Clinical validation of a software for quantitative follow-up of abdominal aortic aneurysm maximal diameter and growth by CT angiography

Using averaged models from 4D ultrasound strain imaging allows to significantly differentiate local wall strains in calcified regions of abdominal aortic aneurysms

Abdominal aortic aneurysms are a degenerative disease of the aorta associated with high mortality. To date, in vivo information to characterize the individual elastic properties of the aneurysm wall in terms of rupture risk is lacking. We have used time-resolved 3D ultrasound strain imaging to calculate spatially resolved in-plane strain distributions characterized by mean and local maximum strains, as well as indices of local variations in strains. Likewise, we here present a method to generate averaged models from multiple segmentations. Strains were then calculated for single segmentations and averaged models. After registration with aneurysm geometries based on CT-A imaging, local strains were divided into two groups with and without calcifications and compared. Geometry comparison from both imaging modalities showed good agreement with a root mean squared error of 1.22 ± 0.15 mm and Hausdorff Distance of 5.45 ± 1.56 mm (mean ± sd, respectively). Using averaged models, circumferential strains in areas with calcifications were 23.2 ± 11.7% (mean ± sd) smaller and significantly distinguishable at the 5% level from areas without calcifications. For single segmentations, this was possible only in 50% of cases. The areas without calcifications showed greater heterogeneity, larger maximum strains, and smaller strain ratios when computed by use of the averaged models. Using these averaged models, reliable conclusions can be made about the local elastic properties of individual aneurysm (and long-term observations of their change), rather than just group comparisons. This is an important prerequisite for clinical application and provides qualitatively new information about the change of an abdominal aortic aneurysm in the course of disease progression compared to the diameter criterion.

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.

Automatic measurement of maximal diameter of abdominal aortic aneurysm on computed tomography angiography using artificial intelligence

INTRODUCTION

The treatment of abdominal aortic aneurysm (AAA) relies on surgical repair and the indication mainly depends on its size evaluated by the maximal diameter (Dmax). The aim of this study was to evaluate a new automatic method based on artificial intelligence (AI) to measure the Dmax on computed tomography angiography (CTA).

METHODS

A fully automatic segmentation of the vascular system was performed using a hybrid method combining expert system with supervised deep learning (DL). The aorta centreline was extracted from the segmented aorta and the aortic diameters were automatically calculated. Results were compared to manual segmentation performed by two human operators.

RESULTS

The median absolute error between the two human operators was 1.2 mm (IQR 0.5- 1.9). The automatic method using the DL algorithm demonstrated correlation with the human segmentation, with a median absolute error of 0.8 (0.5- 4.2) mm and a coefficient correlation of 0.91 (p<0.001).

CONCLUSION

Although validation in larger cohorts is required, this method brings perspectives to develop new tools to standardize and automate the measurement of AAA Dmax in order to help clinicians in the decision-making process.

Standardizing Methods of Reading CT Maximum Aortic Diameters Amongst Experts Reduces Variations and Discordance, Improving Accuracy

BACKGROUND

There is no consensus on the method of obtaining abdominal aortic aneurysm (AAA) maximum diameters based on computed tomographic angiography, and the reproducibility and accuracy of different methods have recently been debated due to advancements in imaging. This study compared the two most common methods based on orthogonal planes and centerline of flow to determine the discordances and accuracy amongst experiences readers.

METHODS

The computed tomographic angiography max diameters of 148 AAAs were measured by three experienced observers, including a vascular surgeon, a radiologist and an imaging cardiologist. Observers used two different methods with standardized protocols: multiplanar reformations based on orthogonal planes, and a software using 3D aortic reconstructions to create centerline flow lumen providing diameters based on cross sections perpendicular to this lumen. Agreements and reliability of measurement methods were assessed by intra-class correlation coefficient and Bland - Altman analysis. Discordances between measurements of the methods and the original reported measurement, as well as outside hospitals were compared.

RESULTS

The average age of the cohort was 75 years and aortic diameters ranged from 3.8 to 9.6 cm. For orthogonal readings, there were agreements within 3 mm between 86% and 92% of the time, while centerline - reading agreement was between 88% and 94%, which was not statistically significant. The intra-class correlation coefficient was high between method type and between readers. Within methods, agreement was between 0.96 and 0.97, while within - reader agreement measures was between 0.96 and 0.98. In comparison to the original and the outside hospital reports, 10% ≥ of the original and 20% ≥ of the outside hospital reported measurements were discordant between the readers.

CONCLUSION

Maximal AAA measurements can have substantial variability leading to clinical significance and change in patient management and outcomes. Based on the results, orthogonal and centerline measurement methods have equally high agreements and concordance within 3 mm and low variations at a high volume center. However, when compared to the official read reports, there is high discordance rates that can significantly alter patient outcomes. A standardized method of measurement maximum diameter can reduce variations and discordances among different methods.

Comparison of Outcomes Following EVAR Based on Aneurysm Diameter and Volume and Their Postoperative Variations

PURPOSE

to evaluate the impact of bi- and 3-dimensional preoperative aortic morphological features and their immediate postoperative variations on the outcome of abdominal aortic aneurysms (AAA) treated by endovascular exclusion with standard devices (EVAR).

MATERIALS AND METHODS

Double centre retrospective analysis of prospectively collected registry data of EVAR patients. For all patients, preoperative and 30-day computed tomographic angiography images (CTA) were reviewed. Preoperative maximum AAA diameter >59 mm and volume >159 cm³, and any 30-day postoperative increasing at CTA, were considered as potentially influencing the outcome. The outcome measures were: primary technical success; 30-day, 1-year, and mean follow-up reintervention, all-cause and AAA-related mortality rates, and also endoleak-related reinterventions.

RESULTS

Three hundred and thrity-three patients were enrolled. Mean preoperative and 30-day AAA diameter and volume were 50.4 mm ± 11.8 vs. 49.1 mm ± 12.1, and 112.9 cm3 ± 79.5 vs. 112.1 cm3 ± 80.5, respectively. Primary technical success was achieved in all cases. At 34.9 months follow-up, cumulative reintervention rate was 12.0%, mortality rates 7.2%, without AAA-related deaths. Endoleak-related reintervention rate was 7.5%. At uni- and multi-variate analysis, preoperative AAA diameter >59 mm, and AAA volume >159 cm³ were significantly associated to reintervention (P = 0.012; P = 0.002), and reintervention and death (P = 0.002; P = 0.001) during follow-up. Additionally, any increase in postoperative AAA diameter or volume was significantly associated with reintervention (P = 0.001, P = 0.001) and reintervention and death (P = 0.006, P = 0.001). Endoleak-related reintervention were also significantly associated with all of the analysed morphological parameters (P = 0.019, P = 0.005, P = 0.005, and P = 0.002, respectively).

CONCLUSIONS

Patients with larger baseline AAA size and volume as well as unfavourable early remodelling of the sac are associated to worse long-term EVAR outcome.

Feasibility and accuracy of a novel automated three-dimensional ultrasonographic analysis system for abdominal aortic aneurysm: comparison with two-dimensional ultrasonography and computed tomography

Background

Accurate measurement of the maximum aortic diameter (Dmax) is crucial for patients with abdominal aortic aneurysm (AAA). Aortic computed tomography (CT) provides accurate Dmax values by three-dimensional (3-D) reconstruction but may cause nephrotoxicity because of contrast use and radiation hazard. We aimed to evaluate the accuracy of a novel semi-automated 3-D ultrasonography (3-D US) system compared with that of CT as a reference.

Methods

Patients with AAA (n = 59) or individuals with normal aorta (n = 18) were prospectively recruited in an outpatient setting. Two-dimensional ultrasonography (2-D US) and 3-D US images were acquired with a single-sweep volumetric transducer. The analysis was performed offline with a software. Dmax and the vessel area of the Dmax slice were measured with 2-D US, 3-D US, and CT. The lumen and thrombus areas of the Dmax slice were also measured in 40 patients with intraluminal thrombus. Vessel and thrombus volumes were measured using 3-D US and CT.

Results

The Dmax values from 3-D US demonstrated better agreement (R² = 0.984) with the CT values than with the 2-D US values (R² = 0.938). Overall, 2-D US underestimated Dmax compared with 3-D US (32.3 ± 12.1 mm vs. 35.1 ± 12.0 mm). The Bland-Altman analysis of the 3-D US values, revealed better agreement with the CT values (2 standard deviations [SD], 2.9 mm) than with the 2-D US values (2 SD, 5.4 mm). The vessel, lumen, and thrombus areas all demonstrated better agreement with CT than with 2-D US (R² = 0.986 vs. 0.960 for the vessel, R² = 0.891 vs. 0.837 for the lumen, and R² = 0.977 vs. 0.872 for the thrombus). The thrombus volume assessed with 3-D US showed good correlation with the CT value (R² = 0.981 and 2 SD in the Bland-Altman analysis: 13.6 cm³).

Conclusions

Our novel semi-automated 3-D US analysis system provides more accurate Dmax values than 2-D US and provides precise volumetric data, which were not evaluable with 2-D US. The application of the semi-automated 3-D US analysis system in abdominal aorta assessment is easy and accurate.

Clinical Validation of a Semi-Automated Software for Maximal Diameter Measurements for Endovascular Repair Follow-up

PURPOSE

To compare automated measurements of maximal diameter (D_max) of abdominal aortic aneurysm (AAA) orthogonal to luminal or outer wall envelope centerline for endovascular repair (EVAR) follow-up.

MATERIAL AND METHODS

Eighty-three consecutive patients with AAA treated by EVAR who had at least 1 computed tomography (CT) scan before and 2 CT scans after EVAR with at least 5 months' interval were included. Three-dimensional reconstruction of the AAA was achieved with dedicated segmentation software. Performances of automated calculation algorithms of D_max perpendicular to lumen or outer wall envelope centerlines were then compared to manual measurement of D_max on double-oblique multiplanar reconstruction (gold standard). Accuracy of automated D_max measurements at baseline, follow-up, and progression over time was evaluated by calculation of mean error, Bland-Altman plot, and regression models.

RESULTS

Disagreement in D_max measurements between outer wall envelope algorithm and manual method was insignificant (mean error: baseline, -0.07 ± 1.66 mm, P = .7; first follow-up, 0.24 ± 1.69 mm, P = .2; last follow-up, -0.41 ± 2.74 mm, P = .17); whereas significant discrepancies were found between the luminal algorithm and the manual method (mean error: baseline, -1.24 ± 2.01 mm, P < .01; first follow-up, -1.49 ± 3.30 mm, P < .01; last follow-up, -1.78 ± 3.60 mm, P < .01). D_max progression results were more accurate with AAA outer wall envelope algorithm compared to luminal method (P = .2).

CONCLUSIONS

AAA outer wall envelope segmentation is recommended to enable automated calculation of D_max perpendicular to its centerline during EVAR follow-up.

A novel software tool for semi-automatic quantification of thoracic aorta dilatation on baseline and follow-up computed tomography angiography

A dedicated software package that could semi-automatically assess differences in aortic maximal cross-sectional diameters from consecutive CT scans would most likely reduce the post-processing time and effort by the physicians. The aim of this study was to present and assess the quality of a new tool for the semi-automatic quantification of thoracic aorta dilation dimensions. Twenty-nine patients with two CTA scans of the thoracic aorta for which the official clinical report indicated an increase in aortic diameters were included in the study. Aortic maximal cross-sectional diameters of baseline and follow-up studies generated semi-automatically by the software were compared with corresponding manual measurements. The semi-automatic measurements were performed at seven landmarks defined on the baseline scan by two operators. Bias, Bland–Altman plots and intraclass correlation coefficients were calculated between the two methods and, for the semi-automatic software, also between two observers. The average time difference between the two scans of a single patient was 1188 ± 622 days. For the semi-automatic software, in 2 out of 29 patients, manual interaction was necessary; in the remaining 27 patients (93.1%), semi-automatic results were generated, demonstrating excellent intraclass correlation coefficients (all values ≥ 0.91) and small differences, especially for the proximal aortic arch (baseline: 0.19 ± 1.30 mm; follow-up: 0.44 ± 2.21 mm), the mid descending aorta (0.37 ± 1.64 mm; 0.37 ± 2.06 mm), and the diaphragm (0.30 ± 1.14 mm; 0.37 ± 1.80 mm). The inter-observer variability was low with all errors in diameters ≤ 1 mm, and intraclass correlation coefficients all ≥ 0.95. The semi-automatic tool decreased the processing time by 40% (13 vs. 22 min). In this work, a semi-automatic software package that allows the assessment of thoracic aorta diameters from baseline and follow-up CTs (and their differences), was presented, and demonstrated high accuracy and low inter-observer variability.

How Precise Are Preinterventional Measurements Using Centerline Analysis Applications? Objective Ground Truth Evaluation Reveals Software-Specific Centerline Characteristics

PURPOSE

To evaluate different centerline analysis applications using objective ground truth from realistic aortic aneurysm phantoms with precisely defined geometry and centerlines to overcome the lack of unknown true dimensions in previously published in vivo validation studies.

METHODS

Three aortic phantoms were created using computer-aided design (CAD) software and a 3-dimensional (3D) printer. Computed tomography angiograms (CTAs) of phantoms and 3 patients were analyzed with 3 clinically approved and 1 research software application. The 3D centerline coordinates, intraluminal diameters, and lengths were validated against CAD ground truth using a dedicated evaluation software platform.

RESULTS

The 3D centerline position mean error ranged from 0.7±0.8 to 2.9±2.5 mm between tested applications. All applications calculated centerlines significantly different from ground truth. Diameter mean errors varied from 0.5±1.2 to 1.1±1.0 mm among 3 applications, but exceeded 8.0±11.0 mm with one application due to an unsteady distortion of luminal dimensions along the centerline. All tested commercially available software tools systematically underestimated centerline total lengths by -4.6±0.9 mm to -10.4±4.3 mm (maximum error -14.6 mm). Applications with the highest 3D centerline accuracy yielded the most precise diameter and length measurements.

CONCLUSION

One clinically approved application did not provide reproducible centerline-based analysis results, while another approved application showed length errors that might influence stent-graft choice and procedure success. The variety and specific characteristics of endovascular aneurysm repair planning software tools require scientific evaluation and user awareness.

Automatic detection of aorto-femoral vessel trajectory from whole-body computed tomography angiography data sets

Extraction of the aorto-femoral vessel trajectory is important to utilize computed tomography angiography (CTA) in an integrated workflow of the image-guided work-up prior to trans-catheter aortic valve replacement (TAVR). The aim of this study was to develop a new, fully-automated technique for the extraction of the entire arterial access route from the femoral artery to the aortic root. An automatic vessel tracking algorithm was first used to find the centerline that connected the femoral accessing points and the aortic root. Subsequently, a deformable 3D-model fitting method was used to delineate the lumen boundary of the vascular trajectory in the whole-body CTA dataset. A validation was carried out by comparing the automatically obtained results with semi-automatically obtained results from two experienced observers. The whole framework was validated on whole body CTA datasets of 36 patients. The average Dice similarity indexes between the segmentations of the automatic method and observer 1 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta were 0.977 ± 0.030, 0.980 ± 0.019, 0.982 ± 0.016; the average Dice similarity indexes between the segmentations of the automatic method and observer 2 were 0.950 ± 0.040, 0.954 ± 0.031 and 0.965 ± 0.019, respectively. The inter-observer variability resulted in a Dice similarity index of 0.954 ± 0.038, 0.952 ± 0.031 and 0.969 ± 0.018 for the left ilio-femoral artery, the right ilio-femoral artery and the aorta. The average minimal luminal diameters (MLDs) of the ilio-femoral artery were 6.03 ± 1.48, 5.70 ± 1.43 and 5.52 ± 1.32 mm for the automatic method, observer 1 and observer 2 respectively. The MLDs of the aorta were 13.43 ± 2.54, 12.40 ± 2.93 and 12.08 ± 2.40 mm for the automatic method, observer 1 and observer 2 respectively. The automatic measurement overestimated the MLD slightly in the ilio-femoral artery at the average by 0.323 mm (SD = 0.49 mm, p < 0.001) compared to observer 1 and by 0.51 mm (SD = 0.71 mm, p < 0.001) compared to observer 2. The proposed segmentation approach can automatically provide reliable measurements of the entire arterial accessing route that can be used to support TAVR procedures. To the best of our knowledges, this approach is the first fully automatic segmentation method of the whole aorto-femoral vessel trajectory in CTA images.

Prognostic and predictive biomarkers of abdominal aortic aneurysm growth rate

OBJECTIVES

To test the utility of clinical and circulating biomarkers to predict abdominal aortic aneurysm (AAA) growth rate and response to doxycycline therapy.

METHODS

Plasma samples were obtained in the Pharmaceutical Aneurysm Stabilization Trial that tested the effect of doxycycline (n = 44) vs. placebo (n = 49) in patients with a 35-50 mm AAA. Approximately 200 biomarkers were evaluated in a candidate approach that included markers of matrix turnover and cathepsin S activity and a broad-based approach of predominantly inflammation-related and clinical biomarkers.

RESULTS

In a recursive partitioning based analysis, total cholesterol, baseline AAA size, and apolipoprotein B were prognostic of AAA growth in the placebo group whereas elastin and biglycan degradation products were predictive of AAA growth with doxycycline treatment. Univariate analysis of these biomarkers showed that baseline total cholesterol (r = 0.38, unadjusted P = 0.011), apolipoprotein B (r = 0.41, unadjusted P = 0.005), and baseline AAA size (r = 0.35, unadjusted P = 0.013) correlated with AAA growth in the placebo but not the doxycycline group. Elastin fragments were associated with 18 month AAA growth (r = 0.33, unadjusted P = 0.031) in the doxycycline group.

LIMITATIONS

Limitations of this study include small sample size, a retrospective growth analysis, and translatability of the method used to measure the analytes.

CONCLUSIONS

This study implies that total cholesterol, baseline AAA size, and apolipoprotein B are predictors of AAA growth. Levels of elastin and biglycan fragments are predictive of doxycycline effects on AAA growth and provide a clue towards this unexpected negative effect.

Abdominal aortic aneurysms revisited: MDCT with multiplanar reconstructions for identifying indicators of instability in the pre- and postoperative patient

Rupture of an abdominal aortic aneurysm is commonly a fatal event. Multidetector computed tomographic (CT) signs of frank aortic rupture are usually readily apparent and widely understood. However, diagnosing an impending aortic rupture on the basis of imaging findings can prove more difficult. CT is the primary modality used for serial imaging in patients with aortic aneurysm and may show findings indicative of aortic instability. Therefore, it is critical that radiologists be familiar with the CT findings of aortic instability to avert the potential complications of hemorrhage, end organ or limb ischemia, and death. Various preoperative CT indicators have been previously described in both research investigations and review articles. A large baseline aneurysm size and a rapid increase in size over time are associated with a higher risk for rupture. The importance of obtaining accurate measurements with multiplanar reconstructions and the role of new semiautomated tools for obtaining accurate, reproducible measurements are discussed. Additional CT findings that reflect aortic aneurysm instability include luminal expansion with lysis of thrombus, intramural hemorrhage (ie, the crescent sign), periaortic hemorrhage, a penetrating atherosclerotic ulcer, and contained rupture (ie, the draped aorta sign). After open or endovascular aneurysm repair, CT is routinely used to monitor for graft complications. In this setting, radiologists should understand that the presence of an endoluminal stent or surgical graft does not preclude aortic rupture. Online supplemental material is available for this article.

Automatic detection of selective arterial devices for advanced visualization during abdominal aortic aneurysm endovascular repair

Here we address the automatic segmentation of endovascular devices used in the endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) that deform vascular tissues. Using this approach, the vascular structure is automatically reshaped solving the issue of misregistration observed on 2D/3D image fusion for EVAR guidance. The endovascular devices we considered are the graduated pigtail catheter (PC) used for contrast injection and the stent-graft delivery device (DD). The segmentation of the DD was enhanced using an asymmetric Frangi filter. The segmented geometries were then analysed using their specific features to remove artefacts. The radiopaque markers of the PC were enhanced using a fusion of Hessian and newly introduced gradient norm shift filters. Extensive experiments were performed using a database of images taken during 28 AAA-EVAR interventions. This dataset was divided into two parts: the first half was used to optimize parameters and the second to compile performances using optimal values obtained. The radiopaque markers of the PC were detected with a sensitivity of 88.3% and a positive predictive value (PPV) of 96%. The PC can therefore be positioned with a majority of its markers localized while the artefacts were all located inside the vessel lumen. The major parts of the DD, the dilatator tip and the pusher surfaces, were detected accurately with a sensitivity of 85.9% and a PPV of 88.7%. The less visible part of the DD, the stent enclosed within the sheath, was segmented with a sensitivity of 63.4% because the radiopacity of this region is low and uneven. The centreline of the DD in this stent region was alternatively traced within a 0.74 mm mean error. The automatic segmentation of endovascular devices during EVAR is feasible and accurate; it could be useful to perform elastic registration of the vascular lumen during endovascular repair.

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods.

Maximum Diameter of Native Abdominal Aortic Aneurysm Measured by Angio-Computed Tomography: Reproducibility and Lack of Consensus Impacts on Clinical Decisions

BACKGROUND

Computed tomography angiography (CTA) is the reference technique for the measurement of native maximum abdominal aortic aneurysm (AAA) diameter when surgery is being considered. However, there is a wide choice available for the methodology of maximum AAA diameter measurement on CTA, and to date, no consensus has been reached on which method is best. We analyzed clinical decisions based on these various measures of native maximum AAA diameter with CTA, then analyzed their reproducibility and identified the method of measurement yielding the highest agreement in terms of patient management.

MATERIALS AND METHODS

Three sets of measures in 46 native AAA were obtained, double-blind by three radiologists (J, S, V) on orthogonal planes, curved multiplanar reconstructions, and semi-automated-software, based on the AAA-lumen centerline. From each set, the clinical decision was recorded as follows: "Follow-up" (if all diameters <50 mm), "ambiguous" (if at least one diameter <50 mm AND at least one ≥50 mm) or "Surgery " (if all diameters ≥50 mm). Intra- and interobserver agreements in clinical decisions were compared using the weighted Kappa coefficient.

RESULTS

Clinical decisions varied according to the measurement sets used by each observer, and according to intra and interobserver (lecture#1) reproducibility. Based on the first reading of each observer, the number of AAA proposed for surgery ranged from 11 to 24 for J, 5 to 20 for S, and 15 to 23 for V. The rate of AAAs classified as "ambiguous" varied from 11% (5/46) to 37% (17/46). The semi-automated method yielded very good intraand interobserver agreements in clinical decisions in all comparisons (Kappa range 0.83-1.00).

CONCLUSION

The semi-automated method seems to be appropriate for native AAA maximum diameter measurement on CTA. In the absence of AAA outer-wallbased software more robust for complex AAA, clinical decisions might best be made with diameter values obtained using this technique.

Impact of contrast injection and stent-graft implantation on reproducibility of volume measurements in semiautomated segmentation of abdominal aortic aneurysm on computed tomography

PURPOSE

To assess the impact of contrast injection and stent-graft implantation on feasibility, accuracy, and reproducibility of abdominal aortic aneurysm (AAA) volume and maximal diameter (D-max) measurements using segmentation software.

MATERIALS AND METHODS

CT images of 80 subjects presenting AAA were divided into four equal groups: with or without contrast enhancement, and with or without stent-graft implantation. Semiautomated software was used to segment the aortic wall, once by an expert and twice by three readers. Volume and D-max reproducibility was estimated by intraclass correlation coefficients (ICC), and accuracy was estimated between the expert and the readers by mean relative errors.

RESULTS

All segmentations were technically successful. The mean AAA volume was 167.0 ± 82.8 mL and the mean D-max 55.0 ± 10.6 mm. Inter- and intraobserver ICCs for volume and D-max measurements were greater than 0.99. Mean relative errors between readers varied between -1.8 ± 4.6 and 0.0 ± 3.6 mL. Mean relative errors in volume and D-max measurements between readers showed no significant difference between the four groups (P ≥ 0.2).

CONCLUSION

The feasibility, accuracy, and reproducibility of AAA volume and D-max measurements using segmentation software were not affected by the absence of contrast injection or the presence of stent-graft.

KEY POINTS

• AAA volumetry by semiautomated segmentation is accurate on CT following endovascular repair. • AAA volumetry by semiautomated segmentation is accurate on unenhanced CT. • Standardization of the segmentation technique maximizes the reproducibility of volume measurements.

Morphologic evaluation of ruptured and symptomatic abdominal aortic aneurysm by three-dimensional modeling

OBJECTIVE

To identify geometric indices of abdominal aortic aneurysms (AAAs) on computed tomography that are associated with higher risk of rupture.

METHODS

This retrospective case-control, institutional review board-approved study involved 63 cases with ruptured or symptomatic AAA and 94 controls with asymptomatic AAA. Three-dimensional models were generated from computed tomography segmentation and used for the calculation of 27 geometric indices. On the basis of the results of univariate analysis and multivariable sequential logistic regression analyses with a forward stepwise model selection based on likelihood ratios, a traditional model based on gender and maximal diameter (Dmax) was compared with a model that also incorporated geometric indices while adjusting for gender and Dmax. Receiver operating characteristic (ROC) curves were calculated for these two models to evaluate their classification accuracy.

RESULTS

Univariate analysis revealed that gender (P = .024), Dmax (P = .001), and 14 other geometric indices were associated with AAA rupture at P < .05. In the multivariable analysis, adjusting for gender and Dmax, the AAA with a higher bulge location (P = .020) and lower mean averaged area (P = .005) were associated with AAA rupture. With these two geometric indices, the area under the ROC curve showed an improvement from 0.67 (95% confidence interval, 0.58-0.77) to 0.75 (95% confidence interval, 0.67-0.83; P < .001). Our predictive model showed comparable sensitivity (64% vs 60%) and specificity (79% vs 77%) with current treatment criteria based on gender and diameter at the point optimizing the Youden index (sensitivity + specificity - 1) on the ROC curve.

CONCLUSIONS

Two geometric indices derived from AAA three-dimensional modeling were independently associated with AAA rupture. The addition of these indices in a predictive model based on current treatment criteria modestly improved the accuracy to detect aneurysm rupture.

Abdominal aortic aneurysm imaging with 3-D ultrasound: 3-D-based maximum diameter measurement and volume quantification

The clinical reliability of 3-D ultrasound imaging (3-DUS) in quantification of abdominal aortic aneurysm (AAA) was evaluated. B-mode and 3-DUS images of AAAs were acquired for 42 patients. AAAs were segmented. A 3-D-based maximum diameter (Max3-D) and partial volume (Vol30) were defined and quantified. Comparisons between 2-D (Max2-D) and 3-D diameters and between orthogonal acquisitions were performed. Intra- and inter-observer reproducibility was evaluated. Intra- and inter-observer coefficients of repeatability (CRs) were less than 5.18 mm for Max3-D. Intra-observer and inter-observer CRs were respectively less than 6.16 and 8.71 mL for Vol30. The mean of normalized errors of Vol30 was around 7%. Correlation between Max2-D and Max3-D was 0.988 (p < 0.0001). Max3-D and Vol30 were not influenced by a probe rotation of 90°. Use of 3-DUS to quantify AAA is a new approach in clinical practice. The present study proposed and evaluated dedicated parameters. Their reproducibility makes the technique clinically reliable.

Analysis of the thoracic aorta using a semi-automated post processing tool

OBJECTIVE

To evaluates a semi-automated method for Thoracic Aortic Aneurysm (TAA) measurement using ECG-gated Dual Source CT Angiogram (DSCTA).

METHODS

This retrospective HIPAA compliant study was approved by our IRB. Transaxial maximum diameters of outer wall to outer wall were studied in fifty patients at seven anatomic locations of the thoracic aorta: annulus, sinus, sinotubular junction (STJ), mid ascending aorta (MAA) at the level of right pulmonary artery, proximal aortic arch (PROX) immediately proximal to innominate artery, distal aortic arch (DIST) immediately distal to left subclavian artery, and descending aorta (DESC) at the level of diaphragm. Measurements were performed using a manual method and semi-automated software. All readers repeated their measurements. Inter-method, intra-observer and inter-observer agreements were evaluated according to intraclass correlation coefficient (ICC) and Bland-Altman plot. The number of cases with manual contouring or center line adjustment for the semi-automated method and also the post-processing time for each method were recorded.

RESULTS

The mean difference between semi-automated and manual methods was less than 1.3mm at all seven points. Strong inter-method, inter-observer and intra-observer agreement was recorded at all levels (ICC ≥ 0.9). The maximum rate of manual adjustment of center line and contour was at the level of annulus. The average time for manual post-processing of the aorta was 19 ± 0.3 min, while it took 8.26 ± 2.1 min to do the measurements with the semi-automated tool (Vitrea version 6.0.0.1 software). The center line was edited manually at all levels, with most corrections at the level of annulus (60%), while the contour was adjusted at all levels with highest and lowest number of corrections at the levels of annulus and DESC (75% and 0.07% of the cases), respectively.

CONCLUSION

Compared to the commonly used manual method, semi-automated measurement of vessel dimensions is feasible in the thoracic aorta with the advantage of reduced post-processing time.

A systematic review of protocols for the three-dimensional morphologic assessment of abdominal aortic aneurysms using computed tomographic angiography

The morphology of infrarenal abdominal aortic aneurysms (AAAs) directly influences the perioperative outcome and long-term durability of endovascular aneurysm repair. A variety of methods have been proposed for the characterization of AAA morphology using reconstructed three-dimensional (3D) computed tomography (CT) images. At present, there is lack of consensus as to which of these methods is most applicable to clinical practice or research. The purpose of this review was to evaluate existing protocols that used 3D CT images in the assessment of various aspects of AAA morphology. An electronic search was performed, from January 1996 to the end of October 2010, using the Embase and Medline databases. The literature review conformed to PRISMA statement standards. The literature search identified 604 articles, of which 31 studies met inclusion criteria. Only 15 of 31 studies objectively assessed reproducibility. Existing published protocols were insufficient to define a single evidence-based methodology for preoperative assessment of AAA morphology. Further development and expert consensus are required to establish a standardized and validated protocol to determine precisely how morphology relates to outcomes after endovascular aneurysm repair.

Automatic aorta segmentation and valve landmark detection in C-arm CT for transcatheter aortic valve implantation

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.

Standardization of outcome measures in clinical trials of pharmacological treatment for abdominal aortic aneurysm

An abdominal aortic aneurysm (AAA) is a common aortic wall disease with an increased prevalence in the elderly population (4-8% for those aged >65 years). Many AAAs are slow growing and remain insidious. Current standard of care for patients with small AAAs (<49 mm) is surveillance, with interventional therapy (open surgical repair or endovascular aneurysm repair) recommended for large (>50-55 mm), rapidly growing (>10 mm/year) or symptomatic AAAs. Although open surgical repair or endovascular aneurysm repair are effective, significant short- and long-term postoperative morbidity and mortality occurs. Currently, there is no pharmacological treatment specific for AAA; the need for the development of targeted pharmacological therapies based on clinically relevant and feasible outcomes acceptable to the medical community, regulatory agencies and third-party payers is high. A consensus on such end points will be critical to accelerating the development of pharmacological agents to prevent formation, arrest the expansion and reduce the rupture risk of AAA.

Computed tomography dataset postprocessing: from data to knowledge

The introduction of spiral computed tomography from the days of single-slice spiral to today's 64-row multidetector computed tomography and beyond creates datasets with unprecedented spatial and temporal resolution. The key to computed tomography imaging in the big picture is not in the acquisition of data, but in the use of the data acquired. By supplementing traditional axial interpretation with 3-dimensional rendering of the computed tomography volume, the greatest amount of information available is extracted. The information provided by a comprehensive postprocessed study, which includes multiplanar reconstruction in the coronal, sagittal, and oblique plane, as well as 3-dimensional maps of both the arterial and venous phase datasets using volume rendering and maximum intensity projection techniques, allows for key clinical decisions to be made with a high degree of accuracy. Postprocessing of computed tomography data is thus no longer an option, but a true requirement in this era of 64-row multidetector computed tomography and beyond.

Reproducibility of deriving parameters of AAA rupture risk from patient-specific 3D finite element models

PURPOSE

To assess the reproducibility of estimating biomechanical parameters of abdominal aortic aneurysms (AAA) based on finite element (FE) computations derived from a commercially available, semiautomatic vascular analyzer that reconstructs computed tomographic angiography (CTA) data into FE models.

METHODS

The CTA data from 10 consecutive male patients (mean age 74 years, range 63-87) with a fusiform infrarenal AAA >5 cm in diameter were used for this study, along with the CTA scans from 4 individuals without aortic disease. Three different observers used semiautomatic reconstruction software to create deformable contour models from axial CT scans. These 3-dimensional FE models captured the aortic wall and thrombus tissue using isotropic finite strain constitutive modeling. Geometric (maximum diameter and volume measurements based on an anatomical centerline) and biomechanical determinants [aneurysm peak wall stress (PWS) and the peak wall rupture risk (PWRR) index] were then calculated from the FE models. The determinations were made 5 times for each anonymized dataset presented for analysis in random order (5-fold measurements for 14 datasets produced 210 measurements from the 3 observers). Inter- and intraobserver variability were assessed by calculating the coefficient of variation of these repeated measures. The methodological variations were expressed with the intraclass correlation coefficient (ICC) and Bland-Altman plots.

RESULTS

The median segmentation time was < 1 hour (mean 39.2 minutes, range 25-48) for datasets from the AAA patients; for the healthy individuals, segmentation times were considerably shorter (median 8.7 minutes, range 4-15). Intraobserver reproducibility was high, as represented by a CV <3% for the diameter measurement and < 5.5% for volume, PWS, and the PWRR index. The ICC was 0.97 (range 0.95-0.98) for diameter and 0.98 (range 0.97-0.99) for volume; for PWS and the PWRR index, the ICCs were equal at 0.98 (range 0.97-0.99).

CONCLUSION

The reproducibility of volume and maximum diameter measurements in infrarenal AAAs with FE analysis is high. With the model used in this semiautomatic reconstruction software, wall stress analysis can be achieved with high agreement among observers and in serial measurements by a single observer.

Porphyromonas gingivalis participates in pathogenesis of human abdominal aortic aneurysm by neutrophil activation. Proof of concept in rats

Background

Abdominal Aortic Aneurysms (AAAs) represent a particular form of atherothrombosis where neutrophil proteolytic activity plays a major role. We postulated that neutrophil recruitment and activation participating in AAA growth may originate in part from repeated episodes of periodontal bacteremia.

Methods and Findings

Our results show that neutrophil activation in human AAA was associated with Neutrophil Extracellular Trap (NET) formation in the IntraLuminal Thrombus, leading to the release of cell-free DNA. Human AAA samples were shown to contain bacterial DNA with high frequency (11/16), and in particular that of Porphyromonas gingivalis (Pg), the most prevalent pathogen involved in chronic periodontitis, a common form of periodontal disease. Both DNA reflecting the presence of NETs and antibodies to Pg were found to be increased in plasma of patients with AAA. Using a rat model of AAA, we demonstrated that repeated injection of Pg fostered aneurysm development, associated with pathological characteristics similar to those observed in humans, such as the persistence of a neutrophil-rich luminal thrombus, not observed in saline-injected rats in which a healing process was observed.

Conclusions

Thus, the control of periodontal disease may represent a therapeutic target to limit human AAA progression.