Correlation of biomechanics to tissue reaction in aortic aneurysms assessed by finite elements and [18F]-fluorodeoxyglucose-PET/CT

Prospect of positron emission tomography for abdominal aortic aneurysm risk stratification

Abdominal aortic aneurysm (AAA) disease is characterized by an asymptomatic, permanent, focal dilatation of the abdominal aorta progressing towards rupture, which confers significant mortality. Patient management and surgical decisions rely on aortic diameter measurements via abdominal ultrasound surveillance. However, AAA rupture can occur at small diameters or may never occur at large diameters, implying that anatomical size is not necessarily a sufficient indicator. Molecular imaging may help identify high-risk patients through AAA evaluation independent of aneurysm size, and there is the question of the potential role of positron emission tomography (PET) and emerging role of novel radiotracers for AAA. Therefore, this review summarizes PET studies conducted in the last 10 years and discusses the usefulness of PET radiotracers for AAA risk stratification. The most frequently reported radiotracer was [18F]fluorodeoxyglucose, indicating inflammatory activity and reflecting the biomechanical properties of AAA. Emerging radiotracers include [18F]-labeled sodium fluoride, a calcification marker, [64Cu]DOTA-ECL1i, an indicator of chemokine receptor type 2 expression, and [18F]fluorothymidine, a marker of cell proliferation. For novel radiotracers, preliminary trials in patients are warranted before their widespread clinical implementation. AAA rupture risk is challenging to evaluate; therefore, clinicians may benefit from PET-based risk assessment to guide patient management and surgical decisions.

Predictors of Abdominal Aortic Aneurysm Risks

Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment.

In silico study of vessel and stent-graft parameters on the potential success of endovascular aneurysm repair

The variety of stent-graft (SG) design variables (eg, SG type and degree of SG oversizing) and the complexity of decision making whether a patient is suitable for endovascular aneurysm repair (EVAR) raise the need for the development of predictive tools to assist clinicians in the preinterventional planning phase. Recently, some in silico EVAR methods have been developed to predict the deployed SG configuration. However, only few studies investigated how to assess the in silico EVAR outcome with respect to EVAR complication likelihoods (eg, endoleaks and SG migration). Based on a large literature study, in this contribution, 20 mechanical and geometrical parameters (eg, SG drag force and SG fixation force) are defined to evaluate the quality of the in silico EVAR outcome. For a cohort of n = 146 realizations of parameterized vessel and SG geometries, the in silico EVAR results are studied with respect to these mechanical and geometrical parameters. All degrees of SG oversizing in the range between 5% and 40% are investigated continuously by a computationally efficient parameter continuation approach. The in silico investigations have shown that the mechanical and geometrical parameters are able to indicate candidates at high risk of postinterventional complications. Hence, this study provides the basis for the development of a simulation-based metric to assess the potential success of EVAR based on engineering parameters.

Biobanking: Objectives, Requirements, and Future Challenges-Experiences from the Munich Vascular Biobank

Collecting biological tissue samples in a biobank grants a unique opportunity to validate diagnostic and therapeutic strategies for translational and clinical research. In the present work, we provide our long-standing experience in establishing and maintaining a biobank of vascular tissue samples, including the evaluation of tissue quality, especially in formalin-fixed paraffin-embedded specimens (FFPE). Our Munich Vascular Biobank includes, thus far, vascular biomaterial from patients with high-grade carotid artery stenosis (n = 1567), peripheral arterial disease (n = 703), and abdominal aortic aneurysm (n = 481) from our Department of Vascular and Endovascular Surgery (January 2004–December 2018). Vascular tissue samples are continuously processed and characterized to assess tissue morphology, histological quality, cellular composition, inflammation, calcification, neovascularization, and the content of elastin and collagen fibers. Atherosclerotic plaques are further classified in accordance with the American Heart Association (AHA), and plaque stability is determined. In order to assess the quality of RNA from FFPE tissue samples over time (2009–2018), RNA integrity number (RIN) and the extent of RNA fragmentation were evaluated. Expression analysis was performed with two housekeeping genes—glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and beta-actin (ACTB)—using TaqMan-based quantitative reverse-transcription polymerase chain reaction (qRT)-PCR. FFPE biospecimens demonstrated unaltered RNA stability over time for up to 10 years. Furthermore, we provide a protocol for processing tissue samples in our Munich Vascular Biobank. In this work, we demonstrate that biobanking is an important tool not only for scientific research but also for clinical usage and personalized medicine.

Bayesian Inference-Based Estimation of Normal Aortic, Aneurysmal and Atherosclerotic Tissue Mechanical Properties: From Material Testing, Modeling and Histology

OBJECTIVE

Mechanical properties of healthy, aneurysmal, and atherosclerotic arterial tissues are essential for assessing the risk of lesion development and rupture. Strain energy density function (SEDF) has been widely used to describe these properties, where material constants of the SEDF are traditionally determined using the ordinary least square (OLS) method. However, the material constants derived using OLS are usually dependent on initial guesses.

METHODS

To avoid such dependencies, Bayesian inference-based estimation was used to fit experimental stress-stretch curves of 312 tissue strips from 8 normal aortas, 19 aortic aneurysms, and 21 carotid atherosclerotic plaques to determine the constants, C₁, D₁, and D₂ of the modified Mooney-Rivlin SEDF.

RESULTS

Compared with OLS, material constants varied much less with prior in the Bayesian inference-based estimation. Moreover, fitted material constants differed amongst distinct tissue types. Atherosclerotic tissues associated with the biggest D₂, an indicator of the rate of increase in stress during stretching, followed by aneurysmal tissues and those from normal aortas. Histological analyses showed that C₁ and D₂ were associated with elastin content and details of the collagen configuration, specifically, waviness and dispersion, in the structure.

CONCLUSION

Bayesian inference-based estimation robustly determines material constants in the modified Mooney-Rivlin SEDF and these constants can reflect the inherent physiological and pathological features of the tissue structure.

SIGNIFICANCE

This study suggested a robust procedure to determine the material constants in SEDF and demonstrated that the obtained constants can be used to characterize tissues from different types of lesions, while associating with their inherent microstructures.

Exploring the Biological and Mechanical Properties of Abdominal Aortic Aneurysms Using USPIO MRI and Peak Tissue Stress: A Combined Clinical and Finite Element Study

Inflammation detected through the uptake of ultrasmall superparamagnetic particles of iron oxide (USPIO) on magnetic resonance imaging (MRI) and finite element (FE) modelling of tissue stress both hold potential in the assessment of abdominal aortic aneurysm (AAA) rupture risk. This study aimed to examine the spatial relationship between these two biomarkers. Patients (n = 50) > 40 years with AAA maximum diameters > = 40 mm underwent USPIO-enhanced MRI and computed tomography angiogram (CTA). USPIO uptake was compared with wall stress predictions from CTA-based patient-specific FE models of each aneurysm. Elevated stress was commonly observed in areas vulnerable to rupture (e.g. posterior wall and shoulder). Only 16% of aneurysms exhibited co-localisation of elevated stress and mural USPIO enhancement. Globally, no correlation was observed between stress and other measures of USPIO uptake (i.e. mean or peak). It is suggested that cellular inflammation and stress may represent different but complimentary aspects of AAA disease progression.

High Structural Stress and Presence of Intraluminal Thrombus Predict Abdominal Aortic Aneurysm 18F-FDG Uptake: Insights From Biomechanics

Supplemental Digital Content is available in the text.

Background—

Abdominal aortic aneurysm (AAA) wall inflammation and mechanical structural stress may influence AAA expansion and lead to rupture. We hypothesized a positive correlation between structural stress and fluorine-18-labeled 2-deoxy-2-fluoro-d-glucose (¹⁸F-FDG) positron emission tomography–defined inflammation. We also explored the influence of computed tomography–derived aneurysm morphology and composition, including intraluminal thrombus, on both variables.

Methods and Results—

Twenty-one patients (19 males) with AAAs below surgical threshold (AAA size was 4.10±0.54 cm) underwent ¹⁸F-FDG positron emission tomography and contrast-enhanced computed tomography imaging. Structural stresses were calculated using finite element analysis. The relationship between maximum aneurysm ¹⁸F-FDG standardized uptake value within aortic wall and wall structural stress, patient clinical characteristics, aneurysm morphology, and compositions was explored using a hierarchical linear mixed-effects model. On univariate analysis, local aneurysm diameter, thrombus burden, extent of calcification, and structural stress were all associated with ¹⁸F-FDG uptake (P<0.05). AAA structural stress correlated with ¹⁸F-FDG maximum standardized uptake value (slope estimate, 0.552; P<0.0001). Multivariate linear mixed-effects analysis revealed an important interaction between structural stress and intraluminal thrombus in relation to maximum standardized uptake value (fixed effect coefficient, 1.68 [SE, 0.10]; P<0.0001). Compared with other factors, structural stress was the best predictor of inflammation (receiver-operating characteristic curve area under the curve =0.59), with higher accuracy seen in regions with high thrombus burden (area under the curve =0.80). Regions with both high thrombus burden and high structural stress had higher ¹⁸F-FDG maximum standardized uptake value compared with regions with high thrombus burdens but low stress (median [interquartile range], 1.93 [1.60–2.14] versus 1.14 [0.90–1.53]; P<0.0001).

Conclusions—

Increased aortic wall inflammation, demonstrated by ¹⁸F-FDG positron emission tomography, was observed in AAA regions with thick intraluminal thrombus subjected to high mechanical stress, suggesting a potential mechanistic link underlying aneurysm inflammation.

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.

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).

An improved baseline model for a human arterial network to study the impact of aneurysms on pressure-flow waveforms

In this study, an improved and robust one-dimensional human arterial network model is presented. The one-dimensional blood flow equations are solved using the Taylor-locally conservative Galerkin finite element method. The model improvements are carried out by adopting parts of the physical models from different authors to establish an accurate baseline model. The predicted pressure-flow waveforms at various monitoring positions are compared against in vivo measurements from published works. The results obtained show that wave shapes predicted are similar to that of the experimental data and exhibit a good overall agreement with measured waveforms. Finally, computational studies on the influence of an abdominal aortic aneurysm are presented. The presence of aneurysms results in a significant change in the waveforms throughout the network.

Measuring and modeling patient-specific distributions of material properties in abdominal aortic aneurysm wall

Both the clinically established diameter criterion and novel approaches of computational finite element (FE) analyses for rupture risk stratification of abdominal aortic aneurysms (AAA) are based on assumptions of population-averaged, uniform material properties for the AAA wall. The presence of inter-patient and intra-patient variations in material properties is known, but has so far not been addressed sufficiently. In order to enable the preoperative estimation of patient-specific AAA wall properties in the future, we investigated the relationship between non-invasively assessable clinical parameters and experimentally measured AAA wall properties. We harvested n = 163 AAA wall specimens (n = 50 patients) during open surgery and recorded the exact excision sites. Specimens were tested for their thickness, elastic properties, and failure loads using uniaxial tensile tests. In addition, 43 non-invasively assessable patient-specific or specimen-specific parameters were obtained from recordings made during surgery and patient charts. Experimental results were correlated with the non-invasively assessable parameters and simple regression models were created to mathematically describe the relationships. Wall thickness was most significantly correlated with the metabolic activity at the excision site assessed by PET/CT (ρ = 0.499, P = 4 × 10(-7)) and to thrombocyte counts from laboratory blood analyses (ρ = 0.445, P = 3 × 10(-9)). Wall thickness was increased in patients suffering from diabetes mellitus, while it was significantly thinner in patients suffering from chronic kidney disease (CKD). Elastic AAA wall properties had significant correlations with the metabolic activity at the excision site (PET/CT), with existent calcifications, and with the diameter of the non-dilated aorta proximal to the AAA. Failure properties (wall strength and failure tension) had correlations with the patient's medical history and with results from laboratory blood analyses. Interestingly, AAA wall failure tension was significantly reduced for patients with CKD and elevated blood levels of potassium and urea, respectively, both of which are associated with kidney disease. This study is a first step to a future preoperative estimation of AAA wall properties. Results can be conveyed to both the diameter criterion and FE analyses to refine rupture risk prediction. The fact that AAA wall from patients suffering from CKD featured reduced failure tension implies an increased AAA rupture risk for this patient group at comparably smaller AAA diameters.