURPOSE

The main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology.

METHODS

The proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning.

RESULTS

Benefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases.

CONCLUSION

We proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.

A quarter of a century biomechanical rupture risk assessment of abdominal aortic aneurysms. Achievements, clinical relevance, and ongoing developments

Abdominal aortic aneurysm (AAA) disease, the local enlargement of the infrarenal aorta, is a serious condition that causes many deaths, especially in men exceeding 65 years of age. Over the past quarter of a century, computational biomechanical models have been developed towards the assessment of AAA risk of rupture, technology that is now on the verge of being integrated within the clinical decision-making process. The modeling of AAA requires a holistic understanding of the clinical problem, in order to set appropriate modeling assumptions and to draw sound conclusions from the simulation results. In this article we summarize and critically discuss the proposed modeling approaches and report the outcome of clinical validation studies for a number of biomechanics-based rupture risk indices. Whilst most of the aspects concerning computational mechanics have already been settled, it is the exploration of the failure properties of the AAA wall and the acquisition of robust input data for simulations that has the greatest potential for the further improvement of this technology.

Local aortic aneurysm wall expansion measured with automated image analysis

Background

Assessment of regional aortic wall deformation (RAWD) might better predict for abdominal aortic aneurysm (AAA) rupture than the maximal aortic diameter or growth rate. Using sequential computed tomography angiograms (CTAs), we developed a streamlined, semiautomated method of computing RAWD using deformable image registration (dirRAWD).

Methods

Paired sequential CTAs performed 1 to 2 years apart of 15 patients with AAAs of various shapes and sizes were selected. Using each patient’s initial CTA, the luminal and aortic wall surfaces were segmented both manually and semiautomatically. Next, the same patient’s follow-up CTA was aligned with the first using automated rigid image registration. Deformable image registration was then used to calculate the local aneurysm wall expansion between the sequential scans (dirRAWD). To measure technique accuracy, the deformable registration results were compared with the local displacement of anatomic landmarks (fiducial markers), such as the origin of the inferior mesenteric artery and/or aortic wall calcifications. Additionally, for each patient, the maximal RAWD was manually measured for each aneurysm and was compared with the dirRAWD at the same location.

Results

The technique was successful in all patients. The mean landmark displacement error was 0.59 ± 0.93 mm with no difference between true landmark displacement and deformable registration landmark displacement by Wilcoxon rank sum test (P = .39). The absolute difference between the manually measured maximal RAWD and dirRAWD was 0.27 ± 0.23 mm, with a relative difference of 7.9% and no difference using the Wilcoxon rank sum test (P = .69). No differences were found in the maximal dirRAWD when derived using a purely manual AAA segmentation compared with using semiautomated AAA segmentation (P = .55).

Conclusions

We found accurate and automated RAWD measurements were feasible with clinically insignificant errors. Using semiautomated AAA segmentations for deformable image registration methods did not alter maximal dirRAWD accuracy compared with using manual AAA segmentations. Future work will compare dirRAWD with finite element analysis–derived regional wall stress and determine whether dirRAWD might serve as an independent predictor of rupture risk.

Current abdominal aortic aneurysm (AAA) surveillance methods are limited to assessments of the maximal diameter, which cannot accurately predict for AAA expansion and rupture risk. Automated assessment of AAA expansion across the entire three-dimensional geometry of the aneurysm could better describe aneurysm growth and could substantially inform management decisions, including the indications for repair. We have developed an accurate and streamlined approach to assessing local three-dimensional AAA expansion with submillimeter accuracy using computed tomography imaging obtained during routine aneurysm surveillance. This novel process does not require significant user expertise nor computer processing power and can be performed using open-source software readily accessible to both scientists and clinicians.

Computational Fluid Dynamic Technique for Assessment of How Changing Character of Blood Flow and Different Value of Hct Influence Blood Hemodynamic in Dissected Aorta

Using computer tomography angiography (CTA) and computational structural analysis, we present a non-invasive method of mass flow rate/velocity and wall stress analysis in type B aortic dissection. Three-dimensional (3D) computer models of the aorta were calculated using pre-operative (baseline) and post-operative CT data from 12 male patients (aged from 51 to 64 years) who were treated for acute type B dissection. A computational fluid dynamics (CFD) technique was used to quantify the displacement forces acting on the aortic wall in the areas of endografts placement. The mass flow rate and wall stress were measured and quantified using the CFD technique. The CFD model indicated the places with a lower value of blood velocity and shear rate, which corelated with higher blood viscosity and a probability of thrombus appearance. Moreover, with the increase in Hct, blood viscosity also increased, while the intensity of blood flow provoked changing viscosity values in these areas. Furthermore, the velocity gradient near the tear surface caused high wall WSS; this could lead to a decreased resistance in the aorta’s wall with further implications to a patient.

Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms

It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40-50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65-0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.

Spatial Configuration of Abdominal Aortic Aneurysm Analysis as a Useful Tool for the Estimation of Stent-Graft Migration

The aim of this study was to prepare a self-made mathematical algorithm for the estimation of risk of stent-graft migration with the use of data on abdominal aortic aneurysm (AAA) size and geometry of blood flow through aneurysm sac before or after stent-graft implantation. AngioCT data from 20 patients aged 50–60 years, before and after stent-graft placement in the AAA was analyzed. In order to estimate the risk of stent-graft migration for each patient we prepared an opposite spatial configuration of virtually reconstructed stent-graft with long body or short body. Thus, three groups of 3D geometries were analyzed: 20 geometries representing 3D models of aneurysm, 20 geometries representing 3D models of long body stent-grafts, and 20 geometries representing 3D models of short body stent-graft. The proposed self-made algorithm demonstrated its efficiency and usefulness in estimating wall shear stress (WSS) values. Comparison of the long or short type of stent-graft with AAA geometries allowed to analyze the implants’ spatial configuration. Our study indicated that short stent-graft, after placement in the AAA sac, generated lower drug forces compare to the long stent-graft. Each time shape factor was higher for short stent-graft compare to long stent-graft.

Multidimensional Analysis of Descending Aortic Growth After Acute Type A Aortic Dissection

BACKGROUND

After repair of acute type A aortic dissection, typical geometric variables of conventional aortic surveillance focus on maximum diameter and its rate of growth, potentially missing important geometric changes elsewhere. We determined additional information provided by a semiautomated, 3-dimensional (3D), nonlinear growth model of the descending thoracic aorta after repair of type A aortic dissection.

METHODS

Computed tomographic angiography data were retrospectively collected after hemiarch repair of type A aortic dissection. The descending aorta was systematically reconstructed to generate a 3D model made up of individual segments. The baseline and follow-up diameters were measured semiautomatically for each segment, and the nonlinear interval growth was determined.

RESULTS

The fastest growing segment expanded at a rate of 3.8 mm/y (interquartile range, 2.2 to 5.4 mm/y) vs 0.6 mm/y (interquartile range, -0.3 to 1.7 mm/y) when measured at the original site of maximum diameter (P < .01). The maximum baseline diameter was a poor predictor of location with fastest growth (r = 0.10, P > .1). Using the society recommended growth limits, a greater proportion of patients would be considered "at risk" when assessed by our method vs conventional surveillance measures.

CONCLUSIONS

Our model identifies areas of rapid aortic growth after repair of type A dissection that would likely be missed using current surveillance techniques. The increased precision, resolution, and reproducibility provided by our technique may improve on limitations of current surveillance techniques, provide novel geometric data on aortic remodeling, and contribute to the pursuit of a comprehensive patient-specific approach to aortic risk stratification.

Estimation of Abdominal Aortic Aneurysm Rupture Risk with Biomechanical Imaging Markers

PURPOSE

To evaluate whether the biomechanical marker known as rupture risk equivalent diameter (RRED) was superior to the actual abdominal aortic aneurysm (AAA) diameter in estimating future rupture risk in patients who had undergone pre-rupture computed tomography (CT) angiography.

MATERIALS AND METHODS

A retrospective study was conducted in 13 patients with ruptured AAAs who had undergone CT angiography before and after rupture between 2001 and 2015. The median time between the 2 scans was 731 days. Biomechanical and geometrical markers such as maximal AAA diameter, peak wall stress (PWS), and RRED were calculated with AAA-dedicated software. The main analyses determined whether RRED was higher than the actual diameter and the threshold diameter for elective surgery (55 mm for men, 50 mm for women) in AAAs before and after rupture. Differences between diameter and biomechanical markers before and after rupture were tested with appropriate statistical tests.

RESULTS

RRED before and after rupture was smaller than the actual diameter in 7 of 13 cases. Post-rupture RRED was estimated to be smaller than the threshold diameter for elective repair in 4 cases, again suggesting a low rupture risk. The median PWS before and after rupture was 181.7 kPa (interquartile range [IQR], 152.1-244.2 kPa) and 274.1 kPa (IQR, 172.2-377.2 kPa), respectively.

CONCLUSIONS

RRED was smaller than the actual diameter in more than half of pre-rupture AAAs, suggesting a lower rupture risk than estimated with the actual diameter. The results suggest that the currently available biomechanical imaging markers might not be ready for use in clinical practice.

Aortic Lumen Area Is Increased in Ruptured Abdominal Aortic Aneurysms and Correlates to Biomechanical Rupture Risk

PURPOSE

To investigate how 2-dimensional geometric parameters differ between ruptured and asymptomatic abdominal aortic aneurysms (AAAs) and provide a biomechanical explanation for the findings.

METHODS

The computed tomography angiography (CTA) scans of 30 patients (mean age 77±10 years; 23 men) with ruptured AAAs and 60 patients (mean age 76±8 years; 46 men) with asymptomatic AAAs were used to measure maximum sac diameter along the center lumen line, the cross-sectional lumen area, the total vessel area, the intraluminal thrombus (ILT) area, and corresponding volumes. The CTA data were segmented to create 3-dimensional patient-specific models for finite element analysis to compute peak wall stress (PWS) and the peak wall rupture index (PWRI). To reduce confounding from the maximum diameter, 2 diameter-matched groups were selected from the initial patient cohorts: 28 ruptured AAAs and another with 15 intact AAAs (diameters 74±12 vs 73±11, p=0.67). A multivariate model including the maximum diameter, the lumen area, and the ILT area of the 60 intact aneurysms was employed to predict biomechanical rupture risk parameters.

RESULTS

In the diameter-matched subgroup comparison, ruptured AAAs had a significantly larger cross-sectional lumen area (1954±1254 vs 1120±623 mm², p=0.023) and lower ILT area ratio (55±24 vs 68±24, p=0.037). The ILT area (2836±1462 vs 2385±1364 mm², p=0.282) and the total vessel area (3956±1170 vs 4338±1388 mm², p=0.384) did not differ statistically between ruptured and intact aneurysms. The PWRI was increased in ruptured AAAs (0.80 vs 0.48, p<0.001), but the PWS was similar (249 vs 284 kPa, p=0.194). In multivariate regression analysis, lumen area was significantly positively associated with both PWS (p<0.001) and PWRI (p<0.01). The ILT area was also significantly positively associated with PWS (p<0.001) but only weakly with PWRI (p<0.01). The lumen area conferred a higher risk increase in both PWS and PWRI when compared with the ILT area.

CONCLUSION

The lumen area is increased in ruptured AAAs compared to diameter-matched asymptomatic AAAs. Furthermore, this finding may in part be explained by a relationship with biomechanical rupture risk parameters, in which lumen area, irrespective of maximum diameter, increases PWS and PWRI. These observations thus suggest a possible method to improve prediction of rupture risk in AAAs by measuring the lumen area without the use of computational modeling.

Additional value of biomechanical indices based on CTa for rupture risk assessment of abdominal aortic aneurysms

OBJECTIVE

Biomechanics for rupture risk prediction in abdominal aortic aneurysms (AAA) are gaining popularity. However, their clinical applicability is still doubtful as there is lack of standardization. This study evaluates the added value of biomechanical indices in rupture risk assessment.

METHODS

This study included 175 asymptomatic, 11 sAAA and 45 ruptured aneurysms. 3D-geometries were reconstructed using computer tomography angiographies. Subsequently, finite element models were made to calculate peak wall stress (PWS), peak wall rupture index (PWRI) and the rupture risk equivalent diameter (RRED). The indices were determined with a dedicated software to facilitate standardization.

RESULTS

SAAAs showed a trend towards higher PWS, PWRI and RRED compared to asymptomatic AAAs, but PWS (22.0±5.8 vs. 33.4±15.8 N/cm2), PWRI (0.52±0.2 vs. 1.01±0.64), and RRED (65±60 vs. 98±51 mm) were significantly (p = 0.001) higher in ruptured. However, after diameter-matching no significant differences were seen. The ROC-curves for the maximum diameter and all biomechanical indices were similar but it slightly increased when diameter and biomechanical indices were combined.

CONCLUSIONS

This study showed no added value for biomechanical indices in AAA rupture risk assessment. Additionally, the difficulty of such an assessment increases. However, as symptomatic aneurysms show a trend towards higher biomechanical indices with similar diameters the indices may provide information about aneurysm growth and development.

Computational Fluid Dynamic Accuracy in Mimicking Changes in Blood Hemodynamics in Patients with Acute Type IIIb Aortic Dissection Treated with TEVAR

Background: We aimed to verify the accuracy of the Computational Fluid Dynamics (CFD) algorithm for blood flow reconstruction for type IIIb aortic dissection (TBAD) before and after thoracic endovascular aortic repair (TEVAR). Methods: We made 3D models of the aorta and its branches using pre- and post-operative CT data from five patients treated for TBAD. The CFD technique was used to quantify the displacement forces acting on the aortic wall in the areas of endograft, mass flow rate/velocity and wall shear stress (WSS). Calculated results were verified with ultrasonography (USG-Doppler) data. Results: CFD results indicated that the TEVAR procedure caused a 7-fold improvement in overall blood flow through the aorta (p = 0.0001), which is in line with USG-Doppler data. A comparison of CFD results and USG-Doppler data indicated no significant change in blood flow through the analysed arteries. CFD also showed a significant increase in flow rate for thoracic trunk and renal arteries, which was in accordance with USG-Doppler data (accuracy 90% and 99.9%). Moreover, we observed a significant decrease in WSS values within the whole aorta after TEVAR compared to pre-TEVAR (1.34 ± 0.20 Pa vs. 3.80 ± 0.59 Pa, respectively, p = 0.0001). This decrease was shown by a significant reduction in WSS and WSS contours in the thoracic aorta (from 3.10 ± 0.27 Pa to 1.34 ± 0.11Pa, p = 0.043) and renal arteries (from 4.40 ± 0.25 Pa to 1.50 ± 0.22 Pa p = 0.043). Conclusions: Post-operative remodelling of the aorta after TEVAR for TBAD improved hemodynamic patterns reflected by flow, velocity and WSS with an accuracy of 99%.

3D Blood Vessels Reconstruction Based on Segmented CT Data for Further Simulations of Hemodynamic in Human Artery Branches

We aimed at the reconstruction of the branches of human aortic arch for blood perfusion analysis used later in the Computational Fluid Dynamic (CFD). The reconstruction was performed based on segmentation results obtained from CT data. Two segmentation algorithms, region growing and level set were implemented. Obtained binary segmentation results were next evaluated by the expert and corrected if needed. The final reconstruction was used for preparation of a numerical grid and for further calculation of blood hemodynamic. The collected data composed of blood velocity and blood flow rate in function of time were compared with USG-Doppler data. Results demonstrate that proposed algorithm may be useful for initial reconstruction of human cardiac system, however its accuracy needs to be improved as further manual corrections are still needed.

Biomechanical changes during abdominal aortic aneurysm growth

The biomechanics-based Abdominal Aortic Aneurysm (AAA) rupture risk assessment has gained considerable scientific and clinical momentum. However, such studies have mainly focused on information at a single time point, and little is known about how AAA properties change over time. Consequently, the present study explored how geometry, wall stress-related and blood flow-related biomechanical properties change during AAA expansion. Four patients with a total of 23 Computed Tomography-Angiography (CT-A) scans at different time points were analyzed. At each time point, patient-specific properties were extracted from (i) the reconstructed geometry, (ii) the computed wall stress at Mean Arterial Pressure (MAP), and (iii) the computed blood flow velocity at standardized inflow and outflow conditions. Testing correlations between these parameters identified several nonintuitive dependencies. Most interestingly, the Peak Wall Rupture Index (PWRI) and the maximum Wall Shear Stress (WSS) independently predicted AAA volume growth. Similarly, Intra-luminal Thrombus (ILT) volume growth depended on both the maximum WSS and the ILT volume itself. In addition, ILT volume, ILT volume growth, and maximum ILT layer thickness correlated with PWRI as well as AAA volume growth. Consequently, a large ILT volume as well as fast increase of ILT volume over time may be a risk factor for AAA rupture. However, tailored clinical studies would be required to test this hypothesis and to clarify whether monitoring ILT development has any clinical benefit.

Gene Expression Profiling in Abdominal Aortic Aneurysms After Finite Element Rupture Risk Assessment

PURPOSE

To investigate the association between local biomechanical rupture risk calculations from finite element analysis (FEA) and whole-genome profiling of the abdominal aortic aneurysm (AAA) wall to determine if AAA wall regions with highest and lowest estimated rupture risk show different gene expression patterns.

METHODS

Six patients (mean age 74 years; all men) scheduled for open surgery to treat asymptomatic AAAs (mean diameter 55.2±3.5 mm) were recruited for the study. Rupture risk profiles were estimated by FEA from preoperative computed tomography angiography data. During surgery, AAA wall samples of ~10 mm² were extracted from the lowest and highest rupture risk locations identified by the FEA. Twelve samples were processed for RNA extraction and subsequent whole genome expression profiling. Expression of single genes and of predefined gene groups were compared between vessel wall areas with highest and lowest predicted rupture risk.

RESULTS

Normalized datasets comprised 15,079 gene transcripts with expression above background. In biopsies with high rupture risk, upregulation of 18 and downregulation of 18 genes was detected when compared to the low-risk counterpart. Global analysis of predefined gene groups revealed expression differences in genes associated with extracellular matrix (ECM) degradation (p<0.001), matrix metalloproteinase activity (p<0.001), and chemokine signaling (p<0.001).

CONCLUSION

Increased expression of genes involved in degrading ECM components was present in AAA wall regions with highest biomechanical stress, supporting the thesis of mechanotransduction. More experimental studies with cooperation of multicenter vascular biobanks are necessary to understand AAA etiologies and identify further parameters of FEA model complementation.

Gender, smoking, body size, and aneurysm geometry influence the biomechanical rupture risk of abdominal aortic aneurysms as estimated by finite element analysis

OBJECTIVE

Finite element analysis (FEA) has been suggested to be superior to maximal diameter measurements in predicting rupture of abdominal aortic aneurysms (AAAs). Our objective was to investigate to what extent previously described rupture risk factors were associated with FEA-estimated rupture risk.

METHODS

One hundred forty-six patients with an asymptomatic AAA of a 40- to 60-mm diameter were retrospectively identified and consecutively included. The patients' computed tomography angiograms were analyzed by FEA without (neutral) and with (specific) input of patient-specific mean arterial pressure (MAP), gender, family history, and age. The maximal wall stress/wall strength ratio was described as a rupture risk equivalent diameter (RRED), which translated this ratio into an average aneurysm diameter of corresponding rupture risk.

RESULTS

In multivariate linear regression, RRED_neutral increased with female gender (3.7 mm; 95% confidence interval [CI], 0.13-7.3) and correlated with patient height (0.27 mm/cm; 95% CI, 0.11-0.43) and body surface area (BSA, 16 mm/m²; 95% CI, 8.3-24) and inversely with body mass index (BMI, -0.40 mm/kg m^-2; 95% CI, -0.75 to -0.054) in a wall stress-dependent manner. Wall stress-adjusted RRED_neutral was raised if the patient was currently smoking (1.1 mm; 95% CI, 0.21-1.9). Age, MAP, family history, and patient weight were unrelated to RRED_neutral. In specific FEA, RRED_specific increased with female gender, MAP, family history positive for AAA, height, and BSA, whereas it was inversely related to BMI. All results were independent of aneurysm diameter. Peak wall stress and RRED correlated with aneurysm diameter and lumen volume.

CONCLUSIONS

Female gender, current smoking, increased patient height and BSA, and low BMI were found to increase the mechanical rupture risk of AAAs. Previously described rupture risk factors may in part be explained by patient characteristic-dependent variations in aneurysm biomechanics.

Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors

PURPOSE

There is no standard for measuring maximal diameter (Dmax) of abdominal aortic aneurysm (AAA) from computer tomography (CT) images although differences between Dmax evaluated from transversal (axialDmax) or orthogonal (orthoDmax) planes can be large especially for angulated AAAs. Therefore we investigated their correlations with alternative rupture risk indicators as peak wall stress (PWS) and peak wall rupture risk (PWRR) to decide which Dmax is more relevant in AAA rupture risk assessment.

MATERIAL AND METHODS

The Dmax values were measured by a trained radiologist from 70 collected CT scans, and the corresponding PWS and PWRR were evaluated using Finite Element Analysis (FEA). The cohort was ordered according to the difference between axialDmax and orthoDmax (D_a-o) quantifying the aneurysm angulation, and Spearman's correlation coefficients between PWS/PWRR - orthoDmax/axialDmax were calculated.

RESULTS

The calculated correlations PWS/PWRR vs. orthoDmax were substantially higher for angulated AAAs (with D_a-o≥3mm). Under this limit, the correlations were almost the same for both Dmax values. Analysis of AAAs divided into two groups of angulated (n=38) and straight (n=32) cases revealed that both groups are similar in all parameters (orthoDmax, PWS, PWRR) with the exception of axialDmax (p=0.024).

CONCLUSIONS

It was confirmed that orthoDmax is better correlated with the alternative rupture risk predictors PWS and PWRR for angulated AAAs (D_A-O≥3mm) while there is no difference between orthoDmax and axialDmax for straight AAAs (D_A-O<3mm). As angulated AAAs represent a significant portion of cases it can be recommended to use orthoDmax as the only Dmax parameter for AAA rupture risk assessment.

A homeostatic-driven turnover remodelling constitutive model for healing in soft tissues

Remodelling of soft biological tissue is characterized by interacting biochemical and biomechanical events, which change the tissue's microstructure, and, consequently, its macroscopic mechanical properties. Remodelling is a well-defined stage of the healing process, and aims at recovering or repairing the injured extracellular matrix. Like other physiological processes, remodelling is thought to be driven by homeostasis, i.e. it tends to re-establish the properties of the uninjured tissue. However, homeostasis may never be reached, such that remodelling may also appear as a continuous pathological transformation of diseased tissues during aneurysm expansion, for example. A simple constitutive model for soft biological tissues that regards remodelling as homeostatic-driven turnover is developed. Specifically, the recoverable effective tissue damage, whose rate is the sum of a mechanical damage rate and a healing rate, serves as a scalar internal thermodynamic variable. In order to integrate the biochemical and biomechanical aspects of remodelling, the healing rate is, on the one hand, driven by mechanical stimuli, but, on the other hand, subjected to simple metabolic constraints. The proposed model is formulated in accordance with continuum damage mechanics within an open-system thermodynamics framework. The numerical implementation in an in-house finite-element code is described, particularized for Ogden hyperelasticity. Numerical examples illustrate the basic constitutive characteristics of the model and demonstrate its potential in representing aspects of remodelling of soft tissues. Simulation results are verified for their plausibility, but also validated against reported experimental data.

Biomechanical rupture risk assessment of abdominal aortic aneurysms based on a novel probabilistic rupture risk index

A rupture risk assessment is critical to the clinical treatment of abdominal aortic aneurysm (AAA) patients. The biomechanical AAA rupture risk assessment quantitatively integrates many known AAA rupture risk factors but the variability of risk predictions due to model input uncertainties remains a challenging limitation. This study derives a probabilistic rupture risk index (PRRI). Specifically, the uncertainties in AAA wall thickness and wall strength were considered, and wall stress was predicted with a state-of-the-art deterministic biomechanical model. The discriminative power of PRRI was tested in a diameter-matched cohort of ruptured (n = 7) and intact (n = 7) AAAs and compared to alternative risk assessment methods. Computed PRRI at 1.5 mean arterial pressure was significantly (p = 0.041) higher in ruptured AAAs (20.21(s.d. 14.15%)) than in intact AAAs (3.71(s.d. 5.77)%). PRRI showed a high sensitivity and specificity (discriminative power of 0.837) to discriminate between ruptured and intact AAA cases. The underlying statistical representation of stochastic data of wall thickness, wall strength and peak wall stress had only negligible effects on PRRI computations. Uncertainties in AAA wall stress predictions, the wide range of reported wall strength and the stochastic nature of failure motivate a probabilistic rupture risk assessment. Advanced AAA biomechanical modelling paired with a probabilistic rupture index definition as known from engineering risk assessment seems to be superior to a purely deterministic approach.

Local Diameter, Wall Stress, and Thrombus Thickness Influence the Local Growth of Abdominal Aortic Aneurysms

Purpose: To investigate the influence of the local diameter, the intraluminal thrombus (ILT) thickness, and wall stress on the local growth rate of abdominal aortic aneurysms. Methods: The infrarenal aortas of 90 asymptomatic abdominal aortic aneurysm (AAA) patients (mean age 70 years; 77 men) were retrospectively reconstructed from at least 2 computed tomography angiography scans (median follow-up of 1 year) and biomechanically analyzed with the finite element method. Each individual AAA model was automatically sliced orthogonally to the lumen centerline and represented by 100 cross sections with corresponding diameters, ILT thicknesses, and wall stresses. The data were grouped according to these parameters for comparison of differences among the variables. Results: Diameter growth was continuously distributed over the entire aneurysm sac, reaching absolute and relative median peaks of 3.06 mm/y and 7.3%/y, respectively. The local growth rate was dependent on the local baseline diameter, the local ILT thickness, and for wall segments not covered by ILT, also on the local wall stress level (all p<0.001). For wall segments that were covered by a thick ILT layer, wall stress did not affect the growth rate (p=0.08). Conclusion: Diameter is not only a strong global predictor but also a local predictor of aneurysm growth. In addition, and independent of the diameter, the ILT thickness and wall stress (for the ILT-free wall) also influence the local growth rate. The high stress sensitivity of nondilated aortic walls suggests that wall stress peaks could initiate AAA formation. In contrast, local diameters and ILT thicknesses determine AAA growth for dilated and ILT-covered aortic walls.

Biomechanical Rupture Risk Assessment: A Consistent and Objective Decision-Making Tool for Abdominal Aortic Aneurysm Patients

Abdominal aortic aneurysm (AAA) rupture is a local event in the aneurysm wall that naturally demands tools to assess the risk for local wall rupture. Consequently, global parameters like the maximum diameter and its expansion over time can only give very rough risk indications; therefore, they frequently fail to predict individual risk for AAA rupture. In contrast, the Biomechanical Rupture Risk Assessment (BRRA) method investigates the wall's risk for local rupture by quantitatively integrating many known AAA rupture risk factors like female sex, large relative expansion, intraluminal thrombus-related wall weakening, and high blood pressure. The BRRA method is almost 20 years old and has progressed considerably in recent years, it can now potentially enrich the diameter indication for AAA repair. The present paper reviews the current state of the BRRA method by summarizing its key underlying concepts (i.e., geometry modeling, biomechanical simulation, and result interpretation). Specifically, the validity of the underlying model assumptions is critically disused in relation to the intended simulation objective (i.e., a clinical AAA rupture risk assessment). Next, reported clinical BRRA validation studies are summarized, and their clinical relevance is reviewed. The BRRA method is a generic, biomechanics-based approach that provides several interfaces to incorporate information from different research disciplines. As an example, the final section of this review suggests integrating growth aspects to (potentially) further improve BRRA sensitivity and specificity. Despite the fact that no prospective validation studies are reported, a significant and still growing body of validation evidence suggests integrating the BRRA method into the clinical decision-making process (i.e., enriching diameter-based decision-making in AAA patient treatment).

Implementation and use of 3D pairwise geodesic distance fields for seeding abdominal aortic vessels

PURPOSE

Precise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery. To perform this task, most of the segmentation algorithms rely on seed points, and better-located seed points provide better initial positions for cross-sectional methods. Under non-optimal acquisition characteristics of daily clinical routine and complex morphology of these vessels, inserting seed points to all these small, but critically important vessels is a tedious, time-consuming, and error-prone task. Thus, in this paper, a novel strategy is developed to generate pathways between user-inserted seed points in order to initialize segmentation methods effectively.

METHOD

The proposed method requires only a single user-inserted seed for each vessel of interest for initializations. Starting from these initial seeds, it automatically generates pathways that span all vessels in between. To accomplish this, first, a geodesic mask is generated by adaptive thresholding, which reinforces the initial seeds to be kept in the vascular tree. Then, a novel implementation of 3D pairwise geodesic distance field (3D-PGDF) is utilized. It is shown that the minimal-valued geodesic of 3D-PGDF successfully defines a path linking the initial seeds as being the shortest geodesic. Moreover, the robustness of the minimum level set of the 3D-PGDF to local variations and regions of high curvature is increased by a region classification strategy, which adds partial geodesics to these critical regions.

RESULTS

The proposed method was applied to 19 challenging CT data sets obtained from four different scanners and compared to two benchmark methods. The first method is a high-precision technique with very long processing time (subvoxel precise multi-stencil fast marching-MSFM), while the second is a very fast method with lower accuracy (3D fast marching). The results, which are obtained using various measures, show that the pathways generated by the developed technique enable significantly higher segmentation performance than 3D fast marching and require much less computational power and time than MSFM.

CONCLUSION

The developed technique offers a useful tool for generating pathways between seed points with minimal user interaction. It guarantees to include all important vessels in a computationally effective manner and thus, it can be used to initialize segmentation methods for abdominal aortic tree.

Prediction of Rupture Sites in Abdominal Aortic Aneurysms After Finite Element Analysis

PURPOSE

To associate regions of highest local rupture risk from finite element analysis (FEA) to subsequent rupture sites in abdominal aortic aneurysms (AAA).

METHODS

This retrospective multicenter study analyzed computed tomography angiography (CTA) data from 13 asymptomatic AAA patients (mean age 76 years; 8 men) experiencing rupture at a later point in time between 2005 and 2011. All patients had CTA scans before and during the rupture event. FEA was performed to calculate peak wall stress (PWS), peak wall rupture risk (PWRR), rupture risk equivalent diameters (RRED), and the intraluminal thrombus volume (ILTV). PWS and PWRR locations in the prerupture state were compared with subsequent CTA rupture findings. Visible contrast extravasation was considered a definite (n=5) rupture sign, while a periaortic hematoma was an indefinite (n=8) sign. A statistical comparison was performed between the 13-patient asymptomatic AAA group before and during rupture and a 23-patient diameter-matched asymptomatic AAA control group that underwent elective surgery.

RESULTS

The asymptomatic AAAs before rupture showed significantly higher PWRR and RRED values compared to the matched asymptomatic AAA control group (median values 0.74 vs 0.52 and 77 vs 59 mm, respectively; p<0.0001 for both). No statistical differences could be found for PWS and ILTV. Ruptured AAAs showed the highest maximum diameters, PWRR, and RRED values. In 7 of the ruptured AAAs (2 definite and 5 indefinite rupture signs), CTA rupture sites correlated with prerupture PWRR locations.

CONCLUSION

The location of the PWRR in unruptured AAAs predicted future rupture sites in several cases. Asymptomatic AAA patients with high PWRR and RRED values have an increased rupture risk.

An approach for patient-specific multi-domain vascular mesh generation featuring spatially varying wall thickness modeling

In this work, we present a computationally efficient image-derived volume mesh generation approach for vasculatures that implements spatially varying patient-specific wall thickness with a novel inward extrusion of the wall surface mesh. Multi-domain vascular meshes with arbitrary numbers, locations, and patterns of both iliac bifurcations and thrombi can be obtained without the need to specify features or landmark points as input. In addition, the mesh output is coordinate-frame independent and independent of the image grid resolution with high dimensional accuracy and mesh quality, devoid of errors typically found in off-the-shelf image-based model generation workflows. The absence of deformable template models or Cartesian grid-based methods enables the present approach to be sufficiently robust to handle aneurysmatic geometries with highly irregular shapes, arterial branches nearly parallel to the image plane, and variable wall thickness. The assessment of the methodology was based on i) estimation of the surface reconstruction accuracy, ii) validation of the output mesh using an aneurysm phantom, and iii) benchmarking the volume mesh quality against other frameworks. For the phantom image dataset (pixel size 0.105 mm; slice spacing 0.7 mm; and mean wall thickness 1.401±0.120 mm), the average wall thickness in the mesh was 1.459±0.123 mm. The absolute error in average wall thickness was 0.060±0.036 mm, or about 8.6% of the largest image grid spacing (0.7 mm) and 4.36% of the actual mean wall thickness. Mesh quality metrics and the ability to reproduce regional variations of wall thickness were found superior to similar alternative frameworks.

Synergy between shear-induced migration and secondary flows on red blood cells transport in arteries: considerations on oxygen transport

Shear-induced migration of red blood cells (RBCs) is a well-known phenomenon characterizing blood flow in the small vessels (micrometre to millimetre size) of the cardiovascular system. In large vessels, like the abdominal aorta and the carotid artery (millimetre to centimetre size), the extent of this migration and its interaction with secondary flows has not been fully elucidated. RBC migration exerts its influence primarily on platelet concentration, oxygen transport and oxygen availability at the luminal surface, which could influence vessel wall disease processes in and adjacent to the intima. Phillips' shear-induced particle migration model, coupled to the Quemada viscosity model, was employed to simulate the macroscopic behaviour of RBCs in four patient-specific geometries: a normal abdominal aorta, an abdominal aortic aneurysm (AAA), a normal carotid bifurcation and a stenotic carotid bifurcation. Simulations show a migration of RBCs from the near-wall region with a lowering of wall haematocrit (volume fraction of RBCs) on the posterior side of the normal aorta and on the lateral-external side of the iliac arteries. A marked migration is observed on the outer wall of the carotid sinus, along the common carotid artery and in the carotid stenosis. No significant migration is observed in the AAA. The spatial and temporal patterns of wall haematocrit are correlated with the near-wall shear layer and with the secondary flows induced by the vessel curvature. In particular, secondary flows accentuate the initial lowering in RBC near-wall concentration by convecting RBCs from the inner curvature side to the outer curvature side. The results reinforce data in literature showing a decrease in oxygen partial pressure on the inner curvature wall of the carotid sinus induced by the presence of secondary flows. The lowering of wall haematocrit is postulated to induce a decrease in oxygen availability at the luminal surface through a diminished concentration of oxyhaemoglobin, hence contributing, with the reported lowered oxygen partial pressure, to local hypoxia.

Finite element analysis in asymptomatic, symptomatic, and ruptured abdominal aortic aneurysms: in search of new rupture risk predictors

OBJECTIVES

To compare biomechanical rupture risk parameters of asymptomatic, symptomatic and ruptured abdominal aortic aneurysms (AAA) using finite element analysis (FEA).

STUDY DESIGN

Retrospective biomechanical single center analysis of asymptomatic, symptomatic, and ruptured AAAs. Comparison of biomechanical parameters from FEA.

MATERIALS AND METHODS

From 2011 to 2013 computed tomography angiography (CTA) data from 30 asymptomatic, 15 symptomatic, and 15 ruptured AAAs were collected consecutively. FEA was performed according to the successive steps of AAA vessel reconstruction, segmentation and finite element computation. Biomechanical parameters Peak Wall Rupture Risk Index (PWRI), Peak Wall Stress (PWS), and Rupture Risk Equivalent Diameter (RRED) were compared among the three subgroups.

RESULTS

PWRI differentiated between asymptomatic and symptomatic AAAs (p < .0004) better than PWS (p < .1453). PWRI-dependent RRED was higher in the symptomatic subgroup compared with the asymptomatic subgroup (p < .0004). Maximum AAA external diameters were comparable between the two groups (p < .1355). Ruptured AAAs showed the highest values for external diameter, total intraluminal thrombus volume, PWS, RRED, and PWRI compared with asymptomatic and symptomatic AAAs. In contrast with symptomatic and ruptured AAAs, none of the asymptomatic patients had a PWRI value >1.0. This threshold value might identify patients at imminent risk of rupture.

CONCLUSIONS

From different FEA derived parameters, PWRI distinguishes most precisely between asymptomatic and symptomatic AAAs. If elevated, this value may represent a negative prognostic factor for asymptomatic AAAs.

A 3D model of thrombus formation in a stent-graft after implantation in the abdominal aorta

Here we present a 3D kinetic model of thrombus formation in an endovascular prosthesis after implantation in an abdominal aorta with an aneurysm. The computational fluid dynamic technique (CFD) was used to determine the process of thrombus formation and growth in the stent-graft on the basis of the medical data from computed tomography angiography and Doppler ultrasound examination of 10 patients. The Quemada model was used to describe rheological properties of blood. Results of the CFD simulations were validated based on actual data from patients with diagnosed thrombi in aortic implants. The results show that the elaborated CFD model correctly predicted thrombus formation, shape and deposition site in an endovascular prosthesis. The developed CFD model of thrombus growth can be applied to predict the risk of thrombus formation in stent-grafts and assist in selection of geometry of the endovascular prosthesis to reduce possible complications after stent-graft implantation using only basic medical data.

In vivo strain assessment of the abdominal aortic aneurysm

The only criteria currently used to inform surgical decision for abdominal aortic aneurysms are maximum diameter (>5.5 cm) and rate of growth, even though several studies have identified the need for more specific indicators of risk. Patient-specific biomechanical variables likely to affect rupture risk would be a valuable addition to the science of understanding rupture risk and prove to be a life saving benefit for patients. Local deformability of the aorta is related to the local mechanical properties of the wall and may provide indication on the state of weakening of the wall tissue. We propose a 3D image-based approach to compute aortic wall strain maps in vivo. The method is applicable to a variety of imaging modalities that provide sequential images at different phases in the cardiac cycle. We applied the method to a series of abdominal aneurysms imaged using cine-MRI obtaining strain maps at different phases in the cardiac cycle. These maps could be used to evaluate the distensibility of an aneurysm at baseline and at different follow-up times and provide an additional index to clinicians to facilitate decisions on the best course of action for a specific patient.

The importance of patient-specific regionally varying wall thickness in abdominal aortic aneurysm biomechanics

Abdominal aortic aneurysm (AAA) is a vascular condition where the use of a biomechanics-based assessment for patient-specific risk assessment is a promising approach for clinical management of the disease. Among various factors that affect such assessment, AAA wall thickness is expected to be an important factor. However, regionally varying patient-specific wall thickness has not been incorporated as a modeling feature in AAA biomechanics. To the best our knowledge, the present work is the first to incorporate patient-specific variable wall thickness without an underlying empirical assumption on its distribution for AAA wall mechanics estimation. In this work, we present a novel method for incorporating regionally varying wall thickness (the "PSNUT" modeling strategy) in AAA finite element modeling and the application of this method to a diameter-matched cohort of 28 AAA geometries to assess differences in wall mechanics originating from the conventional assumption of a uniform wall thickness. For the latter, we used both a literature-derived population average wall thickness (1.5 mm; the "UT" strategy) as well as the spatial average of our patient-specific variable wall thickness (the "PSUT" strategy). For the three different wall thickness modeling strategies, wall mechanics were assessed by four biomechanical parameters: the spatial maxima of the first principal stress, strain, strain-energy density, and displacement. A statistical analysis was performed to address the hypothesis that the use of any uniform wall thickness model resulted in significantly different biomechanical parameters compared to a patient-specific regionally varying wall thickness model. Statistically significant differences were obtained with the UT modeling strategy compared to the PSNUT strategy for the spatial maxima of the first principal stress (p = 0.002), strain (p = 0.0005), and strain-energy density (p = 7.83 e-5) but not for displacement (p = 0.773). Likewise, significant differences were obtained comparing the PSUT modeling strategy with the PSNUT strategy for the spatial maxima of the first principal stress (p = 9.68 e-7), strain (p = 1.03 e-8), strain-energy density (p = 9.94 e-8), and displacement (p = 0.0059). No significant differences were obtained comparing the UT and PSUT strategies for the spatial maxima of the first principal stress (p = 0.285), strain (p = 0.152), strain-energy density (p = 0.222), and displacement (p = 0.0981). This work strongly recommends the use of patient-specific regionally varying wall thickness derived from the segmentation of abdominal computed tomography (CT) scans if the AAA finite element analysis is focused on estimating peak biomechanical parameters, such as stress, strain, and strain-energy density.

Review: the role of biomechanical modeling in the rupture risk assessment for abdominal aortic aneurysms

AAA disease is a serious condition and a multidisciplinary approach including biomechanics is needed to better understand and more effectively treat this disease. A rupture risk assessment is central to the management of AAA patients, and biomechanical simulation is a powerful tool to assist clinical decisions. Central to such a simulation approach is a need for robust and physiologically relevant models. Vascular tissue senses and responds actively to changes in its mechanical environment, a crucial tissue property that might also improve the biomechanical AAA rupture risk assessment. Specifically, constitutive modeling should not only focus on the (passive) interaction of structural components within the vascular wall, but also how cells dynamically maintain such a structure. In this article, after specifying the objectives of an AAA rupture risk assessment, the histology and mechanical properties of AAA tissue, with emphasis on the wall, are reviewed. Then a histomechanical constitutive description of the AAA wall is introduced that specifically accounts for collagen turnover. A test case simulation clearly emphasizes the need for constitutive descriptions that remodels with respect to the mechanical loading state. Finally, remarks regarding modeling of realistic clinical problems and possible future trends conclude the article.

The role of geometric and biomechanical factors in abdominal aortic aneurysm rupture risk assessment

The current clinical management of abdominal aortic aneurysm (AAA) disease is based to a great extent on measuring the aneurysm maximum diameter to decide when timely intervention is required. Decades of clinical evidence show that aneurysm diameter is positively associated with the risk of rupture, but other parameters may also play a role in causing or predisposing the AAA to rupture. Geometric factors such as vessel tortuosity, intraluminal thrombus volume, and wall surface area are implicated in the differentiation of ruptured and unruptured AAAs. Biomechanical factors identified by means of computational modeling techniques, such as peak wall stress, have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone. The objective of this review is to examine these factors, which are found to influence AAA disease progression, clinical management and rupture potential, as well as to highlight on-going research by our group in aneurysm modeling and rupture risk assessment.

Multidimensional growth measurements of abdominal aortic aneurysms

BACKGROUND

Monitoring the expansion of abdominal aortic aneurysms (AAAs) is critical to avoid aneurysm rupture in surveillance programs, for instance. However, measuring the change of the maximum diameter over time can only provide limited information about AAA expansion. Specifically, regions of fast diameter growth may be missed, axial growth cannot be quantified, and shape changes of potential interest for decisions related to endovascular aneurysm repair cannot be captured.

METHODS

This study used multiple centerline-based diameter measurements between the renal arteries and the aortic bifurcation to quantify AAA growth in 51 patients from computed tomography angiography (CTA) data. Criteria for inclusion were at least 1 year of patient follow-up and the availability of at least two sufficiently high-resolution CTA scans that allowed an accurate three-dimensional reconstruction. Consequently, 124 CTA scans were systematically analyzed by using A4clinics diagnostic software (VASCOPS GmbH, Graz, Austria), and aneurysm growth was monitored at 100 cross-sections perpendicular to the centerline.

RESULTS

Monitoring diameter development over the entire aneurysm revealed the sites of the fastest diameter growth, quantified the axial growth, and showed the evolution of the neck morphology over time. Monitoring the development of an aneurysm's maximum diameter or its volume over time can assess the mean diameter growth (r = 0.69, r = 0.77) but not the maximum diameter growth (r = 0.43, r = 0.34). The diameter growth measured at the site of maximum expansion was ~16%/y, almost four times larger than the mean diameter expansion of 4.4%/y. The sites at which the maximum diameter growth was recorded did not coincide with the position of the maximum baseline diameter (ρ = 0 .12; P = .31). The overall aneurysm sac length increased from 84 to 89 mm during the follow-up (P < .001), which relates to the median longitudinal growth of 3.5%/y. The neck length shortened, on average, by 6.2% per year and was accompanied by a slight increase in neck angulation.

CONCLUSIONS

Neither maximum diameter nor volume measurements over time are able to measure the fastest diameter growth of the aneurysm sac. Consequently, expansion-related wall weakening might be inappropriately reflected by this type of surveillance data. In contrast, localized spots of fast diameter growth can be detected through multiple centerline-based diameter measurements over the entire aneurysm sac. This information might further reinforce the quality of aneurysm surveillance programs.

A pull-back algorithm to determine the unloaded vascular geometry in anisotropic hyperelastic AAA passive mechanics

Biomechanical studies on abdominal aortic aneurysms (AAA) seek to provide for better decision criteria to undergo surgical intervention for AAA repair. More accurate results can be obtained by using appropriate material models for the tissues along with accurate geometric models and more realistic boundary conditions for the lesion. However, patient-specific AAA models are generated from gated medical images in which the artery is under pressure. Therefore, identification of the AAA zero pressure geometry would allow for a more realistic estimate of the aneurysmal wall mechanics. This study proposes a novel iterative algorithm to find the zero pressure geometry of patient-specific AAA models. The methodology allows considering the anisotropic hyperelastic behavior of the aortic wall, its thickness and accounts for the presence of the intraluminal thrombus. Results on 12 patient-specific AAA geometric models indicate that the procedure is computational tractable and efficient, and preserves the global volume of the model. In addition, a comparison of the peak wall stress computed with the zero pressure and CT-based geometries during systole indicates that computations using CT-based geometric models underestimate the peak wall stress by 59 ± 64 and 47 ± 64 kPa for the isotropic and anisotropic material models of the arterial wall, respectively.