AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment:Status Quo

The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges

Prediction of Abdominal Aortic Aneurysm Growth by Automatic Segmentation and Radiomics Feature Quantification

An accurate assessment of abdominal aortic aneurysm (AAA) progression is essential to its clinical management. Currently, the maximum diameter of AAA at diagnosis is considered as the primary indicator of rupture risk. However, it is not optimal as rupture can happen at any size. Several patient-specific factors may also influence AAA rupture risk. Given the clinical variability in aneurysm progression, additional prognostic markers are desirable to enhance patient-specific risk stratification. Radiomics is an image processing technique that extracts quantitative and high-dimensional features from medical images. While it has emerged as a novel approach for solving diagnosis in oncology, its application in cardiovascular diseases is still limited. This study set out with an aim to determine the feasibility of radiomics in identifying AAA with a fast growth rate (>0.3cm/year) using CT images. An automatic AAA segmentation algorithm was developed in our pipeline. Based on the radiomics features of an 84 CT dataset, supervised classification models were implemented with two feature selection algorithms and two classifiers in a machine-learning framework. An AUC of 0.80 was achieved and the predictive power was proved through comparisons to the maximum diameter and conventional risk factors. Further multivariate analysis suggested that a radiomics-based classification model could be used as an independent, yet strong predictor for fast AAA growth rate

Quantification of periaortic fat tissue in contrast-enhanced computed tomography

Objective. Periaortic fat tissue (PaFT) has been implicated in the progression of abdominal aortic aneurysms (AAAs). Therefore, its quantification as a prognostic marker for aneurysm expansion has attracted clinical interest. Most existing research on PaFT, however, is based on unenhanced aortic CT scans, whereas the CT diagnosis of aortic aneurysms is usually performed with enhanced CT angiographies. The objective of this study is to examine the feasibility of measuring abdominal periaortic fat tissue in enhanced aortic CT-scans using a new method based on the OsirixMD post-processing software and evaluate any methodological issues/considerations arising from it, in order to reliably quantify periaortic fat tissue from enhanced and unenhanced CT-scans. Methods. In a derivation cohort (n= 101), PaFT Volume and PaFT mean HU value were measured within a 5 mm-wide periaortic ring in arterial phases and compared to the same values from native scans. Fat tissue was defined within the range of -45 to -195 Hounsfield Units (HU). After testing their correlation, fat tissue values from both CT phases underwent linear regression through the origin to define a correction factor (slope of the line of best fit), allowing the conversion of arterial back to native scores. This conversion factor was then applied to fat tissue values in a different validation cohort (n=47) and the agreement of the corrected fat tissue values and values in the native scans was examined using Bland-Altman plots and Passing-Bablok regression. In a secondary study the pooled date sets from both studies (n=148) were stratified in an AAA and non-AAA group and the average fat tissue values for both groups (with PaFT volumes adjusted for aortic size) were calculated using both native and corrected arterial values. Results. In the derivation cohort, periaortic fat tissue Volume and mean HU value showed very high correlations between arterial and native scans (r> .99 and r= .95 respectively, p< .0001 both). Linear regression defined a conversion factor of 1.1057 for arterial periaortic fat tissue Volume and 1.0011 for arterial periaortic fat tissue mean HU. Potential confounding factors (mean intraluminal contrast density, aortic wall calcification, longitudinal contrast dispersion, aortic diameter, CT-tube voltage, slice thickness, image noise) showed no significant impact in multivariate regression. Application of the conversion factors in arterial scans of the validation study resulted in corrected arterial fat tissue values that showed very good agreement with PaFT values in native scans. Bland Altman analysis showed the following mean differences [95% confidence interval]: 0.36 [-0.01 to 0.73] for periaortic fat tissue Volume and 0.83 [-1.08 to 0.1] for periaortic fat tissue mean HU. Passing-Bablok regression confirmed minimal/no residual bias. Median periaortic fat tissue size-adjusted PaFT Volumes and Mean HU values from the Mann-Whitney test showed no significant difference between the AAA and non-AAA groups. Conclusion. Periaortic fat tissue Volume and mean HU values demonstrate only minimal variation between arterial and native scans and can be measured in enhanced aortic CT scans with very high reliability. Periaortic fat tissue Mean HU value, unlike Volume, is independent from the presence of paraaortic organs. Certain issues, like non-circular aortic discs, histological boundaries of periortic fat tissue and dependence from Body Mass Index and other fat tissue depots need to be explored further.1. ZUSAMMENFASSUNG Ziel. Das periaortale Fettgewebe spielt bei der Progression von Aortenaneurysmen eine Rolle, so dass seine Quantifizierung als prognostischer Marker für die Aneurysmaprogression von besonderem klinischem Interesse ist. Die aktuelle Forschung ist basiert jedoch fast ausschließlich auf nativen CTs, während Aortenaneurysmen üblicherweise nur mittels kontrastmittelverstärkten CT angiographien dargestellt werden. Das Ziel dieser Studie ist die methodische Überprüfung der Bestimmung vom abdominalen periaortalen Fettgewebe in kontrastmittelverstärkten CTs mit der frei verfügbaren OsirixMD Softwareanwendung und die Evaluation von potenziellen Faktoren, die eine zuverlässige periaortale Fettgewebsquantifikation in nativen und kontrastverstärkten CTs ermöglichen. Methodik. In einer Derivationsgruppe (n=101), wurde das Fettgewebsvolumen und die HU Mittelwerte innerhalb von einem 5 mm breiten periaortalen Ring in arteriellen CTs bestimmt und die Werte wurden mit entsprechenden Werten aus nativen CTs verglichen. Das Fettgewebe wurde als HU Werte -45 bis -195 HU definiert. Die Fettgewebswerte von beiden CT-Phasen wurden auf Korrelation überprüft und anschließend einer linearen Regressionsanalyse unterzogen, wobei ein Konversionsfaktor bestimmt wurde, um arterielle in nativen Fettgewebswerten zu konvertieren. Der Konversionsfaktor wurde danach in einer zweiten Validierungsgruppe (n=47) angewendet. Sodann wurde die Übereinstimmung von korrigierten arteriellen und nativen Fettgewebswerten mittels Bland-Altmann Plots und Passing-Bablok Regressionsanalyse überprüft. In einer Sekundärstudie, wurden die gepoolten Datasets beider Studien (n=148) in einer Bauchaortenaneurysma- und einer Nichtbauchaortenanerysmagruppe stratifiziert, um die Mittelwerte von Fettgewebsvolumen (adjustiert für Aortengröße) und HU Mittelwert in beiden Gruppen zu bestimmen. Ergebnisse. In der Derivationsgruppe, zeigte das Fettgewebsvolumen und der HU Mittelwert eine sehr hohe Korrelation zwischen kontrastverstärkten und nativen CTs (r > 0,99 und r= 0,95 entsprechend, p< 0,0001 für beide). Die lineare Regressionsanalyse ergab einen Konversionsfaktor von 1,1057 für das Fettgewebsvolumen und 1,0011 für den Fettgewebs-HU Mittelwert. Potenzielle Störfaktoren (intraluminale Kontrastmitteldichte, Aortenwandkalzifikation, longitudinale Kontrastmittelverteilung, Aortendiameter, CT-Röhrenspannung, Slicestärke, Größe der intraluminalen Kontrast-ROI, Bildrauschen) zeigten keinen signifikanten Einfluss in der multiplen Regressionsanalyse. In der Validierungsgruppe, zeigten die mittels Konversionsfaktor korrigierten Fettgewebswerte der arteriellen Phase eine sehr hohe Übereinstimmung mit den Fettgewebswerten der nativen CT-Phase. Die Bland-Altman Analyse ergab folgende mittlere Differenzen [95% Konfidenzintervall]: 0,36 [- 0,01 bis 0,73] fürs Volumen und 0,83 [-1,08 bis 0,1] für den HU Mittelwert. Die Passing-Bablok Regressionsanalyse bestätigte ein minimales bzw. kein residuales Bias. In der Sekundärstudie, zeigten die Mediane der Fettgewebswerte aus dem Mann-Whitney Test keinen signifikanten Unterschied zwischen der BAA und nicht-BAA Gruppe. Schlussfolgerung. Periaortales Fettgewebsvolumen und HU-Mittelwert zeigen eine minimale Variation zwischen arteriellen und nativen CTs und lassen sich in kontrastverstärkten Aorten-CTs sehr zuverlässig bestimmen. Der Fettgewebsmittelwert ist von der Präsenz anderer paraaortale Organe unabhängig. Gewisse Faktoren, z.B. nicht-zirkuläre aortalen Scheiben, histologische Grenzen des periaortalen Fettgewebes und seine Abhängigkeit vom Body Mass Index und anderen Fettgewebskompartimenten benötigen eine weitere Analyse

Motion Calculations on Stent Grafts in AAA

Endovascular aortic repair (EVAR) is a technique which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. Although this technique has been shown to be very successful on the short term, the long term results are less optimistic due to failure of the stent graft. The pulsating blood flow applies stresses and forces to the stent graft, which can cause problems such as breakage, leakage, and migration. Therefore it is of importance to gain more insight into the in vivo motion behavior of these devices. If we know more about the motion patterns in well-behaved stent graft as well as ill-behaving devices, we shall be better able to distinguish between these type of behaviors These insights will enable us to detect stent-related problems and might even be used to predict problems beforehand. Further, these insights will help in designing the next generation stent grafts. Firstly, this work discusses the applicability of ECG-gated CT for measuring the motions of stent grafts in AAA. Secondly, multiple methods to segment the stent graft from these data are discussed. Thirdly, this work proposes a method that uses image registration to apply motion to the segmented stent mode

Precise Tracking and Initial Segmentation of Abdominal Aortic Aneurysm

[[abstract]]In this paper we propose a mean-shift based technique for a precise tracking and segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) angiography images. The proposed method applies median filter on the gradient of ray-length and linear interpolation for denoising. The segmentation result can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important for selection of appropriate stent graft device for treatment of AAA. Comparing to conventional approaches, our method is very efficient and can save a lot of manual labors.[[conferencetype]]國際[[conferencedate]]20131102~20131104[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Aizu-Wakamatsu, Japa

Numerical Insights for AAA Growth Understanding and Predicting: Morphological and Hemodynamic Risk Assessment Features and Transient Coherent Structures Uncovering

Les anévrismes de l'aorte abdominale (AAA) sont des dilatations localisées et fréquentes de l'aorte. En cas de rupture, seul un traitement immédiat peut prévenir la morbidité et la mortalité. Le diamètre maximal AAA ($D_{max}$) et la croissance sont les paramètres actuels pour évaluer le risque associé et planifier l'intervention, avec des seuils inférieurs pour les femmes. Cependant, ces critères ne sont pas personnalisés ; la rupture peut se produire à un diamètre inférieur et les patients vivre avec un AAA important. Si l'on sait que la maladie est associée à une modification de la morphologie et de la circulation sanguine, à un dépôt de thrombus intra-luminal et à des symptômes cliniques, les mécanismes de croissance ne sont pas encore entièrement compris. Dans cette étude longitudinale, une analyse morphologique et des simulations de flux sanguins sont effectuées et comparées aux sujets témoins chez 32 patients ayant reçu un diagnostic clinique d'AAA et au moins 3 tomodensitogrammes de suivi par patient. L'objectif est d'abord d'examiner quels paramètres stratifient les patients entre les groupes sains, à faible risque et à risque élevé. Les corrélations locales entre les paramètres hémodynamiques et la croissance de l'AAA sont également explorées, car la croissance hétérogène de l'AAA n'est actuellement pas comprise. Enfin, les paramètres composites sont construits à partir de données cliniques, morphologiques et hémodynamiques et de leur capacité à prédire si un patient sera soumis à un test de risque. La performance de ces modèles construits à partir de l'apprentissage supervisé est évaluée par les ROC AUC : ils sont respectivement de 0.73 ± 0.09, 0.93 ± 0.08 et 0.96 ± 0.10 . En incorporant tous les paramètres, on obtient une AUC de 0.98 ± 0.06. Pour mieux comprendre les interactions entre la croissance et la topologie de l'écoulement de l'AAA, on propose un worflow spécifique au patient pour calculer les exposants de Lyapunov en temps fini et extraire les structures lagrangiennes-cohérentes (SLC). Ce modèle de calcul a d'abord été comparé à l'imagerie par résonance magnétique (IRM) par contraste de phase 4-D chez 5 patients. Pour mieux comprendre l'impact de la topologie de l'écoulement et du transport sur la croissance de l'AAA, des SLC hyperboliques répulsives ont été calculées chez un patient au cours d'un suivi de 8 ans, avec 9 mesures morphologiques volumétriques de l'AAA par tomographie-angiographie. Les SLC ont défini les frontières du jet entrant dans l'AAA. Les domaines situés entre le SLC et le mur aortique ont été considérés comme des zones de stagnation. Leur évolution a été étudiée lors de la croissance de l'AAA. En plus des SLC hyperboliques (variétés attractives et répulsives) découvertes par FTLE, les SLC elliptiques ont également été considérées. Il s'agit de régions dominées par la rotation, ou tourbillons, qui sont de puissants outils pour comprendre les phénomènes de transport dans les AAA.Abdominal aortic aneurysms (AAA) are localized, commonly-occurring dilations of the aorta. In the event of rupture only immediate treatment can prevent morbidity and mortality. The AAA maximal diameter ($D_{max}$) and growth are the current metrics to evaluate the associated risk and plan intervention, with lower thresholds for women. However, these criteria lack patient specificity; rupture may occur at lower diameter and patients may live with large AAA. If the disease is known to be associated with altered morphology and blood flow, intra-luminal thrombus deposit and clinical symptoms, the growth mechanisms are yet to be fully understood. In this longitudinal study, morphological analysis and blood flow simulations for 32 patients with clinically diagnosed AAA and at least 3 follow-up CT-scans per patient, are performed and compared to control subjects. The aim is first to investigate which metrics stratify patients between healthy, low risk and high risk groups. Local correlations between hemodynamical metrics and AAA growth are also explored, as AAA heterogeneous growth is currently not understood. Finally, composite metrics are built from clinical, morphological, and hemodynamical data, and their ability to predict if a patient will become at risk tested. Performance of these models built from supervised learning is assessed by ROC AUCs: they are respectively, 0.73 ± 0.09, 0.93 ± 0.08 and 0.96 ± 0.10. Mixing all metrics, an AUC of 0.98 ± 0.06 is obtained. For further insights into AAA flow topology/growth interaction, a workout of patient-specific computational flow dynamics (CFD) is proposed to compute finite-time Lyapunov exponents and extract Lagrangian-coherent structures (LCS). This computational model was first compared with 4-D phase-contrast magnetic resonance imaging (MRI) on 5 patients. To better understand the impact of flow topology and transport on AAA growth, hyperbolic, repelling LCS were computed in 1 patient during 8-years follow-up, including 9 volumetric morphologic AAA measures by computed tomography-angiography (CTA). LCS defined barriers to Lagrangian jet cores entering AAA. Domains enclosed between LCS and the aortic wall were considered to be stagnation zones. Their evolution was studied during AAA growth. In addition to hyperbolic (attracting and repelling) LCS uncovered by FTLE, elliptic LCS were also considered. Those encloses rotation-dominated regions, or vortices, which are powerful tools to understand the flow transport in AAA