Multidimensional growth measurements of abdominal aortic aneurysms

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair

PURPOSE

Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the accuracy of ARVA when analyzing preoperative and postoperative computed tomography angiography (CTA) in patients managed with fenestrated endovascular repair (FEVAR) for complex aortic aneurysms (cAAs).

MATERIALS AND METHODS

Preoperative and postoperative CTAs from 50 patients (n=100 CTAs) who underwent FEVAR for cAAs were extracted from the picture archiving and communication system (PACS) of a single aortic center equipped with ARVA. All studies underwent automated AI aneurysm morphology assessment by ARVA. Appropriate identification of the outer wall of the aorta was verified by manual review of the AI-generated overlays for each patient. Maximum outer-wall aortic diameters were measured by 2 clinicians using multiplanar reconstruction (MPR) and curved planar reformatting (CPR), and among studies where the aortic wall was appropriately identified by ARVA, they were compared with ARVA automated measurements.

RESULTS

Identification of the outer wall of the aorta was accurate in 89% of CTA studies. Among these, diameter measurements by ARVA were comparable to clinician measurements by MPR or CPR, with a median absolute difference of 2.4 mm on the preoperative CTAs and 1.6 mm on the postoperative CTAs. Of note, no significant difference was detected between clinician measurements using MPR or CPR on preoperative and postoperative scans (range 0.5-0.9 mm).

CONCLUSION

For patients with cAAs managed with FEVAR, ARVA provides accurate preoperative and postoperative assessment of aortic diameter in 89% of studies. This technology may provide an opportunity to automate cAA morphology assessment in most cases where time-intensive, manual clinician measurements are currently required.

CLINICAL IMPACT

In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

OBJECTIVE

ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth.

MATERIAL AND METHODS

70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.

RESULTS

the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.

CONCLUSION

global shape features might provide an important contribution for predicting the aneurysm growth.

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.

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.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.

Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their risk of rupture - which is the 13th leading cause of death in the US - exceeds the risks associated with repair. Clinical intervention occurs when an aneurysm diameter exceeds 5.5 cm, but this "one-size fits all" criterion is insufficient, as it has been reported thatup to a quarter of AAA smaller than 5.5 cm do rupture. Therefore, there is a need for a more reliable, patient-specific, clinical tool to aide in the management of AAA. Biomechanical assessment of AAA is thought to provide critical physical insights to rupture risk, but clinical translataion of biomechanics-based tools has been limited due to the expertise, time, and computational requirements. It was estimated that through 2015, only 348 individual AAA cases have had biomechanical stress analysis performed, suggesting a deficient sample size to make such analysis relevant in the clinic. Artificial intelligence (AI) algorithms offer the potential to increase the throughput of AAA biomechanical analyses by reducing the overall time required to assess the wall stresses in these complex structures using traditional methods. This can be achieved by automatically segmenting regions of interest from medical images and using machine learning models to predict wall stresses of AAA. In this study, we present an automated AI-based methodology to predict the biomechanical wall stresses for individual AAA. The predictions using this approach were completed in a significantly less amount of time compared to a more traditional approach (~4 hours vs 20 seconds).

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.

Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management

OBJECTIVE

The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a "master curve" of AAA growth, and tests which parameters serve to enhance its predictability for clinical use.

METHODS

106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization.

RESULTS

An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates.

CONCLUSIONS

The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.

3D morphometric analysis of ascending aorta as an adjunctive tool to predict type A acute aortic dissection

Background

Acute type A aortic dissection (AAAD) is a pathological process that implicates the ascending aorta and represents a surgical emergency burdened by high mortality if not promptly treated in the first hours of onset. Despite best efforts, the annual incidence rates of aortic dissection has remained stable over the past decades. We measured aortic dimensions (aortic diameters, area, length and volume) using 3D multiplanar reconstruction imaging with the purpose of refining the risk- morphology for AAAD.

Methods

Computerized tomography angiography studies of three groups were compared retrospectively: patients affected by AAAD (AAAD group; n=71), patients affected by aortic aneurysm and subsequently subjected to ascending aorta replacement (Aneurysm, n=77) and a healthy aorta’s group (Control, n=75).

Results

Mean diameters of AAAD (4.9 cm) and Aneurysm (5.1 cm) aortas were significantly larger than those of the control group (3.4 cm). In AAAD patients, an ascending aorta diameter greater than 5.5 cm was observed in 18% of patients. Multiple comparisons showed statistically significant differences among mean of the ratio of aortic root area to height between the three groups (P<0.001). In frontal and sagittal planes, the length of the ascending aorta was significantly greater in patients affected by aortic pathology (AAAD and aneurysm) than in the control group (P<0.001). Significant differences were confirmed when indexing the aortic length to patient’s height and BSA, and the aortic volume to patient’s BSA.

Conclusions

Maximum transverse diameter, considered separately, is not the best predictor of aortic dissection. In our opinion, the introduction into clinical practice of measurements of the area, length, and volume of the aorta, as absolute or indexed values, could improve the selection of patients who would benefit from preventive surgical aortic replacement.

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.

Evaluating the diagnostic accuracy of maximal aortic diameter, length and volume for prediction of aortic dissection

OBJECTIVE

Management of thoracic aortic aneurysms (TAAs) comprises regular diameter follow-up until the indication criterion for prophylactic surgery is reached. However, this approach is unable to predict the majority of acute type A aortic dissections (ATAADs). The current study aims to evaluate the diagnostic accuracy of ascending aortic diameter, length and volume for occurrence of ATAAD.

METHODS

This two-centre observational cohort study retrospectively screened 477 consecutive patients who presented with ATAAD between 2009 and 2018. Of those, 25 (5.2%) underwent CT angiography (CTA) within 2 years before dissection onset. Aortic diameter, length and volume of these patients ('pre-ATAAD') were compared with those of TAA controls (n=75). Receiver operating curve analysis was performed to evaluate the predictive accuracy of the three different measurements.

RESULTS

96% of patients with pre-ATAAD did not meet the surgical diameter threshold of 55 mm before dissection onset. Maximal aortic diameters (45 (40-49) mm vs 46 (44-49) mm, p=0.075) and volume (126 (95-157) cm³ vs 124 (102-136) cm³, p=0.909) were comparable between patients with pre-ATAAD and TAA controls. Conversely, ascending aortic length (84±9 mm vs 90±16 mm, p=0.031) was significantly larger in patients with pre-ATAAD. All three parameters had an area under the curve of >0.800. At the 55 mm cut-off point, the maximal diameter yielded a positive predictive value (PPV) of 20%. While maintaining same specificity levels, measurements of aortic volume and length showed superior diagnostic accuracy (PPV 55% and 70%, respectively).

CONCLUSION

Measurements of aortic volume and length have superior diagnostic accuracy compared with the maximal diameter and could improve the timely identification of patients at risk for ATAAD.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

METHODS

106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

RESULTS

The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

Predictive imaging for thoracic aortic dissection and rupture: moving beyond diameters

Abstract

Acute aortic syndromes comprise a group of potentially fatal conditions that result from weakening of the aortic vessel wall. Pre-emptive surgical intervention is currently reserved for patients with severe aortic dilatation, although abundant evidence describes the occurrence of dissection and rupture in aortas with diameters below surgical thresholds. Modern imaging techniques (such as hybrid PET-CT and 4D flow MRI) afford the non-invasive assessment of anatomic, hemodynamic, and molecular features of the aorta, and may provide for a more accurate selection of patients who will benefit from preventative surgical intervention. In the current review, we summarize evidence and considerations regarding predictive aortic imaging and highlight evolving imaging modalities that have shown promise to improve risk assessment for the occurrence of dissection and rupture.

Key Points

• Guidelines for the preventative management of aortic disease depend on maximal vessel diameters, while these have shown to be poor predictors for the occurrence of catastrophic acute aortic events.

• Evolving imaging modalities (such as 4D flow MRI and hybrid PET-CT) afford a more comprehensive insight into anatomic, hemodynamic, and molecular features of the aorta and have shown promise to detect vessel wall instability at an early stage.

Spatial Distribution of Abdominal Aortic Aneurysm Surface Expansion and Correlation With Maximum Diameter and Volume Growth

BACKGROUND

Abdominal aortic aneurysm (AAA) growth rate, measured as maximum diameter (Dmax) change over time, is used as a surrogate marker of rupture risk. However, AAA expansion presents significant spatial variability. We aim to record the spatial distribution of regional wall surface expansion.

METHODS

Thirty AAAs were retrospectively studied. Each AAA had one baseline and at least one follow-up computed tomography scan. Three-dimensional AAA models were reconstructed, and change in Dmax and total aneurysm volume was recorded to calculate annual growth rates. Regional surface growth was quantified using the VascForm algorithm, which is based on nonrigid point cloud registration and iterative closest point analysis. Maximum and average surface growths were calculated and correlated with the diameter/volume growth rates. Furthermore, to identify potential correlation between maximum thrombus (intraluminal thrombus) thickness and maximum surface growth, as well as between peak wall stress (PWS) and surface growth, their colocalization was examined.

RESULTS

The median average annual surface growth was 6% (0%-28%), and the maximum surface growth 24% (11%-238%). There was strong evidence of a moderate correlation between Dmax and average as well as maximum surface growth. Regarding volumes, there was strong evidence of a very strong association with average surface growth rate and a moderate association with maximum surface growth rate (rho: 0.91, P < 0.001; rho: 0.7, P < 0.001, respectively). In 51.6% of the follow-ups, maximum surface growth occurred away from Dmax site. Sixteen cases presented maximum surface growth away and fifteen at the region of maximum initial intraluminal thrombus thickness. AAAs in the former group had significantly thinner initial intraluminal thrombus thickness (11.3 vs 19.5 mm, P < 0.001) than those in the latter. Apart from a single case, maximum surface growth did not occur at the PWS region.

CONCLUSIONS

More than half of the lesions display maximum growth away from Dmax, suggesting that a more accurate method of analyzing AAA growth needs to be established in clinical practice that will take into account local surface growth.

Inositol in the MAnaGemENt of abdominal aortic aneurysm (IMAGEN): study protocol for a randomised controlled trial

Background

An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta and is associated with a risk of fatal rupture. Experimental studies suggest that myo-inositol may exert beneficial effects on AAAs through favourable changes to biological pathways implicated in AAA pathology. The aim of the Inositol in the MAnaGemENt of abdominal aortic aneurysm (IMAGEN) trial is to assess if myo-inositol will reduce AAA growth.

Methods/design

IMAGEN is a multi-centre, prospective, parallel-group, randomised, double-blind, placebo-controlled trial. A total of 164 participants with an AAA measuring ≥ 30 mm will be randomised to either 2 g of myo-inositol or identical placebo twice daily for 12 months. The primary outcome measure will be AAA growth estimated by increase in total infrarenal aortic volume measured on computed tomographic scans. Secondary outcome measures will include AAA diameter assessed by computed tomography and ultrasound, AAA peak wall stress and peak wall rupture index, serum lipids, circulating AAA biomarkers, circulating RNAs and health-related quality of life. All analysis will be based on the intention-to-treat principle at the time of randomisation. All patients who meet the eligibility criteria, provide written informed consent and are enrolled in the study will be included in the primary analysis, regardless of adherence to dietary allocation.

Discussion

Currently, there is no known medical therapy to limit AAA progression. The IMAGEN trial will be the first randomised trial, to our knowledge, to assess the value of myo-inositol in limiting AAA growth.

Trial registration

Australian New Zealand Clinical Trials Registry, ACTRN12615001209583. Registered on 6 November 2015.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-017-2304-x) contains supplementary material, which is available to authorized users.

Use of Synchrotron Radiation to Accurately Assess Cross-Sectional Area Reduction of the Aortic Branch Ostia Caused by Suprarenal Stent Wires

PURPOSE

To compare in vivo the use of synchrotron radiation to computed tomography angiography (CTA) for the measurement of cross-sectional area (CSA) reduction of the aortic branch ostia caused by suprarenal stent-graft wires.

METHODS

This study was performed with a Zenith stent-graft placed in a phantom of the human aorta to simulate treatment of abdominal aortic aneurysm. Synchrotron radiation scans were performed using beam energies between 40 and 100 keV and spatial resolution of 19.88 μm per pixel. CSA reduction of the aortic branch ostia by suprarenal stent wires was calculated based on these exposure factors and compared with measurements from CTA images acquired on a 64-row scanner with slice thicknesses of 1.0, 1.5, and 2.0 mm.

RESULTS

Images acquired with synchrotron radiation showed <10% of the CSA occupied by stent wires when a single wire crossed a renal artery ostium and <20% for 2 wires crossing a renovisceral branch ostium. The corresponding areas ranged from 24% to 25% for a single wire and from 40% to 48% for double wires crossing the branch ostia when measured on CT images. The stent wire was accurately assessed on synchrotron radiation with a diameter between 0.38±0.01 and 0.53±0.03 mm, which is close to the actual size of 0.47±0.01 mm. The wire diameter measured on CT images was greatly overestimated (1.15±0.01 to 1.57±0.02 mm).

CONCLUSION

CTA has inferior spatial resolution that hinders accurate assessment of CSA reduction. This experiment demonstrated the superiority of synchrotron radiation over CTA for more accurate assessment of aortic stent wires and CSA reduction of the aortic branch ostia.

Growth Description for Vessel Wall Adaptation: A Thick-Walled Mixture Model of Abdominal Aortic Aneurysm Evolution

(1) Background: Vascular tissue seems to adapt towards stable homeostatic mechanical conditions, however, failure of reaching homeostasis may result in pathologies. Current vascular tissue adaptation models use many ad hoc assumptions, the implications of which are far from being fully understood; (2) Methods: The present study investigates the plausibility of different growth kinematics in modeling Abdominal Aortic Aneurysm (AAA) evolution in time. A structurally motivated constitutive description for the vessel wall is coupled to multi-constituent tissue growth descriptions; Constituent deposition preserved either the constituent’s density or its volume, and Isotropic Volume Growth (IVG), in-Plane Volume Growth (PVG), in-Thickness Volume Growth (TVG) and No Volume Growth (NVG) describe the kinematics of the growing vessel wall. The sensitivity of key modeling parameters is explored, and predictions are assessed for their plausibility; (3) Results: AAA development based on TVG and NVG kinematics provided not only quantitatively, but also qualitatively different results compared to IVG and PVG kinematics. Specifically, for IVG and PVG kinematics, increasing collagen mass production accelerated AAA expansion which seems counterintuitive. In addition, TVG and NVG kinematics showed less sensitivity to the initial constituent volume fractions, than predictions based on IVG and PVG; (4) Conclusions: The choice of tissue growth kinematics is of crucial importance when modeling AAA growth. Much more interdisciplinary experimental work is required to develop and validate vascular tissue adaption models, before such models can be of any practical use.

A novel approach for local abdominal aortic aneurysm growth quantification

Although aneurysm size still remains the most accepted predictor of rupture risk, abdominal aortic aneurysms (AAAs) with maximum diameter smaller than 5 cm may also rupture. Growth rate is an additional marker for rupture risk as it potentially reflects an undesirable wall remodeling that leads to fast regional growth. Currently, an indication for surgery is an expansion rate >10 mm/year, measured as change in maximum diameter over time. However, as AAA expansion is non-uniform, it is questionable whether measurement of maximum diameter change over time can capture increased localized remodeling activity. A method for estimating AAA surface area growth is introduced, providing a better measure of local wall deformation. The proposed approach is based on the non-rigid iterative closest point algorithm. Optimization and validation is performed using 12 patient-specific AAA geometries artificially deformed to produce a target surface with known nodal displacements. Mesh density sensitivity, range of uncertainty, and method limitations are discussed. Application to ten AAA patient-specific follow-ups suggested that maximum diameter growth does not correlate strongly with the maximum surface growth (R ² = 0.614), which is not always colocated with maximum diameter, or uniformly distributed. Surface growth quantification could reinforce the quality of aneurysm surveillance programs.

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).

Advances in determining abdominal aortic aneurysm size and growth

Abdominal aortic aneurysm is a common pathology in the aging population of the developed world which carries a significant mortality in excess of 80% in case of rupture. Aneurysmal disease probably represents the only surgical condition in which size is such a critical determinant of the need for intervention and therefore the ability to accurately and reproducibly record aneurysm size and growth over time is of outmost importance. In the same time that imaging techniques may be limited by intra- and inter-observer variability and there may be inconsistencies due to different modalities [ultrasound, computed tomography (CT)], rapid technologic advancement have taken aortic imaging to the next level. Digital imaging, multi-detector scanners, thin slice CT and most- importantly the ability to perform 3-dimensional reconstruction and image post-processing have currently become widely available rendering most of the imaging modalities used in the past out of date. The aim of the current article is to report on various imaging methods and current state of the art techniques used to record aneurysm size and growth. Moreover we aim to emphasize on the future research directions and report on techniques which probably will be widely used and incorporated in clinical practice in the near future.

Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine

Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to computationally investigate the effect of the spine for progression of the AAA. Medical image-based geometry of an aorta is constructed along with the spine surface, which is incorporated into the computational model as a cloud of points. The G&R simulation is initiated by local elastin degradation with different spatial distributions. The AAA-spine interaction is accounted for using a penalty method when the AAA surface meets the spine surface. The simulation results show that, while the radial growth of the AAA wall is prevented on the posterior side due to the spine acting as a constraint, the AAA expands faster on the anterior side, leading to higher curvature and asymmetry in the AAA configuration compared to the simulation excluding the spine. Accordingly, the AAA wall stress increases on the lateral, posterolateral, and the shoulder regions of the anterior side due to the AAA-spine contact. In addition, more collagen is deposited on the regions with a maximum diameter. We show that an image-based computational G&R model not only enhances the prediction of the geometry, wall stress, and strength distributions of AAAs but also provides a framework to account for the interactions between an enlarging AAA and the spine for a better rupture potential assessment and management of AAA patients.

On growth measurements of abdominal aortic aneurysms using maximally inscribed spheres

The maximum diameter, total volume of the abdominal aorta, and its growth rate are usually regarded as key factors for making a decision on the therapeutic operation time for an abdominal aortic aneurysm (AAA) patient. There is, however, a debate on what is the best standard method to measure the diameter. Currently, two dominant methods for measuring the maximum diameter are used. One is measured on the planes perpendicular to the aneurism's central line (orthogonal diameter) and the other one is measured on the axial planes (axial diameter). In this paper, another method called 'inscribed-spherical diameter' is proposed to measure the diameter. The main idea is to find the diameter of the largest sphere that fits within the aorta. An algorithm is employed to establish a centerline for the AAA geometries obtained from a set of longitudinal scans obtained from South Korea. This centerline, besides being the base of the inscribed spherical method, is used for the determination of orthogonal and axial diameter. The growth rate parameters are calculated in different diameters and the total volume and the correlations between them are studied. Furthermore, an exponential growth pattern is sought for the maximum diameters over time to examine a nonlinear growth pattern of AAA expansion both globally and locally. The results present the similarities and discrepancies of these three methods. We report the shortcomings and the advantages of each method and its performance in the quantification of expansion rates. While the orthogonal diameter measurement has an ability of capturing a realistic diameter, it fluctuated. On the other hand, the inscribed sphere diameter method tends to underestimate the diameter measurement but the growth rate can be bounded in a narrow region for aiding prediction capability. Moreover, expansion rate parameters derived from this measurement exhibit good correlation with each other and with growth rate of volume. In conclusion, although the orthogonal method remains the main method of measuring the diameter of an abdominal aorta, employing the idea of maximally inscribed spheres provides both a tool for generation of the centerline, and an additional parameter for quantification of aneurysmal growth rates.

New Insights Into Aortic Diseases: A Report From the Third International Meeting on Aortic Diseases (IMAD3)

The current state of research and treatment on aortic diseases was discussed in the "3rd International Meeting on Aortic Diseases" (IMAD3) held on October 4-6, 2012, in Liège, Belgium. The 3-day meeting covered a wide range of topics related to thoracic aortic aneurysms and dissections, abdominal aortic aneurysms, and valvular diseases. It brought together clinicians and basic scientists and provided an excellent opportunity to discuss future collaborative research projects for genetic, genomics, and biomarker studies, as well as clinical trials. Although great progress has been made in the past few years, there are still a large number of unsolved questions about aortic diseases. Obtaining answers to the key questions will require innovative, interdisciplinary approaches that integrate information from epidemiological, genetic, molecular biology, and bioengineering studies on humans and animal models. It is more evident than ever that multicenter collaborations are needed to accomplish these goals.