Volume growth of abdominal aortic aneurysms correlates with baseline volume and increasing finite element analysis-derived rupture risk

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model

BACKGROUND

Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth.

METHODS

Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study.

RESULTS

Of 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, -6.82 to 35.1) and zone 5 (13.4%; IQR, -7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91).

CONCLUSIONS

We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

Multicentric clinical evaluation of a computed tomography-based fully automated deep neural network for aortic maximum diameter and volumetric measurements

OBJECTIVE

This study aims to evaluate a fully automatic deep learning-based method (augmented radiology for vascular aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements.

METHODS

A clinical validation dataset was constructed from preoperative and postoperative aortic computed tomography angiography (CTA) scans for assessing these functions. The dataset totaled 350 computed tomography angiography scans from 216 patients treated at two different hospitals. ARVA's ability to segment the aorta into seven morphologically based aortic segments and measure maximum outer-to-outer wall transverse diameters and compute volumes for each was compared with the measurements of six experts (ground truth) and thirteen clinicians.

RESULTS

Ground truth (experts') measurements of diameters and volumes were manually performed for all aortic segments. The median absolute diameter difference between ground truth and ARVA was 1.6 mm (95% confidence interval [CI], 1.5-1.7; and 1.6 mm [95% CI, 1.6-1.7]) between ground truth and clinicians. ARVA produced measurements within the clinical acceptable range with a proportion of 85.5% (95% CI, 83.5-86.3) compared with the clinicians' 86.0% (95% CI, 83.9-86.0). The median volume similarity error ranged from 0.93 to 0.95 in the main trunk and achieved 0.88 in the iliac arteries.

CONCLUSIONS

This study demonstrates the reliability of a fully automated artificial intelligence-driven solution capable of quick aortic segmentation and analysis of both diameter and volume for each segment.

The Value of Aortic Volume and Intraluminal Thrombus Quantification for Predicting Aortic Events after Endovascular Thoracic Aneurysm Repair

Objectives: To assess the ability of the aortic aneurysm volume (AAV), aneurysmal lumen volume (ALV), and aneurysmal thrombus volume (ATV) to predict the need for aortic reintervention when using the maximal aortic diameter as a reference. Methods: This monocentric retrospective study included 31 consecutive patients who underwent successful thoracic endovascular aortic repair (TEVAR) to treat an atheromatous thoracic aortic aneurysm. All patients underwent clinical and computed tomography angiography (CTA) for 3 years after TEVAR. The patients were categorized into group 0 if no aortic reintervention was required during the follow-up period and categorized into group 1 if they experienced a type I or III endoleak or aneurysm diameter increase requiring intervention. The maximum aneurysm sac diameter and the AAV, ALV, and ATV were calculated using CTA images obtained preoperatively (T0) and at 6-12 months (T1), 24 months (T2), and 36 months (T3) postoperatively, and their changes over time were analyzed. Correlations between diameter and changes in AAV, ALV, and ATV were assessed, and the association between diameter and volume changes and reintervetion was examined. The cutoff values for predicting the need for reintervention was determined using a receiver operating characteristic (ROC) curve. The accuracy of volume change versus diameter change for predicting the need for reintervention was analyzed. Results: There were no significant differences in terms of the mean aneurysm diameter or AAV, ALV or ATV between the groups at preoperative CTA or after one year of follow-up imaging. The mean ATV was higher in group 1 than in group 0 at 2 years (187.6 ± 86.3 mL vs. 114.7 ± 64.7 mL; p = 0.057) and after 3 years (195.0 ± 86.7 mL vs. 82.1 ± 39.9 mL; p = 0.013). The maximal diameter was greater in group 1 than in group 0 at 3 years (67.3 ± 9.5 mm vs. 55.3 ± 12.6 mm; p = 0.044). The rate of AAV change between T0 and T1 was significantly higher in group 1 (7 ± 4.5%) than in group 0 (-6 ± 6.8%; p < 0.001). The rate of ATV change between T1-T3 was significantly higher in group 1 than in group 0 (34 ± 40.9% vs. -13 ± 14.4% (p = 0.041)); similar results were observed for the rate of ATV change between T2 and T3 (27 ± 50.1% for group 1 vs. -8 ± 49.5% in group 0 (p < 0.001)). According to our multivariate analysis, the annual growth rate for AAV between T0 and T1 was the only independent factor that was significantly associated with aortic reintervention (area under the curve (AUC) = 0.84, OR = 1.57, p = 0.025; optimal cutoff +0.4%). An increase in the annual growth rate of the ATV between T0 and T3 was independently associated with the need for aortic reintervention (area under the curve (AUC) = 0.90, OR = 1.11, p = 0.0347; optimal cutoff +10.1%). Conclusions: Aortic volume analysis can predict the need for aortic reintervention more accurately and earlier than maximal aortic diameter.

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.

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.

Chemokine Receptor 2 Is A Theranostic Biomarker for Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a degenerative vascular disease impacting aging populations with a high mortality upon rupture. There are no effective medical therapies to prevent AAA expansion and rupture. We previously demonstrated the role of the monocyte chemoattractant protein-1 (MCP-1) / C-C chemokine receptor type 2 (CCR2) axis in rodent AAA pathogenesis via positron emission tomography/computed tomography (PET/CT) using CCR2 targeted radiotracer 64 Cu-DOTA-ECL1i. We have since translated this radiotracer into patients with AAA. CCR2 PET showed intense radiotracer uptake along the AAA wall in patients while little signal was observed in healthy volunteers. AAA tissues collected from individuals scanned with 64 Cu-DOTA-ECL1i and underwent open-repair later demonstrated more abundant CCR2+ cells compared to non-diseased aortas. We then used a CCR2 inhibitor (CCR2i) as targeted therapy in our established male and female rat AAA rupture models. We observed that CCR2i completely prevented AAA rupture in male rats and significantly decreased rupture rate in female AAA rats. PET/CT revealed substantial reduction of 64 Cu-DOTA-ECL1i uptake following CCR2i treatment in both rat models. Characterization of AAA tissues demonstrated decreased expression of CCR2+ cells and improved histopathological features. Taken together, our results indicate the potential of CCR2 as a theranostic biomarker for AAA management.

3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth

The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.

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.

Ascending Aortic Volume: A Feasible Indicator for Ascending Aortic Aneurysm Elective Surgery?

Diameter-based criterion have been widely adopted for preventive surgery of ascending thoracic aortic aneurysm (ATAA). However, recent and growing evidence has shown that diameter-based methods may not be sufficient for identifying patients who are at risk of an ATAA. In this study, fluid-structure interaction (FSI) analysis was performed on one-hundred ATAA geometries reconstructed from clinical data to examine the relationship between hemodynamic conditions, ascending aortic volume (AAV), ascending aortic curvature, and aortic ratios measured from the reconstructed 3D models. The simulated hemodynamic and biomechanical parameters were compared among different groups of ATAA geometries classified based on AAV. The ATAAs with enlarged AAV showed significantly compromised hemodynamic conditions and higher mechanical wall stress. The maximum oscillatory shear index (OSI), particle residence time (PRT) and wall stress (WS) were significantly higher in enlarged ATAAs compared with controls (0.498 [0.497, 0.499] vs 0.499 [0.498, 0.499], p = 0.002, 312.847 [207.445, 519.391] vs 996.047 [640.644, 1573.140], p < 0.001, 769.680 [668.745, 879.795] vs 1072.000 [873.060, 1280.000] kPa, p < 0.001, respectively). Values were reported as median with interquartile range (IQR). AAV was also found to be more strongly correlated with these parameters compared to maximum diameter. The correlation coefficient between AAV and average WS was as high as 0.92 (p < 0.004), suggesting that AAV might be a feasible risk identifier for ATAAs. STATEMENT OF SIGNIFICANCE: Ascending thoracic aortic aneurysm is associated with the risk of dissection or rupture, creating life-threatening conditions. Current surgical intervention guidelines are purely diameter based. Recently, many studies proposed to incorporate other morphological parameters into the current clinical guidelines to better prevent severe adverse aortic events like rupture or dissection. The purpose of this study is to gain a better understanding of the relationship between morphological parameters and hemodynamic parameters in ascending aortic aneurysms using fluid-solid-interaction analysis on patient-specific geometries. Our results suggest that ascending aortic volume may be a better indicator for surgical intervention as it shows a stronger association with pathogenic hemodynamic conditions.

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.

Abdominal aortic aneurysm volume and relative intraluminal thrombus volume might be auxiliary predictors of rupture-an observational cross-sectional study

Objectives

The study aimed to identify differences and compare anatomical and biomechanical features between elective and ruptured abdominal aortic aneurysms (AAAs).

Methods

Data (clinical, anatomical, and biomechanical) of 98 patients with AAA, 75 (76.53%) asymptomatic (Group aAAA) and 23 (23.46%) ruptured AAA (Group rAAA), were prospectively collected and analyzed. Anatomical, morphological, and biomechanical imaging markers like peak wall stress (PWS) and rupture risk equivalent diameter (RRED), comorbid conditions, and demographics were compared between the groups. Biomechanical features were assessed by analysis of Digital Imaging and Communication in Medicine images by A4clinics (Vascops), and anatomical features were assessed by 3Surgery (Trimensio). Binary and multiple logistic regression analysis were used and adjusted for confounders. Accuracy was assessed using receiving operative characteristic (ROC) curve analysis.

Results

In a multivariable model, including gender and age as confounder variables, maximal aneurysm diameter [MAD, odds ratio (OR) = 1.063], relative intraluminal thrombus (rILT, OR = 1.039), and total aneurysm volume (TAV, OR = 1.006) continued to be significant predictors of AAA rupture with PWS (OR = 1.010) and RRED (OR = 1.031). Area under the ROC curve values and correct classification (cc) for the same parameters and the model that combines MAD, TAV, and rILT were measured: MAD (0.790, cc = 75%), PWS (0.713, cc = 73%), RRED (0.717, cc = 55%), TAV (0.756, cc = 79%), rILT (0.656, cc = 60%), and MAD + TAV + rILT (0.797, cc = 82%).

Conclusion

Based on our results, in addition to MAD, other important predictors of rupture that might be used during aneurysm surveillance are TAV and rILT. Biomechanical parameters (PWS, RRED) as valuable predictors should be assessed in prospective clinical trials. Similar studies on AAA smaller than 55 mm in diameter, even difficult to organize, would be of even greater clinical value.

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.

Abdominal Aortic Aneurysm Diameter versus Volume: A Systematic Review

Recently, AAA volume measurement has been proposed as a potentially valuable surveillance method in situations when diameter measurement might fail.

OBJECTIVE

The aim of this systematic review was to analyze the results of previous studies comparing AAA diameter and volume measurements.

METHODS

A systematic search in PubMed, Cochrane, and EMBASE databases was performed to identify studies investigating the use of diameter and volume measurements in AAA diagnosis and prognosis in English, German, and Russian, published until December 2022. The manuscripts were reviewed by three researchers and scored on the quality of the research using MINORS criteria.

RESULTS

After screening 752 manuscripts, 19 studies (n = 1690) were included. The majority (n = 17) of the manuscripts appeared to favor volume. It is, however, important to highlight the heterogeneity of methodologies and lack of standardized protocol for measuring both volume and diameter in the included studies, which hindered the interpretation of the results.

CONCLUSIONS

The clinical relevance of abdominal aortic aneurysm volume measurement is still unclear, although studies show favorable and promising results for volumetric changes in AAA, especially in follow-up after EVAR.

A Comprehensive Research Schema for the Characterization of Aortic Aneurysms

A robust, accurate, and standardized approach to measurement of the aorta is critical to improve the predictive accuracy of these aortic measurements, and to investigate other aortic imaging biomarkers. Developing a comprehensive and generic schema for characterization of the aorta to enable investigators to standardize data that are collected across all aorta research. A systematic review of the literature was conducted to identify and assess schemata of aortic measurement and description. The schemata were reported and discussed to guide the synthesis of a comprehensive schema. We propose the International College of Angiology Aortic Research Schema as a comprehensive design that fills the gaps left behind by previously reported schemata. It is intended to be applicable for all clinically relevant purposes, including endograft development for aneurysm repair and for the accurate characterization of the aortic anatomy. This schema divides the aorta into 14 segments and 2 sections (thoracic and abdominal aortas). The segmentation proposed can be used in addition to specific measurements taken for any aneurysm including the neck, and maximal and minimal diameters of the aneurysm.

One-year volume growth of abdominal aortic aneurysms measured by extended field-of-view ultrasound

BACKGROUND

Measurement of volume has the potential to detect subtle growth not recognized in the current surveillance paradigm of abdominal aortic aneurysms (AAAs). Currently available three-dimensional ultrasound allows for estimation of AAA volume, but for most patients, the AAA extends beyond the ultrasound field-of-view and only allows visualization of a partial AAA volume. A new extended field-of-view three-dimensional ultrasound protocol (XFoV US) has been found to improve the proportion of patients with visualization of the full AAA volume.

METHODS

To investigate the applicability of the XFoV US protocol in estimating AAA volume growth in follow-up, 86 patients with AAAs were recruited from the surveillance program at a university hospital. All were imaged by XFoV US at baseline and at one-year follow-up.

RESULTS

Assessment of full volume, based on visualization of the AAA neck and bifurcation at both baseline and one-year follow-up, was achieved in 67/86 (78%) of patients. One-year mean growth in maximum diameter was 2.8 mm (6%/year), in centerline length 2.9 mm (4%/year), and in volume 15.9 mL (19%/year). In 17/67 (25%) of patients, volume growth was detected in diameter-stable AAAs. Baseline XFoV US volume was associated with one-year AAA volume growth, while, conversely, maximum baseline diameter was not associated with one-year AAA diameter growth.

CONCLUSIONS

This study concludes that the XFoV US protocol provides a safe and repeatable modality for assessing AAA volume growth, and that AAA volume is a promising predictive measure of AAA growth.

Abdominal aortic aneurysm growth rates are not correlated to body surface area in screened men

BACKGROUND

Screening for abdominal aortic aneurysm (AAA) in 65-year-old males reduces aneurysm related mortality. Infrarenal aortic diameter (IAD) has been shown to correlate to body surface area (BSA) which could influence diagnostic criteria for AAA. This study investigates whether AAA growth rates are also dependent on BSA, as that might have potential effects on surveillance of small AAAs.

METHODS

We conducted a retrospective, single center cohort study of 301 men with screening detected AAA between 2010-2017 with surveillance to 2021. AAA growth rates were analyzed in relation to the subject's BSA, smoking habits, and diabetic disease using a linear mixed-effects model. All men were offered smoking cessation program, optimized medical treatment, and advice on physical activity.

RESULTS

The screening program included 28,784 men. Of the 22,819 (79%) attending the examinations, 374 men (1.6%) were found to have an AAA out of which 301 men had undergone two or more examinations during surveillance and were included with a median follow-up of 1846 days (IQR: 1 399). Mean unadjusted AAA growth rate was 1.60 mm/year (95% CI: 1.41-1.80). Diabetes mellitus had a statistically significant negative impact, smoking had a statistically significant positive impact on AAA growth rates whereas no correlation between AAA growth rate and BSA could be found.

CONCLUSIONS

Body surface area could not be found to have a statistically significant correlation to AAA growth rates. The impact of smoking and diabetes on AAA growth rates remains similar to previously reported.

Rupture risk parameters upon biomechanical analysis independently change from vessel geometry during abdominal aortic aneurysm growth

Objective

The indication for abdominal aortic aneurysm (AAA) repair is based on a diameter threshold. However, mechanical properties, such as peak wall stress (PWS) and peak wall rupture index (PWRI), influence the individual rupture risk. This study aims to correlate biomechanical and geometrical AAA characteristics during aneurysm growth applying a new linear transformation-based comparison of sequential imaging.

Methods

Patients with AAA with two sequential computed tomography angiographies (CTA) were identified from a single-center aortic database. Patient characteristics included age, gender, and comorbidities. Semiautomated segmentation of CTAs was performed using Endosize (Therenva) for geometric variables (diameter, neck configuration, α/β angle, and vessel tortuosity) and for finite element method A4 Clinics Research Edition (Vascops) for additional variables (intraluminal thrombus [ILT]), vessel volume, PWS, PWRI). Maximum point coordinates from at least one CTA 6 to 24 months before their final were predicted for the final preoperative CTA using linear transformation along fix and validation points to estimate spatial motion. Pearson's correlation and the t test were used for comparison.

Results

Thirty-two eligible patients (median age, 70 years) were included. The annual AAA growth rate was 3.7 mm (interquartile range [IQR], 2.25-5.44; P < .001) between CTs. AAA (+17%; P < .001) and ILT (+43%; P < .001) volume, maximum ILT thickness (+35%; P < .001), β angle (+1.96°; P = .017) and iliac tortuosity (+0.009; P = .012) increased significantly. PWS (+12%; P = .0029) and PWRI (+16%; P < .001) differed significantly between both CTAs. Both mechanical parameters correlated most significantly with the AAA volume increase (r = 0.68 [P < .001] and r = 0.6 [P < .001]). Changes in PWS correlated best with the aneurysm neck configuration. The spatial motion of maximum ILT thickness was 14.4 mm (IQR, 7.3-37.2), for PWS 8.4 mm (IQR, 3.8-17.3), and 11.5 mm (IQR, 5.9-31.9) for PWRI. Here, no significant correlation with any of the aforementioned parameters, patient age, or time interval between CTs were observed.

Conclusions

PWS correlates highly significant with vessel volume and aneurysm neck configuration. Spatial motion of maximum ILT thickness, PWS, and PWRI is detectable and predictable and might expose different aneurysm wall segments to maximum stress throughout aneurysm growth. Linear transformation could thus add to patient-specific rupture risk analysis.

Clinical Relevance

Abdominal aortic aneurysm rupture risk assessment is a key feature in future individualized therapy approaches for patients, since more and more data are obtained concluding a heterogeneous disease entity that might not be addressed ideally looking only at diameter enlargement. The approach presented in this pilot study demonstrates the feasibility and importance of measuring peak wall stress and rupture risk indices based on predicted and actual position of maximum stress points including intraluminal thrombus.

Advanced ultrasound techniques in arterial diseases

Ultrasound (US) remains a valuable modality for the assessment of vascular diseases, with conventional sonographic techniques such as grayscale and Doppler US used extensively to assess carotid atherosclerosis and abdominal aortic aneurysms. However, conventional US techniques are inherently limited by factors such as operator dependency and limited field of view. There is an increasing interest in the use of advanced sonographic techniques such as contrast-enhanced US (CEUS) and 3-dimensional (3D) US to mitigate some of these limitations. Clinical applications of advanced sonographic techniques include surveillance of abdominal aortic aneurysm, post-endovascular aortic repair, and carotid atherosclerotic plaques. Recently published studies have demonstrated that CEUS and 3D US are superior to conventional US and comparable to computed tomography for certain vascular applications. Further research is required to fully validate the application of advanced sonographic techniques in evaluating various atherosclerotic diseases.

Molecular Imaging of Aortic Aneurysm and Its Translational Power for Clinical Risk Assessment

Aortic aneurysms (AAs) are dilations of the aorta, that are often fatal upon rupture. Diagnostic radiological techniques such as ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT) are currently used in clinical practice for early diagnosis as well as clinical follow-up for preemptive surgery of AA and prevention of rupture. However, the contemporary imaging-based risk prediction of aneurysm enlargement or life-threatening aneurysm-rupture remains limited as these are restricted to visual parameters which fail to provide a personalized risk assessment. Therefore, new insights into early diagnostic approaches to detect AA and therefore to prevent aneurysm-rupture are crucial. Multiple new techniques are developed to obtain a more accurate understanding of the biological processes and pathological alterations at a (micro)structural and molecular level of aortic degeneration. Advanced anatomical imaging combined with molecular imaging, such as molecular MRI, or positron emission tomography (PET)/CT provides novel diagnostic approaches for in vivo visualization of targeted biomarkers. This will aid in the understanding of aortic aneurysm disease pathogenesis and insight into the pathways involved, and will thus facilitate early diagnostic analysis of aneurysmal disease. In this study, we reviewed these molecular imaging modalities and their association with aneurysm growth and/or rupture risk and their limitations. Furthermore, we outline recent pre-clinical and clinical developments in molecular imaging of AA and provide future perspectives based on the advancements made within the field. Within the vastness of pre-clinical markers that have been studied in mice, molecular imaging targets such as elastin/collagen, albumin, matrix metalloproteinases and immune cells demonstrate promising results regarding rupture risk assessment within the pre-clinical setting. Subsequently, these markers hold potential as a future diagnosticum of clinical AA assessment. However currently, clinical translation of molecular imaging is still at the onset. Future human trials are required to assess the effectivity of potentially viable molecular markers with various imaging modalities for clinical rupture risk assessment.

Application Effect of Intra-Abdominal Pressure Monitoring System in Early Enteral Nutrition Nursing of ICU Patients

In order to study and explore the application effect of intra-abdominal pressure monitoring in guiding ICU patients to implement early enteral nutrition therapy, we selected patients admitted to the emergency department of a hospital and classified them into groups A (n = 105) and B (n = 98). Among them, the A group gave early enteral nutrition treatment by monitoring the residual gastric mass, and the B group gave early enteral nutrition treatment by monitoring the stomach. It has been established that, compared to gastric residue monitoring, intra-abdominal pressure monitoring in ICU patients with early enteral nutrition therapy has an obvious advantage, it helps to improve the prognosis of patients, and intra-abdominal pressure combined with gastric residual monitoring scheme can effectively reduce the early enteral nutrition in the ICU patients, the incidence of abdominal distension, vomiting, and make it reach the goal as soon as possible. Early enteral nutrition in patients with increased tolerance is of great significance.

Profiling abdominal aortic aneurysm growth with three-dimensional ultrasound

BACKGROUND

"Profiling" is a new method based on three-dimensional ultrasound (3D-US) allowing for direct comparison of baseline and follow-up diameters along the AAA length. This study aimed to evaluate the feasibility of profiling to visualize AAA changes at sub-maximum diameters, and to categorize the growth profiles.

METHODS

Retrospective analysis of prospectively and consecutively included patients under AAA surveillance at a tertiary referral centre. 3D-US images of AAAs at baseline and at one-year follow-up were segmented, generating a centerline and a mesh of the aneurysm geometry. The mesh was processed to illustrate diameter changes of a given AAA. Three growth profiles were identified: A) Peak Growth: the largest, significant (≥3.6 mm) diameter difference occurred within a 10 mm margin to either side of the maximum baseline diameter; B) Edge Growth: at least one significant diameter difference and the criteria for Peak Growth did not apply; C) No Growth: all diameter differences were nonsignificant. A centerline length of ≥60 mm was assumed to capture a comparable segment of the wall geometry at baseline and follow-up. Cohen's kappa and Kaplan Meier analysis were used to analyze data.

RESULTS

In total, 186 patients had growth profiles generated. Of these, 28 (15%) were discarded, mainly based on inadequate centerline lengths (n= 21, 11.3%). The remaining patients were categorized into Edge Growth (n=83, 52%), No Growth (n=47, 30%), and Peak Growth (n=28, 18%).

CONCLUSIONS

Profiling interprets AAA growth at sub-maximum diameters. Half of the cohort had Edge Growth. These AAAs risk being classified as stable.

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.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

Rupture risk assessment of abdominal aortic aneurysms (AAAs) by means of quantifying wall stress is a common biomechanical strategy. However, the clinical translation of this approach has been greatly limited due to the complexity associated with the computational tools required for its implementation. Thus, being able to estimate wall stress using nonbiomechanical markers that can be quantified as a direct outcome of clinical image segmentation would be advantageous in improving the potential implementation of said strategy. In the present work, we investigated the use of geometric indices to predict patient-specific AAA wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAAs. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. SAWS estimated from finite element analysis was considered the gold standard for the predictions. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). With the reduced sets of indices, the NN-based approach exhibited the highest mean goodness-of-fit (for the symptomatic group 0.71 and for the asymptomatic group 0.79) and lowest mean relative error (17% for both groups). In contrast, MARS yielded a mean goodness-of-fit of 0.59 for the symptomatic group and 0.77 for the asymptomatic group, with relative errors of 17% for the symptomatic group and 22% for the asymptomatic group. GAM had a mean goodness-of-fit of 0.70 for the symptomatic group and 0.80 for the asymptomatic group, with relative errors of 16% for the symptomatic group and 20% for the asymptomatic group. GLM did not perform as well as the other algorithms, with a mean goodness-of-fit of 0.53 for the symptomatic group and 0.70 for the asymptomatic group, with relative errors of 19% for the symptomatic group and 23% for the asymptomatic group. Nevertheless, the NN models required a reduced set of 15 and 13 geometric indices to predict SAWS for the symptomatic and asymptomatic AAA groups, respectively. This was in contrast to the reduced set of nine and eight geometric indices required to predict SAWS with the MARS and GAM algorithms for each AAA group, respectively. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling. The performance metrics of NN models are expected to improve with significantly larger group sizes, given the suitability of NN modeling for "big data" applications.

Efficient parallel simulation of hemodynamics in patient-specific abdominal aorta with aneurysm

Surgical planning for aortic aneurysm repair is a difficult task. In addition to the morphological features obtained from medical imaging, alternative features obtained with computational modeling may provide additional useful information. Though numerical studies are noninvasive, they are often time-consuming, especially when we need to study and compare multiple repair scenarios, because of the high computational complexity. In this paper, we present a highly parallel algorithm for the numerical simulation of unsteady blood flows in the patient-specific abdominal aorta before and after the aneurysmic repair. We model the blood flow with the unsteady incompressible Navier-Stokes equations with different outlet boundary conditions, and solve the discretized system with a highly scalable domain decomposition method. With this approach, a high resolution simulation of a full-size adult aorta can be obtained in less than an hour, instead of days with older methods and software. In addition, we show that the parallel efficiency of the proposed method is near 70% on a parallel computer with 2, 880 processor cores.

A Volumetric Metric for Monitoring Intracranial Aneurysms: Repeatability and Growth Criteria in a Longitudinal MR Imaging Study

BACKGROUND AND PURPOSE

The reliability of contrast-enhanced MRA in monitoring serial volumetric changes of unruptured intracranial aneurysms has not been established. We aimed to determine the coefficient of variance of contrast-enhanced MRA in measuring aneurysm volumes, thus establishing criteria for aneurysm growth and permitting identification of variables predictive of growth.

MATERIALS AND METHODS

Aneurysm volumes were measured from serial contrast-enhanced MRA studies of patients with untreated intracranial aneurysms who underwent >2 sequential MR imaging evaluations. After coregistering all sequential studies in 3D space for each aneurysm and signal intensity normalization, aneurysm volume was determined across all time points. A linear mixed effects model was built to estimate the coefficient of variance of the measurement as well as to determine predictive variables. Growth was defined as relative growth exceeding 2 times the measurement coefficient of variance (sudden growth, as 4 times the coefficient of variance).

RESULTS

A total of 95 patients with 112 aneurysms were included (5.9 scans during 4.0 years on average, 616 scan measurements in total). The coefficient of variance was 5.5% of the aneurysm volume, and the relative growth rate was dependent on the location: anterior cerebral artery, 4.52% per year; vertebral artery, 2.46% per year; middle cerebral artery, 2.74% per year; basilar artery, 2.36% per year; internal carotid artery, 1.14% per year. Thirty-six of 112 (32%) aneurysms were characterized as growing, and 11/36 of them had an episode of sudden growth.

CONCLUSIONS

Volume measurement of unruptured intracranial aneurysms by contrast-enhanced MRA seems a reliable metric for tracking the growth trajectory of aneurysms. Furthermore, the aneurysm growth rate differs among different locations.

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.

Imaging Predictive Factors of Abdominal Aortic Aneurysm Growth

Background: Variable imaging methods may add important information about abdominal aortic aneurysm (AAA) progression. The aim of this study is to assess available literature data regarding the predictive imaging factors of AAA growth. Methods: This systematic review was conducted using the PRISMA guidelines. A review of the literature was conducted, using PubMed, EMBASE and CENTRAL databases. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Primary outcomes were defined as AAA growth rate and factors associated to sac expansion. Results: The analysis included 23 studies. All patients (2244; mean age; 69.8 years, males; 85%) underwent imaging with different modalities; the initial evaluation was followed by one or more studies to assess aortic expansion. AAA initial diameter was reported in 13 studies (range 19.9–50.9 mm). Mean follow-up was 34.5 months. AAA diameter at the end was ranging between 20.3 and 55 mm. The initial diameter and intraluminal thrombus were characterized as prognostic factors associated to aneurysm expansion. A negative association between atherosclerosis and AAA expansion was documented. Conclusions: Aneurysm diameter is the most studied factor to be associated with expansion and the main indication for intervention. Appropriate diagnostic modalities may account for different anatomical characteristics and identify aneurysms with rapid growth and higher rupture risk. Future perspectives, including computed mathematical models that will assess wall stress and elasticity and further flow characteristics, may offer valuable alternatives in AAA growth prediction.

Methodology for Estimation of Annual Risk of Rupture for Abdominal Aortic Aneurysm

BACKGROUND AND OBJECTIVE

Estimating patient specific annual risk of rupture of abdominal aortic aneurysm (AAA) is currently based only on population. More accurate knowledge based on patient specific data would allow surgical treatment of only those AAAs with significant risk of rupture. This would be beneficial for both patients and health care system.

METHODS

A methodology for estimating annual risk of rupture (EARR) of abdominal aortic aneurysms (AAA) that utilizes Bayesian statistics, mechanics and patient-specific blood pressure monitoring data is proposed. EARR estimation takes into consideration, peak wall stress in AAA computed by patient-specific finite element modeling, the probability distributions of wall thickness, wall strength, systolic blood pressure and the period of time that the patient is known to have already survived with the intact AAA. Initial testing of proposed approach was performed on fifteen patients with intact AAA (mean maximal diameter 51mm±8mm). They were equipped with a pressure holter and their blood pressure was recorded over 24 hours. Then, we calculated EARR values for four possible scenarios - without considering any days of survival prior identification of AAA at computed tomography scans (EARR_0), considering past survival of 30 (EARR_30), 90 (EARR_90) and 180 days (EARR_180). Finally, effect of patient-specific blood pressure variability was analyzed.

RESULTS

Consideration of past survival does indeed significantly improve predictions of future risk: EARR_30 (1.04%± 0.87%), EARR_90 (0.67%± 0.56%) and EARR_180 (0.47%± 0.39%) which are unrealistically high otherwise (EARR_0 5.02%± 5.24%). Finally, EARR values were observed to vary by an order as a consequence of blood pressure variability and by factor of two as a consequence of neglected growth.

CONCLUSIONS

Methodology for computing annual risk of rupture of AAA was developed for the first time. Sensitivity analyses showed respecting patient specific blood pressure is important factor and should be included in the AAA rupture risk assessment. Obtained EARR values were generally low and in good agreement with confirmed survival time of investigated patients so proposed method should be further clinically validated.

Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation

OBJECTIVE

Imaging software has become critical tools in the diagnosis and decision making for the treatment of abdominal aortic aneurysms (AAA). However, the interobserver reproducibility of the maximum cross-section diameter is poor. This study aimed to present and assess the quality of a new fully automated software (PRAEVAorta) that enables fast and robust detection of the aortic lumen and the infrarenal AAA characteristics including the presence of thrombus.

METHODS

To evaluate the segmentation obtained with this new software, we performed a quantitative comparison with the results obtained from a semiautomatic segmentation manually corrected by a senior and a junior surgeon on a dataset of 100 preoperative computed tomography angiographies from patients with infrarenal AAAs (13,465 slices). The Dice similarity coefficient (DSC), Jaccard index, sensitivity, specificity, volumetric similarity (VS), Hausdorff distance, maximum aortic transverse diameter, and the duration of segmentation were calculated between the two methods and, for the semiautomatic software, also between the two observers.

RESULTS

The analyses demonstrated an excellent correlation of the volumes, surfaces, and diameters measured with the fully automatic and manually corrected segmentation methods, with a Pearson's coefficient correlation of greater than 0.90 (P < .0001). Overall, a comparison between the fully automatic and manually corrected segmentation method by the senior surgeon revealed a mean Dice similarity coefficient of 0.95 ± 0.01, a Jaccard index of 0.91 ± 0.02, sensitivity of 0.94 ± 0.02, specificity of 0.97 ± 0.01, VS of 0.98 ± 0.01, and mean Hausdorff distance per slice of 4.61 ± 7.26 mm. The mean VS reached 0.95 ± 0.04 for the lumen and 0.91 ± 0.07 for the thrombus. For the fully automatic method, the segmentation time varied from 27 seconds to 4 minutes per patient vs 5 minutes to 80 minutes for the manually corrected methods (P < .0001).

CONCLUSIONS

By enabling a fast and fully automated detailed analysis of the anatomic characteristics of infrarenal AAAs, this software could have strong applications in daily clinical practice and clinical research.

Full-Volume Assessment of Abdominal Aortic Aneurysms by 3-D Ultrasound and Magnetic Tracking

Volume assessment of abdominal aortic aneurysms (AAAs) using 3-D ultrasound (US) is an innovative technique reporting good agreement with computed tomography angiography. One major limitation of the current 3-D US technique is a limited field of view, allowing full AAA acquisition in only 60% of patients. This study presents two new US acquisition protocols using magnetic field tracking, providing an "extended field of view" (XFoV-2-D and XFoV-3-D) with the aim of including both the aortic bifurcation and neck for full-volume assessment, and compares these methods with the current standard 3-D US protocol and with computed tomography angiography. A total of 20 AAA patients were included and underwent the current standard 3-D US protocol and the two novel 3-D US "extended field of view" protocols. Four patients were excluded from further analysis because of low image quality, leaving 16 patients eligible for analysis. Full AAA volume was achieved in 8 patients (50%) using the standard 3-D US protocol, in 11 patients (69%) with the XFoV-2-D protocol and in 13 patients (81%) with the XFoV-3-D protocol. In conclusion, this article describes two new and feasible US protocols applicable for full-AAA-volume estimation in most patients and should initiate further research into the added value of full volume in AAA surveillance.

Aneurysm Growth After Endovascular Sealing of Abdominal Aortic Aneurysms (EVAS) with the Nellix Endoprosthesis

OBJECTIVE

The aim of this study was to measure the incidence of post endovascular aneurysm sealing (EVAS) abdominal aortic aneurysm (AAA) growth, and its association with stent migration, in a cohort of patients with differing compliance to old and new Instructions For Use (IFU).

METHODS

A retrospective single centre study was conducted to review the computed tomography (CT) and clinical data of elective, infrarenal EVAS cases, performed as a primary intervention, between December 2013 and March 2018. All included patients had a baseline post-operative CT scan at one month and at least one year follow up. The primary outcome measure was the incidence of AAA growth and its association with stent migration. AAA growth was defined as a ≥5% increase in aortic volume between the lowermost renal artery and the aortic bifurcation post EVAS at any time during follow up, in comparison to the baseline CT scan. Migration was defined according to the ESVS guidelines, as > 10 mm downward movement of either Nellix stent frame in the proximal zone.

RESULTS

Seventy-six patients were eligible for inclusion in the study (mean age 76 ± 7.4 years; 58 men). AAA growth was identified in 50 of 76 patients (66%); adherence to IFU did not affect its incidence (mean growth within IFU-2016 compliant cohort vs. non-compliant: 16% vs. 13%, p = .33). Over time, the incidence of AAA growth increased, from 32% at one year to 100% at four years. AAA growth by volume was progressive (p < .001), as its extent increased over time. Migration was detected in 16 patients and there was a statistically significant association with AAA growth (13 patients displayed migration and AAA growth, p = .036).

CONCLUSION

Patients treated with EVAS are prone to AAA growth, irrespective of whether their aortic anatomy is IFU compliant. AAA growth by volume is associated with stent migration. Clinicians should continue close surveillance post EVAS, regardless of whether patients are treated within IFU.

CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression

The aim of this study is to investigate the role of early postoperative CT texture analysis in aneurysm progression. Ninety-nine patients who had undergone post-endovascular aneurysm repair (EVAR) infra-renal abdominal aortic aneurysm CT serial scans were enrolled from July 2014 to December 2019. The clinical and traditional imaging features were obtained. Aneurysm texture analysis was performed using three methods—the grey-level co-occurrence matrix (GLCM), the grey-level run length matrix (GLRLM), and the grey-level difference method (GLDM). A multilayer perceptron neural network was applied as a classifier, and receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) analysis were employed to illustrate the classification performance. No difference was found in the morphological and clinical features between the expansion (+) and (−) groups. GLCM yielded the best performance with an accuracy of 85.17% and an AUC of 0.90, followed by GLRLM with an accuracy of 87.23% and an AUC of 0.8615, and GLDM with an accuracy of 86.09% and an AUC of 0.8313. All three texture analyses showed superior predictive ability over clinical risk factors (accuracy: 69.41%; AUC: 0.6649), conventional imaging features (accuracy: 69.02%; AUC: 0.6747), and combined (accuracy: 75.29%; AUC: 0.7249). Early post-EVAR arterial phase-derived aneurysm texture analysis is a better predictor of later aneurysm expansion than clinical factors and traditional imaging evaluation combined.

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.

Long-Term Follow-Up of Men Invited to Participate in a Population-Based Abdominal Aortic Aneurysm Screening Program

A screening program for abdominal aortic aneurysm (AAA), inviting 65-year-old men, was started in Stockholm in 2010 (2.3 million inhabitants). The aim was to present a long-term follow-up of men participating in screening, as well as AAA repair and ruptures among nonparticipants. Demographics were collected for men with screening detected with AAA 2010 to 2016 (n = 672) and a control group with normal aortas at screening (controls, n = 237). Medical charts and regional Swedvasc (Swedish Vascular registry) data were analyzed for aortic repair for men born 1945 to 1951. Ultrasound maximum aortic diameter (AD) as well as Aortic Size Index (ASI) was recorded. Participation was 78% and prevalence of AAA was 1.2% (n = 672). Aortic repair rates correlated with high ASI and AD. During the study period, 22% of the AAA patients were treated with the elective repair; 35 men in surveillance died (5.2%), non-AAA-related causes (82.9%) dominated, followed by unknown causes among 4 (11.4%), and 2 (5.7%) possibly AAA-related deaths. Abdominal aortic aneurysm rupture rate was higher among nonparticipants (0.096% vs 0.0036%, P < .001). The low dropout rate confirms acceptability of follow-up after screening. The efficacy is shown by the much higher rupture rate among the nonparticipating men.

Combining Volumetric and Wall Shear Stress Analysis from CT to Assess Risk of Abdominal Aortic Aneurysm Progression

Background Despite known limitations, the decision to operate on abdominal aortic aneurysm (AAA) is primarily on the basis of measurement of maximal aneurysm diameter. Purpose To identify volumetric and computational fluid dynamics parameters to predict AAAs that are likely to progress in size. Materials and Methods This study, part of a multicenter prospective registry (NCT01599533), included 126 patients with AAA. Patients were sorted into stable (≤10-mL increase in aneurysm volume) and progression (>10-mL increase in aneurysm volume) groups. Initial AAA characteristics of the derivation cohort were analyzed (maximal diameter and surface, thrombus and lumen volumes, maximal wall pressure, and wall shear stress [WSS]) to identify relevant parameters for a logistic regression model. Model and maximal diameter diagnostic performances were assessed in both cohorts and for AAAs smaller than 50 mm by using area under the receiver operating characteristic curve (AUC). Results Eighty-one patients were included (mean age, 73 years ± 7 years [standard deviation]; 78 men). The derivation and validation cohorts included, respectively, 50 and 31 participants. In the derivation cohort, there was higher mean lumen volume and lower mean WSS in the progression group compared with the stable group (60 mL ± 14 vs 46 mL ± 18 [P = .005] and 66% ± 6 vs 53% ± 9 [P = .02], respectively). Mean lumen volume and mean WSS at baseline were correlated to total volume growth (r = 0.47 [P = .002] and -0.42 [P = .006], respectively). In the derivation cohort, a regression model including lumen volume and WSS to predict aneurysm enlargement was superior to maximal diameter alone (AUC, 0.78 vs 0.52, respectively; P = .003); although no difference was found in the validation cohort (AUC, 0.79 vs 0.71, respectively; P = .51). For AAAs smaller than 50 mm, a regression model that included both baseline WSS and lumen volume performed better than maximal diameter (AUC, 0.79 vs 0.53, respectively; P = .01). Conclusion Combined analysis of lumen volume and wall shear stress was associated with enlargement of abdominal aortic aneurysms at 1 year, particularly in aneurysms smaller than 50 mm in diameter. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Mitsouras and Leach in this issue.

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.

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.

A large proportion of patients with small ruptured abdominal aortic aneurysms are women and have chronic obstructive pulmonary disease

Objective

In a population-based cohort of ruptured abdominal aortic aneurysms (rAAAs), our aim was to investigate clinical, morphological and biomechanical features in patients with small rAAAs.

Methods

All patients admitted to an emergency department in Stockholm and Gotland, a region with a population of 2.1 million, between 2009–2013 with a CT-verified rupture (n = 192) were included, and morphological measurements were performed. Patients with small rAAAs, maximal diameter (Dmax) ≤ 60 mm were selected (n = 27), and matched 2:1 by Dmax, sex and age to intact AAA (iAAAs). For these patients, morphology including volume and finite element analysis-derived biomechanics were assessed.

Results

The mean Dmax for all rAAAs was 80.8 mm (SD = 18.9 mm), women had smaller Dmax at rupture (73.4 ± 18.4 mm vs 83.1 ± 18.5 mm, p = 0.003), and smaller neck and iliac diameters compared to men. Aortic size index (ASI) was similar between men and women (4.1 ± 3.1 cm/m² vs 3.8 ± 1.0 cm/m²). Fourteen percent of all patients ruptured at Dmax ≤ 60 mm, and a higher proportion of women compared to men ruptured at Dmax ≤ 60 mm: 27% (12/45) vs. 10% (15/147), p = 0.005. Also, a higher proportion of patients with a chronic obstructive pulmonary disease ruptured at Dmax ≤ 60 mm (34.6% vs 14.6%, p = 0.026). Supra-renal aortic size index (14.0, IQR 13.3–15.3 vs 12.8, IQR = 11.4–14.0) and peak wall rupture index (PWRI, 0.35 ± 0.08 vs 0.43 ± 0.11, p = 0.016) were higher for small rAAAs compared to matched iAAAs. Aortic size index, peak wall stress and aneurysm volume did not differ.

Conclusion

More than one tenth of ruptures occur at smaller diameters, women continuously suffer an even higher risk of presenting with smaller diameters, and this must be considered in surveillance programs. The increased supra-renal aortic size index and PWRI are potential markers for rupture risk, and patients under surveillance with these markers may benefit from increased attention, and potentially from timely repair.

Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.

Anisotropic stiffness and tensional homeostasis induce a natural anisotropy of volumetric growth and remodeling in soft biological tissues

Growth in soft biological tissues in general results in anisotropic changes of the tissue geometry. It remains a key challenge in biomechanics to understand, quantify, and predict this anisotropy. In this paper, we demonstrate that anisotropic tissue stiffness and the well-known mechanism of tensional homeostasis induce a natural anisotropy of the geometric changes resulting from volumetric growth in soft biological tissues. As a rule of thumb, this natural anisotropy makes differential tissue volume elements dilate mainly in the direction(s) of lowest stiffness. This simple principle is shown to explain the experimentally observed growth behavior in a host of different soft biological tissues without relying on any additional heuristic assumptions or quantities (such as ad hoc defined growth tensors).

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.

Abdominal aortic aneurysms

An abdominal aortic aneurysm (AAA) is a localized dilatation of the infrarenal aorta. AAA is a multifactorial disease, and genetic and environmental factors play a part; smoking, male sex and a positive family history are the most important risk factors, and AAA is most common in men >65 years of age. AAA results from changes in the aortic wall structure, including thinning of the media and adventitia due to the loss of vascular smooth muscle cells and degradation of the extracellular matrix. If the mechanical stress of the blood pressure acting on the wall exceeds the wall strength, the AAA ruptures, causing life-threatening intra-abdominal haemorrhage - the mortality for patients with ruptured AAA is 65-85%. Although AAAs of any size can rupture, the risk of rupture increases with diameter. Intact AAAs are typically asymptomatic, and in settings where screening programmes with ultrasonography are not implemented, most cases are diagnosed incidentally. Modern functional imaging techniques (PET, CT and MRI) may help to assess rupture risk. Elective repair of AAA with open surgery or endovascular aortic repair (EVAR) should be considered to prevent AAA rupture, although the morbidity and mortality associated with both techniques remain non-negligible.

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

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

Geometric surrogates of abdominal aortic aneurysm wall mechanics

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: D_max (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), K_median (median of the Gaussian surface curvature), C_max (maximum lumen compactness), and M_mode (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.