Deep learning analysis of vessel reduction images after EVAR

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

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

Diagnosis and Treatment of Abdominal and Thoracic Aortic Aneurysms Including the Ascending Aorta and the Aortic Arch

This book considers diagnosis and treatment of abdominal and thoracic aortic aneurysms. It addresses vascular and cardiothoracic surgeons and interventional radiologists, but also anyone engaged in vascular medicine. The book focuses amongst other things on operations in the ascending aorta and the aortic arch. Surgical procedures in this area have received increasing attention in the last few years and have been subjected to several modifications. Especially the development of interventional radiological endovascular techniques that reduce the invasive nature of surgery as well as complication rates led to rapid advancements. Thoracoabdominal aortic aneurysm (TAAA) repair still remains a challenging operation since it necessitates extended exposure of the aorta and reimplantation of the vital aortic branches. Among possible postoperative complications, spinal cord injury (SCI) seems one of the most formidable morbidities. Strategies for TAAA repair and the best and most reasonable approach to prevent SCI after TAAA repair are presented

Design of a comprehensive modeling, characterization, rupture risk assessment and visualization pipeline for Abdominal Aortic Aneurysms

Abdominal aortic aneurysms (AAA) is a dilation of the abdominal aorta, typically within the infra-renal segment of the vessel that cause an expansion of at least 1.5 times the normal vessel diameter. It is becoming a leading cause of death in the United States and around the world, and consequentially, in 2009, the Society for Vascular Surgery (SVS) practice guidelines expressed the critical need to further investigate the factors associated with the risk of AAA rupture, along with potential treatment methods. For decades, the maximum diameter (Dmax) was introduced as the main parameter used to assess AAA behavior and its rupture risk. However, it has been shown that three main categories of parameters including geometrical indices, such as the maximum transverse diameter, biomechanical parameters, such as material properties, and historical clinical parameters, such as age, gender, hereditary history and life-style affect AAA and its rupture risk. Therefore, despite all efforts that have been undertaken to study the relationship among different parameters affecting AAA and its rupture, there are still limitations that require further investigation and modeling; the challenges associated with the traditional, clinical quality images represent one class of these limitations. The other limitation is the use of the homogenous hyper-elastic material property model to study the entire AAA, when, in fact, there is evidence that different degrees of degradation of the elastin and collagen network of the AAA wall lead to different regions of the AAA exhibiting different material properties, which, in turn, affect its biomechanical behavior and rupture. Moreover, the effects of all three main categories of parameters need to be considered simultaneously and collectively when studying the AAAs and their rupture, so once again, the field can further benefit from such studies. Therefore, in this work, we describe a comprehensive pipeline consisting of three main components to overcome some of these existing limitations. The first component of the proposed method focuses on the reconstruction and analysis of both synthetic and human subject-specific 3D models of AAA, accompanied by a full geometric parameter analysis and their effects on wall stress and peak wall stress. The second component investigates the effect of various biomechanical parameters, specifically the use of various homogeneous and heterogeneous material properties to model the behavior of the AAA wall. To this extent, we introduce two different patient-specific regional material property models to better mimic the physiological behavior of the AAA wall. Finally, the third component utilizes machine learning methods to develop a comprehensive predictive model that incorporates the effect of the geometrical, biomechanical and historical clinical data to predict the rupture severity of AAA in a patient-specific manner. This is the first comprehensive semi-automated method developed for the assessment of AAA. Our findings illustrate that using a regional material property model that mimics the realistic heterogeneity of the vessel’s wall leads to more reliable and accurate predictions of AAA severity and associated rupture risk. Additionally, our results indicate that using only Dmax as an indicator for the rupture risk is insufficient, while a combination of parameters from different sources along with PWS could serve as a more reliable rupture assessment. These methods can help better characterize the severity of AAAs, better predict their associated rupture risk, and, in turn, help clinicians with earlier, patient-customized diagnosis and patient-customized treatment planning approaches, such as stent grafting

In vivo quantification of metabolic activity in aortic aneurysms using PET

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-γ 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

Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms

Optical coherence tomography images of human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall. These disorders can be employed as indicators for wall degradation and, therefore, become a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. Two approaches are followed to evaluate this risk: the analysis of the reflectivity decay along the penetration depth and the textural analysis of a two-dimensional spatial distribution of the aortic wall backscattering. Both techniques require preprocessing stages for the identification of the air–sample interface and for the segmentation of the media layer. Results show that the alterations in the media layer of the aortic wall are better highlighted when the textural approach is considered and also agree with a semiquantitative histopathological grading that assesses the degree of wall degradation. The correlation of the co-occurrence matrix attains a sensitivity of 0.906 and specificity of 0.864 when aneurysm automatic diagnosis is evaluated with a receiver operating characteristic curve

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

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

Desarrollo y validación de una herramienta de segmentación automática del trombo para el seguimiento postoperatorio de los aneurismas de aorta abdominal tratados de forma endovascular.

118 p.El aneurisma de aorta abdominal (AAA) es una dilatación focal de la aorta que puede provocar su ruptura. El tratamiento habitual es la reparación endovascular (EVAR), que conlleva un seguimiento postoperatorio de por vida en base a imágenes de angiografía por tomografía computarizada (ATC) para detectar posibles complicaciones. La evaluación de los ATC realizados en el seguimiento consiste en la identificación visual de posibles fugas y en la medición del diámetro máximo del saco aneurismático.El volumen es un indicador mucho más sensible que el diámetro en detectar cambios en la morfología del aneurisma, pero no se emplea en la práctica clínica ni es recomendado en las guías clínicas de manejo de los AAA dado que no existe una técnica rápida, precisa y reproducible que aísle el trombo de los AAA del resto de estructuras anatómicas y permita evaluar el volumen aórtico.El término segmentación se emplea para hacer referencia a esa tarea de aislar las partes de una imagen que pertenecen a un mismo objeto, en nuestro caso, el trombo de los AAA. La segmentación de los AAA en las imágenes de ATC es un reto.Los algoritmos basados en Deep Learning han impulsado el análisis de la imagen médica con resultados sin precedentes en tareas complejas como la segmentación. La mayor parte del progreso en la segmentación de la imagen médica se ha llevado a cabo bajo un esquema supervisado, basado en las redes neuronales convolucionales (Convolutional Neural Networks, CNNs). Los métodos supervisados aprenden directamente de muestras de entrenamiento, extrayendo las características y la información de contexto.Basados en CNNs, se ha desarrollado una herramienta que es capaz de aislar de forma automática el trombo de los AAA tras el implante de la endoprótesis y así, poder extraer el volumen no luminal durante el seguimiento postoperatorio de EVAR.Se ha validado la herramienta de segmentación automática comparándola con las ediciones manuales del trombo realizadas por dos expertos en una serie de 44 pacientes con AAA tratados mediante EVAR con al menos dos años de seguimiento, de los cuales 27 presentaron buena evolución y 17 requirieron reintervención. De cada uno de los pacientes se utilizaron dos ATCs: el primero realizado dentro del primer mes postoperatorio y el segundo al cabo del primer año de seguimiento. Se extrajo el diámetro máximo (mm) y el volumen (mm3) tanto de las segmentaciones automáticas como de las editadas por cada uno de los dos expertos.El algoritmo de segmentación automático de trombo basado en redes neuronales convolucionales ofrece muy buenos resultados, siendo la aproximación obtenida con la herramienta de segmentación automática de trombo equivalente a la obtenida con la segmentación editad

Molecular imaging of abdominal aortic aneurysms

Abdominal aortic aneurysm (AAA) disease is characterised by an asymptomatic, permanent, focal dilatation of the abdominal aorta progressing towards rupture, which confers significant mortality. Patient management and surgical decisions currently rely on aortic diameter measurements via abdominal ultrasound screening. However, AAA rupture can occur at small diameters or may never occur at large diameters. Therefore, there is a need to develop molecular imaging-based biomarkers independent of aneurysm diameter that may help stratify patients with early-stage AAA to reduced surveillance. AAA uptake of [18F]fluorodeoxyglucose on positron emission tomography (PET) has been demonstrated previously; however, its glucose-dependent uptake may overlook other key mechanisms. The cell proliferation marker [18F]fluorothymidine ([18F]FLT) is primarily used in tumour imaging. The aim of the overall study for this thesis was to explore the feasibility of [18F]FLT PET / computed tomography (CT) to visualise and quantify AAA in the angiotensin II (AngII)-infused mouse model. The experiments presented in this thesis revealed increased uptake of [18F]FLT in the 14-day AngII AAA model than in saline controls, followed by a decrease in this uptake at 28 days. Moreover, in line with the in vivo PET/CT findings, Western blotting of aortic tissue revealed increased levels of thymidine kinase-1 (the substrate of [18F]FLT) and nucleoside transporters in the 14-day AngII AAA model than in saline controls, followed by decreased expression levels at 28 days. A pilot experiment further demonstrated that [18F]FLT PET/CT could be used to detect an early therapeutic response to oral imatinib treatment in the AngII AAA model. Therefore, [18F]FLT PET/CT may be a feasible modality to detect and quantify cell proliferation in the AngII AAA murine model. The findings of this thesis are encouraging for the application of [18F]FLT PET/CT in patients with small AAA