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Neuroradiology

, Volume 61, Issue 2, pp 147–153 | Cite as

Comparability of semiautomatic tortuosity measurements in the carotid artery

  • Evelien E. de Vries
  • Vanessa E. C. Pourier
  • Constance J. H. C. M. van Laarhoven
  • Evert J. Vonken
  • Joost A. van Herwaarden
  • Gert J. de BorstEmail author
Open Access
Diagnostic Neuroradiology

Abstract

Purpose

Increased arterial tortuosity has been suggested as a predisposing factor for carotid artery dissection, which is an important risk factor for development of extracranial carotid artery aneurysms (ECAA). Prior to comparison with non-ECAA controls, the optimal measurement technique should be defined. This study describes the difference between software packages in terms of reproducibility and absolute outcome of arterial tortuosity measurements in ECAA patients.

Methods

CT-angiography analysis was performed on 12 ECAA patients selected from our registry, using four software packages: 3mensio Vascular, TeraRecon, Vital Images, and Aycan OsiriX PRO. The tortuosity index (TI) was calculated from the skull base until the carotid bifurcation and aortic arch, and was defined as the centerline’s true length divided by the straight line distance. Intraclass correlation coefficients (ICC) with 95% confidence intervals were calculated to quantify inter- and intra-observer variability within one software package, and differences in measured TI between packages.

Results

Inter-observer agreement was nearly perfect for 3mensio, excellent for Vital Images and OsiriX, and substantial for TeraRecon, with ICC 0.99 (0.96–1.0), 0.90 (0.69–0.97), 0.84 (0.53–0.95), and 0.72 (0.28–0.91), respectively. Intra-observer agreement ranged from ICC 1.0 for 3mensio to 0.91 for TeraRecon. Agreements in TI ranged from ICC 0.99 (0.98–1.0) for 3mensio vs. OsiriX, to 0.95 (0.82–0.98) for 3mensio vs. TeraRecon. Median time needed to complete one round of measurements was highest for OsiriX (p = 0.013).

Conclusions

Carotid artery tortuosity measurements are reproducible and comparable between current commercially available software packages, with high intra-observer agreement. Although the reproducibility differed per software packages, all packages scored an acceptable inter-observer agreement.

Keywords

Carotid artery disease Carotid aneurysm Software validation CT angiography Quantitative analysis 

Abbreviations

CCA

Common carotid artery

ECAA

Extracranial carotid artery aneurysm

ICA

Internal carotid artery

ICC

Intraclass correlation coefficient

TI

Tortuosity index

Introduction

Increased arterial tortuosity has been suggested as a predisposing factor for carotid artery dissection [1, 2, 3], which is in turn an important etiological risk factor for extracranial carotid artery aneurysm (ECAA) formation [4]. Conversely, although experts in the field have suggested that most ECAA have a relative elongated inflow and outflow track, no validated data exist about arterial tortuosity in ECAA patients. In fact, little is known about the natural clinical course and risk factors for adverse outcome of ECAA patients [5]. If increased arterial tortuosity would exist in ECAA patients, it may aid in individual patient’s risk prediction for adverse outcome. In addition, severe tortuosity affects planning and performing interventions for aneurysm exclusion when indicated [6].

In order to validate the relative tortuosity in patients with ECAA as compared to patients with normal carotid arteries, a comparative study with non-ECAA controls should be set-up. Beforehand, it is crucial to first establish a standardized method to define and measure the arterial tortuosity. Tortuosity is defined in literature as the property of the artery to have “many turns” [7]. Multiple definitions exist to quantify arterial tortuosity, but due to its good reproducibility values, it is commonly referred to as the tortuosity index (TI), which is the true length of the vessel divided by the straight distance [8, 9, 10, 11].

Manual measurements in different arterial territories are reliable, but time consuming [12, 13, 14]. Multiple software packages to facilitate (semiautomatic) TI measurements are commercially available. It is unknown how the tortuosity measurements performed by these different packages relate to each other in terms of inter- and intra-observer variability, and differences in absolute measured tortuosity. Furthermore, it is unknown how increased vascular tortuosity will influence the assessed values within these software packages.

Accordingly, the present study aimed to investigate the difference between these software packages in terms of reproducibility and absolute outcome of carotid artery tortuosity measurements, in patients with an ECAA.

Methods

Case selection

Datasets of 12 patients with an ECAA all located in the internal carotid artery (ICA) were retrieved from our Carotid Aneurysm Registry (www.carotidaneurysmregistry.com) [15]. The registry has been approved by the local ethics committee, and all patients gave informed consent. For the purpose of this study, a computed tomography angiography (CTA) scan with slice thickness below 1.0 mm was eligible for inclusion to guarantee proper slice thickness for reconstruction. This necessary condition limited the amount of eligible CTAs to 12 due to rarity of disease. The CTAs had been performed for evaluation or treatment of ECAA between 2008 and 2017, in the University Medical Center Utrecht. We aimed to select an equal amount of cases with fusiform and saccular ECAA. As specified within the registry protocol [15], fusiform aneurysms were defined as ≥ 150% diameter increase of the normal ICA diameter, while saccular aneurysms were defined as a distended sac of any size affecting only part of the ICA circumference.

Imaging

A 64-slice or 128-slice CT scanner (Philips Brilliance; Philips medical systems, Best, the Netherlands) was used to acquire the CTA scans. The carotid arteries were visualized from the aortic arch to the skull base. Median slice thickness was 0.67 mm (range 0.62–0.90 mm), increment 0.33, collimation 64 × 0.625, and pitch 0.609. Radiation exposure parameters were 100–120 kV and 150–300 mA s. The field of view is set per patient. Injection of 65 ml intravascular contrast (ultravist 300, Schering, Berlin, Germany) was followed by a saline bolus of 40 ml, both at a flow rate of 6 ml per second.

Software packages

Our study focused on the evaluation of semiautomatic measurement software packages. A search was performed to identify software packages which facilitated semiautomatic vessel tortuosity measurements and were commercially available. To this end, the MEDLINE database was searched using the search terms “software,” “length” or “tortuosity,” “vascular,” and synonyms. Availability of free trial licenses was required in order to participate in this comparative study. Four commonly used commercial software packages were selected: 3mensio Vascular (version 8.1, Pie Medical Imaging BV, Maastricht, the Netherlands), Aquarius iNtuition (version 4.4.12.265, iNtuition Cloud, TeraRecon, Foster City, CA, USA), Vitrea (version 7.4, Vital Images Inc., Toshiba Medical, Minnetonka, MN, USA), and Aycan OsiriX PRO (version 3.10.xxx, Aycan Medical Systems, Rochester, NY, USA). All software packages are commonly used for semiautomatic (vessel) analysis and centerline composition [16, 17, 18, 19, 20, 21, 22, 23, 24, 25].

Study design

Two observers (EEV and VECP) independently scored the 12 datasets at two time points (round 1 and 2, interval ≥ 1 week) with the four software packages. Observers were blinded to each other’s measurements and to earlier measurements with the same software package. For each software package, both observers received a training session by the company of 1 h, and practiced three measurements in order to familiarize with the package and overcome the early learning curve.

Outcome measures

In all software programs, carotid artery tortuosity was determined by calculating the tortuosity index (TI) of the carotid artery ipsilateral to the ECAA. The TI was defined as the true length of the central luminal line (CLL) divided by the straight distance. It was calculated in two ways: from the skull base (just proximal from the carotid siphon) until (1) the carotid bifurcation and (2) aortic arch (Fig. 1).
Fig. 1

In each dataset, carotid artery tortuosity index (TI) was calculated from the skull base until a the carotid bifurcation and b the aortic arch (this rendering using Vital Images software). A left internal carotid artery (ICA) with a saccular ECAA is shown, depicted from the aortic arch until the cavernous part of the ICA. The blue line indicates the part of the centerline (in red) that was used to measure the TI of the ICA (a) or ICA plus common carotid artery (b). This true length was divided by the straight distance (shown in yellow)

The primary outcome measure was the reproducibility of tortuosity measurements, expressed as the inter- and intra-observer variability in the TI, as measured with the different software programs. The secondary outcome measure was defined as the agreement in absolute TI between the software programs. For both the primary and secondary outcome measures, the correlation between measurements was calculated by using the TI from the skull base to the carotid bifurcation, since ECAAs are located primarily in the ICA. The tertiary outcome measure was the time needed per scan (difference in time between scoring round 1 and 2 (learning curve)).

Statistics

Inter- and intra-observer variability

The intraclass correlation coefficient (ICC) was used to calculate the inter- and intra-observer variability for measurements obtained with one software package (model: two-way mixed, type: consistency). An ICC of 1.0 equals perfect agreement, an ICC of 0.81–0.99 excellent agreement, and an ICC of 0.61–0.80 substantial agreement [26]. The first round of measurements of both investigators was compared in order to calculate inter-observer variability for each software package. Bland-Altman plots were constructed to assess presence of systematic differences between both observers.

Agreement on absolute TI

The ICC was also used to calculate agreements on obtained TIs per software package. In order to calculate the differences in measured absolute TI with each software package, the average TI (TIaverage) per case was calculated by taking the average of all four measurements. This was done for each software package separately, thereby producing one TIaverage per software package for each of the 12 cases.

Time needed per scan and learning curve

A Kruskal-Wallis test was used to calculate differences between software packages in time needed to complete all measurements, while a Wilcoxon signed rank test was used to calculate differences in time needed to complete round 1 and 2 within one software package. Mann-Whitney U tests were used as post-hoc tests, and Bonferroni correction was applied to account for multiple testing.

Statistical analyses were conducted using SPSS 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.). A P value less than 0.05 was considered statistically significant.

Results

Patient and aneurysm characteristics

Most ECAA patients were male (n = 7; 58%) with a median age of 62 years (range 25–80 years). Aneurysms were saccular (n = 7; 58%) or fusiform (n = 5; 42%). The median aneurysm diameter reported in the patient records was 12 mm (range 7–40 mm) for saccular aneurysms, and 11 mm (range 5–32 mm) for fusiform aneurysms (Table 1).
Table 1

Patient and aneurysm characteristics

Characteristic

ECAA (n = 12)

Male

7 (58)

Age (years)

62 (25–80)

Left carotid artery affected

6 (50)

Aneurysm shape

 Saccular

7 (58)

 Fusiform

5 (42)

Reported aneurysm diameter (mm)

 Saccular

12 (7–40)

 Fusiform

11 (5–32)

Data are given as numbers (percentage) or median (range)

ECAA extracranial carotid artery aneurysm

Reproducibility of measurements

Inter- and intraclass correlations between software packages are summarized in Table 2. The agreement between both observers was nearly perfect for measurements performed with 3mensio, excellent for Vital Images and OsiriX, and substantial for TeraRecon. Bland and Altman plots were visually evaluated and revealed no clear systematic differences between both observers (Fig. 2). The average intra-observer variability ranged from a perfect agreement of 1.0 for 3mensio to 0.91 for TeraRecon.
Table 2

Inter- and intra-observer variability of tortuosity index (TI), measured from the carotid bifurcation to the skull base

 

Inter-observer variability

Intra-observer variability (observer 1)

Intra-observer variability (observer 2)

Average intra-observer variability

3mensio

0.99 (0.96–1.0)

1.0 (0.99–1.0)

0.99 (0.96–1.0)

1.0

OsiriX

0.84 (0.53–0.95)

0.95 (0.85–0.99)

0.90 (0.68–0.97)

0.93

TeraRecon

0.72 (0.28–0.91)

0.97 (0.90–0.99)

0.85 (0.55–0.95)*

0.91

Vital Images

0.90 (0.69–0.97)

0.99 (0.97–1.0)

0.97 (0.86–0.99)

0.98

Inter-observer variability was calculated using the first round of measurement of both observers. The average intra-observer variability was calculated as the average ICC of observer 1 and 2

Values are intraclass correlation coefficient (ICC) with 95% confidence interval (CI)

*ICC based on 9 out of 12 cases

Fig. 2

Bland-Altman plots showing agreement of two observers on measured TI for each of the 12 cases. Each graph represents a different software package. The line in the middle represents the mean difference of the TI between the two observers, and the two dotted lines represent the upper and lower limits of agreement (mean difference ± 1.96 × standard deviation)

Comparison of measured tortuosity indices

In order to calculate the differences in absolute measured TIaverage with each software package, the ICC was calculated for each software comparison (Table 3). Agreement on measured average TIs between all four packages was excellent, as the ICC for these comparisons equaled 0.95 or higher. Thus, all software packages measured similar TIs for the 12 cases. The median TIaverage of the 12 cases was 1.42 (interquartile range [IQR] 1.29–1.65) from the carotid bifurcation until the skull base, versus 1.29 (IQR 1.15–1.45) from the aortic arch until the skull base.
Table 3

Intraclass correlation coefficients (ICCs) with 95% confidence intervals (CIs) of for each software package comparison of the average tortuosity indices (TIaverage)

Software comparison

ICC (95% CI)

3mensio vs TeraRecon

0.95 (0.82–0.98)

3mensio vs Vital Images

0.98 (0.91–1.0)

3mensio vs OsiriX

0.99 (0.98–1.0)

TeraRecon vs Vital Images

0.99 (0.96–1.0)

TeraRecon vs OsiriX

0.95 (0.83–0.99)

Vital Images vs OsiriX

0.99 (0.96–1.0)

Of note, in OsiriX software, the straight distance had to be drawn in one and the same sagittal/coronal slice even if the skull base and proximal endpoint (bifurcation or aortic arch) were located in a different slice, which could have led to an overestimation of the TI.

Usability of the software packages

The time needed to complete the first round of measurements was comparable between 3mensio, TeraRecon, and Vital Images, with time ranging from a median of 8.5 (IQR 5.0–14.8) minutes for TeraRecon to 11.8 (IQR 5.8–15.6) minutes for Vital Images, while OsiriX software took median 16.8 (IQR 14.4–18.5) minutes (Fig. 3). This difference was significant for both round 1 (p = 0.013) and round 2 (p < 0.001), and post-hoc tests revealed that OsiriX took significantly longer than the other software packages. Since the number of corrections needed to create a proper fit for the CLL was graded equal for all packages, this difference was probably attributable to a longer time-to-segmentation of the carotid artery for OsiriX software. Except for TeraRecon, round 2 of measurements took significantly shorter compared to round 1. Thus, the usage of the software packages seemed to encompass a significant learning curve (but without influencing reproducibility).
Fig. 3

Time needed to complete the first and second round of measurements, for each software package separately. Significant differences are flagged with: *p < 0.008, **p < 0.001. Due to Bonferroni correction for multiple testing, the significance level was set at p < 0.008 for comparisons between software packages (Mann-Whitney U test), and at p < 0.013 for comparisons between round 1 and 2 within a software package (Wilcoxon signed rank test)

Discussion

The present study shows that carotid artery tortuosity measurements are reproducible and comparable between the four commercially available software packages that were included in this analysis: 3mensio, TeraRecon, Vital Images, and OsiriX. Calculated inter-observer agreements ranged from 0.99 to 0.72, and agreements between the packages on measured absolute tortuosity indices equaled 0.95 or higher. This suggests that all four software packages can be applied for TI measurements of carotid arteries, and that obtained results with these different software packages are comparable.

A range of different software packages is being used for assessment of vessel anatomies. As clinicians often have limited access to direct comparisons to aid in software selection, it is unclear whether outcomes of these software packages are comparable. To our knowledge, few research groups examined comparability of software packages for measurements on patient vessels. Three papers focused on (phantom) abdominal aneurysm diameter measurements, and demonstrated high levels of agreement for 3mensio, TeraRecon, and Simbionix PRORS software (ICC ≥ 0.82) [16, 17], or TeraRecon versus OsiriX software (ICC ≥ 0.82) [24]. A comparison of 3mensio, TeraRecon, Philips, and Siemens software demonstrated good correlations for semiautomatic carotid stenosis measurements with manual measurements (ICC ≥ 0.81) [19].

However, high vessel tortuosity is likely to challenge semiautomatic centerline composition. We found a median tortuosity index of 1.42 for the internal carotid artery of these ECAA patients, which is deemed higher than the 1.19 of normal internal carotid arteries [18]. Obviously, these data need to be evaluated in more detail in a direct comparison with non-ECAA controls. Nevertheless, the present study shows that even in these challenging vessels, tortuosity measurements remain comparable between software packages. Although fully automated measurements would most likely increase observer agreement and lower time consumption, we believe manual correction of the centerlines will often be required to ensure accuracy of measurements, especially in these tortuous cases.

Currently, little is known about the natural clinical course of patients with an ECAA [5, 27], and no guidelines exist regarding treatment or follow-up. Increased tortuosity values have been linked to increased clinical risk of dissection in different arterial territories [11, 28]. However, whether these vessels were tortuous at baseline and therefore a cause of ECAA formation or merely a consequence remains to be elucidated. Nonetheless, carotid artery tortuosity in ECAA patients has potential as a risk predictor of adverse outcome, and may provide valuable additional sensitivity for the individual ECAA patient’s risk prediction.

There are several limitations to the present study. Due to rarity of disease, only 12 cases could be included in this pilot study. Moreover, merely two observers scored the scans, both with over 1 year of experience with these specific carotid CTA scans. As the primary purpose of this study was to compare the performance of the software programs rather than assessing the true TI values, we consider a good inter-observer reliability between these similarly trained observers most relevant and sufficient. Also, both observers scored the software packages in a different order, but the 12 cases were measured in a non-randomized fashion; therefore, a learning curve might have influenced results obtained for the first and last cases. However, as all packages scored high inter- and intra-observer agreements, this effect may be considered negligible. Finally, it is unknown how little changes in head posture during scanning might influence carotid arteries tortuosity indices. If found relevant, future studies should consider scanning the patients with their head in locked position to rule out confounding due to different head postures.

Conclusions

In summary, semi-automated carotid artery tortuosity measurements are reproducible and comparable between software packages. Although the reproducibility differed per software packages, all packages scored an acceptable inter-observer agreement. This suggests that the type of software package will not influence outcomes of tortuosity measurements in highly tortuous carotid arteries, and that all four software packages are valid for measuring TI.

Notes

Compliance with ethical standards

Funding

3mensio, TeraRecon, Vital Images Inc. and Aycan Medical Systems provided unrestricted research software licenses. The companies did not influence the realization of this manuscript, censor the manuscript prior to publication or require permission to publish study results.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants from our Carotid Aneurysm Registry included in the study.

References

  1. 1.
    Kim BJ, Yang E, Kim N et al (2016) Vascular tortuosity may be associated with cervical artery dissection. Stroke 47:2548–2552CrossRefGoogle Scholar
  2. 2.
    Saba L, Argiolas GM, Sumer S, Siotto P, Raz E, Sanfilippo R, Montisci R, Piga M, Wintermark M (2015) Association between internal carotid artery dissection and arterial tortuosity. Neuroradiology 57:149–153CrossRefGoogle Scholar
  3. 3.
    Giossi A, Mardighian D, Caria F, Poli L, de Giuli V, Costa P, Morotti A, Gamba M, Gilberti N, Ritelli M, Colombi M, Sessa M, Grassi M, Padovani A, Gasparotti R, Pezzini A (2017) Arterial tortuosity in patients with spontaneous cervical artery dissection. Neuroradiology 59:571–575CrossRefGoogle Scholar
  4. 4.
    Welleweerd JC, Nelissen BGL, Koole D, de Vries JPPM, Moll FL, Pasterkamp G, Vink A, de Borst GJ (2015) Histological analysis of extracranial carotid artery aneurysms. PLoS One 10:e0117915CrossRefGoogle Scholar
  5. 5.
    Pourier VEC, De Borst GJ (2016) Which carotid artery aneurysms need to be treated (and how)? J Cardiovasc Surg (Torino) 57:152–157 J Cardiovasc Surg 2016 April;57(2):152–7Google Scholar
  6. 6.
    Welleweerd JC, Moll FL, De Borst GJ (2012) Technical options for the treatment of extracranial carotid aneurysms. Expert Rev Cardiovasc Ther 10:925–931CrossRefGoogle Scholar
  7. 7.
    Morris SA (2015) Arterial tortuosity in genetic arteriopathies. Curr Opin Cardiol 30:587–593CrossRefGoogle Scholar
  8. 8.
    Wang H-F, Wang D-M, Wang J-J et al (2017) Extracranial internal carotid artery tortuosity and body mass index. Front Neurol 8:1–9Google Scholar
  9. 9.
    Virgilio F, Maurel B, Davis M, Hamilton G, Mastracci TM (2017) Vertebral tortuosity index in patients with non-connective tissue disorder- related aneurysm disease. Eur J Vasc Endovasc Surg 53:425–430CrossRefGoogle Scholar
  10. 10.
    Franken R, De Waard V, Timmermans J et al (2015) Increased aortic tortuosity indicates a more severe aortic phenotype in adults with Marfan syndrome. Int J Cardiol 194:7–12CrossRefGoogle Scholar
  11. 11.
    Morris SA, Orbach DB, Geva T, Singh MN, Gauvreau K, Lacro RV (2011) Increased vertebral artery tortuosity index is associated with adverse outcomes in children and young adults with connective tissue disorders. Circulation 124:388–396CrossRefGoogle Scholar
  12. 12.
    Boll DT, Lewin JS, Duerk JL, Smith D, Subramanyan K, Merkle EM (2004) Assessment of automatic vessel tracking techniques in preoperative planning of transluminal aortic stent graft implantation. J Comput Assist Tomogr 28:278–285CrossRefGoogle Scholar
  13. 13.
    Rengier F, Weber TF, Partovi S et al (2011) Reliability of semiautomatic centerline analysis versus manual aortic measurement techniques for TEVAR among non-experts. Eur J Vasc Endovasc Surg 43:324–331CrossRefGoogle Scholar
  14. 14.
    Müller-Eschner M, Rengier F, Partovi S, Weber TF, Kopp-Schneider A, Geisbüsch P, Kauczor HU, von Tengg-Kobligk H (2013) Accuracy and variability of semiautomatic centerline analysis versus manual aortic measurement techniques for TEVAR. Eur J Vasc Endovasc Surg 45:241–247CrossRefGoogle Scholar
  15. 15.
    Welleweerd J, Bots M, Kappelle L et al (2015) Rationale and design of the extracranial carotid artery aneurysm registry. J Cardiovasc Surg 56:1–27Google Scholar
  16. 16.
    Velu F, Jebbink EG, De VJPM et al (2017) Validation of the Simbionix PROcedure Rehearsal Studio sizing module : a comparison of software for endovascular aneurysm repair sizing and planning. Vascular 25:80–85CrossRefGoogle Scholar
  17. 17.
    Velu F, Jebbink EG, De VJPM et al (2017) A phantom study for the comparison of different brands of computed tomography scanners and software packages for endovascular aneurysm repair sizing and planning. Vascular 0:1–5Google Scholar
  18. 18.
    Choudhry FA, Grantham JT, Rai AT, Hogg JP (2016) Vascular geometry of the extracranial carotid arteries : an analysis of length, diameter, and tortuosity. J Neurointerv Surg 8:536–540CrossRefGoogle Scholar
  19. 19.
    Borst J, Marquering HA, Kappelhof M, Zadi T, van Dijk AC, Nederkoorn PJ, van den Berg R, van der Lugt A, Majoie CBLM (2015) Diagnostic accuracy of 4 commercially available semiautomatic packages for carotid artery stenosis measurement on CTA. Am J Neuroradiol 36:1978–1987CrossRefGoogle Scholar
  20. 20.
    Entezari P, Kino A, Honarmand AR et al (2013) Analysis of the thoracic aorta using a semi-automated post processing tool. Eur J Radiol 82:1558–1564CrossRefGoogle Scholar
  21. 21.
    Lee WA (2007) Endovascular abdominal aortic aneurysm sizing and case planning using the TeraRecon aquarius workstation. Vasc Endovasc Surg 41:61–67CrossRefGoogle Scholar
  22. 22.
    Baumueller S, Nguyen TDL, Goetti RP, Lachat M, Seifert B, Pfammatter T, Frauenfelder T (2011) Maximum diameter measurements of aortic aneurysms on axial CT images after endovascular aneurysm repair: sufficient for follow-up? Cardiovasc Intervent Radiol 34:1182–1189CrossRefGoogle Scholar
  23. 23.
    van Prehn J, van der Wal MBA, Vincken K, Bartels LW, Moll FL, van Herwaarden JA (2008) Intra- and interobserver variability of aortic aneurysm volume measurement with fast CTA postprocessing software. J Endovasc Ther 15:504–510CrossRefGoogle Scholar
  24. 24.
    Corriere MA, Islam A, Craven TE, Conlee TD, Hurie JB, Edwards MS (2014) Influence of computed tomography angiography reconstruction software on anatomic measurements and endograft component selection for endovascular abdominal aortic aneurysm repair. J Vasc Surg 59:1224–1231.e3CrossRefGoogle Scholar
  25. 25.
    Reynisson PJ, Scali M, Smistad E et al (2015) Airway segmentation and centerline extraction from thoracic CT - comparison of a new method to state of the art commercialized methods. PLoS One 10:1–20CrossRefGoogle Scholar
  26. 26.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  27. 27.
    Welleweerd JC, Den Ruijter HM, Nelissen BGL et al (2015) Management of extracranial carotid artery aneurysm. Eur J Vasc Endovasc Surg 50:141–147CrossRefGoogle Scholar
  28. 28.
    Shirali AS, Bischoff MS, Lin HM, Oyfe I, Lookstein R, Griepp RB, di Luozzo G (2013) Predicting the risk for acute type B aortic dissection in hypertensive patients using anatomic variables. JACC Cardiovasc Imaging 6:349–357CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Vascular SurgeryUniversity Medical Center UtrechtUtrechtthe Netherlands
  2. 2.Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands

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