105 research outputs found

    Deep learning analysis of vessel reduction images after EVAR

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    Máster Universitario en Deep Learning for Audio and Video Signal ProcessingAn aortic aneurysm is an enlargement of the aorta, the largest artery supplying blood to the body. The most common aneurysms are abdominal aortic aneurysms (AAA). AAAs tend to grow and rupture, resulting in a high risk of death from internal bleeding. The most commonly used surgical treatment today is the placement of an aortic stent graft, EVAR (EndoVascular Aneurysm Repair). EVAR is a self-expanding device that is placed inside the diseased artery to exclude the aneurysm from circulation, reducing the pressure on the aneurysm and eliminating the risk of AAA rupture. The best prognostic sign of this treatment is the reduction in size of the AAA over time, once depressurization has occurred. An AAA that is correctly treated with EVAR and excluded from circulation is called an aneurysmal sac. However, EVAR is not free of complications, the most frequent are the so-called leaks, which are small inflows of blood into the treated AAA that condition the pressurization of the aneurysmal sac and therefore the reappearance of the risk of rupture and bleeding, which is always accompanied by a lack of reduction in the size of the sac. There are different types of leaks depending on where blood enters the sac. Early detection is essential to plan appropriate treatment in time. The current diagnostic test to follow up EVARs is CT (Computed Tomography). The images obtained with this technique are studied by physicians looking for contrast spots within the treated AAA that indicate the presence of leaks. These contrast peaks may be evident in some cases, but difficult to see in others, especially considering the large volume of images per CT scan. The model proposed in this study consists of a detection network, based on RetinaNet, to localize the sac in the CT images and remove the surrounding noise. Then using a binary classification model based on convolutional networks, both 2D and 3D, to analyze the images and make a prediction of the evolution of the aneurysm size, which would allow physicians to perform targeted surveillance on patients at higher risk of leaking

    Biomechanical and morphological aspects of abdominal aortic aneurysm growth and rupture

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    Abdominal aortic aneurysms (AAAs) are dilatations of the abdominal aorta that pose a risk of rupture. The only effective treatment is intervention prior to rupture, but this is also associated with mortality and morbidity. It is therefore important to weigh the risks of intervention with the potential benefit. Current treatment guidelines recommend using the maximal aneurysm diameter (Dmax) as the indicator for rupture risk, and rec- ommend considering intervention in men with AAAs > 55 mm, and >50 mm in women. Patients with small AAAs are put in surveillance, and the Dmax is followed until it reaches the threshold. The current policy is relatively efficient on a population-level but lacks specificity for individuals. Some patients rupture before this threshold, and many remain stable despite passing it. Aneurysm growth is often described as erratic, but measure- ments are affected by several levels of uncertainty. Biomechanical assessment, where 3D models of AAAs from computed tomography angiographies (CTAs) are analysed by finite element analysis, may improve risk prediction. In the first study a population-based cohort of 192 patients with ruptured AAAs and CT imaging available at rupture were studied. A significant portion of patients ruptured with AAAs smaller than 60 mm, 10% of men and 27 % of women. When normalizing Dmax for body surface area (so-called aortic size index) there was, however, was not difference between the sexes. In an analysis of small, ruptured AAAs compared to Dmax, age and sex-matched asymptomatic AAAs, peak wall rupture index (PWRI), but not peak wall stress (PWS) was increased in the ruptured AAAs. In the second study, a cohort of 100 patients with at least three computed tomog- raphy examinations were analysed with 3D morphological and biomechanical analysis. The growth pattern of AAAs appeared continuous and conferred well to a linear growth model. The evolution of the different analysed indices, Dmax, aneurysm volume and bio- mechanical stress did, however, not parallel each other. Intraluminal thrombus (ILT) grew faster than the lumen, but lumen volume growth was more closely related to increase in biomechanical stress. In the third study, a cohort of 67 patients with 109 CTA examinations prior to rupture were identified. The relation between biomechanical variables and time-to-rupture was investigated. In small and medium sized AAAs (< 70 mm), PWRI, but not PWS, was associ- ated with time-to-rupture, also when adjusting for potential confounders, aneurysm size and sex. The results further show that women have an approximately two-fold increased hazard ratio for AAA rupture, compared to men, when adjusted for AAA size. In the fourth study lumen area is indicated as a potentially useful rupture risk marker. Ruptured AAAs, compared to Dmax-matched asymptomatic AAAs, have a larger luminal area, and the luminal area is related to biomechanical stress, even when adjusting for an- eurysm size, or ILT area. In conclusion, the results of this thesis indicate areas of potential improvement in the current care of patients with AAAs, explores the 3D growth of AAAs, and strengthens the potential role for biomechanical analysis. These results may in the future have rele- vance for personalizing timing of treatment for patients with AAAs, and the evaluation of pharmacological therapy for AAAs

    Advanced computer modeling of abdominal aortic aneurysms to predict risk of rupture

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    An abdominal aortic aneurysm (AAA) is an abnormal enlargement of the aorta which is related to weakness of the vessel wall (associated with degradation of connective tissue), and if left untreated will lead to rupture and cause death in 78% to 94% of cases. Approximately 7,000 deaths each year in the United Kingdom are caused by AAA rupture.AAA repair requires surgical intervention but the surgery itself has a mortality rate of about 5% in patients with stable AAA. The decision to undertake the surgery is made depending on the aortic maximum diameter of ≥5 cm. However, it is observed that rupture sometimes occurs in aneurysms with smaller diameter, thereby creating the need for better criteria for surgical intervention. Therefore, (biomechanical) indicators of AAA rupture were introduced as a superior criterion to the maximum diameter for predicting the risk AAA rupture. Several studies that have been conducted on abdominal aortic aneurysms have suggested that peak wall stress may be a more reliable predictor of the risk of AAA rupture.This thesis is a continuation of a previous study undertaken at the University of Hull which investigated a number of biomechanical factors that affect the AAA wall stress magnitude and distribution. Novel results were gained which may help in the understanding of AAA growth and rupture events. For the first time, it is proposed that aspect ratio has an effect on the stress magnitude, location and distribution of the outer wall of AAA. These findings were used to introduce an empirical relationship between the aneurysm aspect ratio and maximum wall stress. This empirical relationship could be used as an additional clinical indicator to predict the location and magnitude of maximum wall stress where a rupture may develop.Analysis of the porosity of the thrombus was introduced for the first time in this work using the simulation of mass transport of blood flow in an AAA, showing novel results for the possible role of blood flow on the site of growth and rupture for AAA. Furthermore, the results of this research may also explain the conflicting views on aneurysm shape and the role of the thrombus as previously reported in the literature.The work carried out in this research used simplified AAA geometries to allow the isolation of specific aneurysm parameters. Clearly, the next stage would include the application of the ideas and results developed here to more complex patient-specific geometries

    In vivo quantification of metabolic activity in aortic aneurysms using PET

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    Objective: To investigate the role of hybrid 18F-FDG PET/CT as a potential risk-stratification tool of aneurysm expansion by measuring metabolic activity on PET and textural analysis on CT in abdominal aortic aneurysm (AAA). Histological markers of AAA wall inflammatory cell infiltrate and enzymatic degradation have been associated with increased 18F-Fluorodeoxyglucose (18F-FDG)-Positron Emission Tomography /Computed Tomography (PET/CT) uptake. Methods: Fifty patients with asymptomatic infrarenal AAA enrolled under surveillance at one of our institutions underwent 18F-FDG-PET/CT. Seventeen subjects were investigated for increased glucolysis in the AAA wall and optimal circulation imaging time for 18F-FDG. In 25 subjects the relationship between aneurysm metabolic activity and expansion was explored. Forty subjects had AAA CT textural analysis (CTTA) parameters performed on the CT component of PET/CT and were studied in relation to aneurysm expansion. Twenty-four subjects had circulating biomarkers analysed. Whole vessel assessment, region of interest analysis and the role of correcting for background blood pool activity were explored. Results: Thirteen of seventeen subjects investigated for increased 18F-FDG uptake had an AAA wall SUVmax > 2.5. In 17 subjects assessed for optimal circulation imaging time for 18F-FDG, no significant advantage in imaging at 3h over 1h after 18F-FDG injection was observed. 18F-FDG uptake correlated inversely with future AAA expansion in the preliminary group of 25 patients and in 40 subjects who also had CTTA. In subjects who had CTTA, coarse texture showed an inverse association with 18F-FDG uptake and medium coarse texture correlated with future AAA expansion. In 24 AAA patients who had serum biomarker assays, significantly higher levels of high sensitivity matrix metalloproteinase-9 (hsMMP-9) and hsMMP-2 compared to healthy controls were found. There was no correlation between AAA 18F-FDG uptake and levels of hsMMP-9, hsMMP-2, hs-interferon-Îł and hs-C-reactive protein. Conclusions: In-vivo 18F-FDG PET/CT data indicated that small AAA show increased glucose metabolism. Relationships between AAA 18F-FDG uptake, CTTA and future expansion were identified. AAA18F-FDG PET/CT shows potential to identify subjects at risk of significant expansion. AAA metabolism may not relate to serum levels of certain inflammatory biomarkers

    Conceptual framework of a novel hybrid methodology between computational fluid dynamics and data mining techniques for medical dataset application

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    This thesis proposes a novel hybrid methodology that couples computational fluid dynamic (CFD) and data mining (DM) techniques that is applied to a multi-dimensional medical dataset in order to study potential disease development statistically. This approach allows an alternate solution for the present tedious and rigorous CFD methodology being currently adopted to study the influence of geometric parameters on hemodynamics in the human abdominal aortic aneurysm. This approach is seen as a “marriage” between medicine and computer domains

    Data Mining and Associated Analytical Tools as Decision Aids for Healthcare practitioners in Vascular Surgery

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    Vascular surgery is an increasingly data rich speciality. Planning treatment and assessing outcomes are highly dependent on objective assessment of number of imaging modalities including duplex ultrasound, CT scans and angiograms which are almost exclusively digitally created stored and accessed. Developments such as the national vascular registry mean that treatment outcomes are recorded scrutinised electronically. The widespread availability of data which is collected electronically and stored for future clinical use has created the opportunity to examine the efficacy of investigations and treatments in a way which has hitherto not been possible. In addition, new computational methods for data analysis have provided the opportunity for the clinicians and researchers to utilise this data to address pertinent clinical questions

    Aortic dissection: simulation tools for disease management and understanding

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    Aortic dissection is a severe cardiovascular pathology in which a tear in the intimal layer of the aortic wall allows blood to flow between the vessel wall layers, forming a 'false lumen'. In type-B aortic dissections, those involving only the descending aorta, the decision to medically manage or surgically intervene is not clear and is highly dependent on the patient. In addition to clinical imaging data, clinicians would benefit greatly from additional physiological data to inform their decision-making process. Computational fluid dynamics methods show promise for providing data on haemodynamic parameters in cardiovascular diseases, which cannot otherwise be predicted or safely measured. The assumptions made in the development of such models have a considerable impact on the accuracy of the results, and thus require careful investigation. Application of appropriate boundary conditions is a challenging but critical component of such models. In the present study, imaging data and invasive pressure measurements from a patient with a type-B aortic dissection were used to assist numerical modelling of the haemodynamics in a dissected aorta. A technique for tuning parameters for coupled Windkessel models was developed and evaluated. Two virtual treatments were modelled and analysed using the developed dynamic boundary conditions. Finally, the influence of wall motion was considered, of which the intimal flap that separates the false lumen from the true lumen, is of particular interest. The present results indicate that dynamic boundary conditions are necessary in order to achieve physiologically meaningful flows and pressures at the boundaries, and hence within the dissected aorta. Additionally, wall motion is of particular importance in the closed regions of the false lumen, wherein rigid wall simulations fail to capture the motion of the fluid due to the elasticity of the vessel wall and intimal flap
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