Optimization of CT scanning protocol of Type B aortic dissection follow-up through 3D printed model

This research aims to develop and evaluate a human tissue-like material 3D printed model used as a phantom in determining optimized scanning parameters to reduce the radiation dose for Type B aortic dissection patients after thoracic endovascular aortic repair. The results show that radiation risk for follow-up Type B aortic dissection patients can be potentially reduced. Further, the value of using 3D printed model in studying CT scanning protocols was further validated

Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification

Aortic dissection (AD) is a life-threatening cardiovascular disease with a high mortality rate. The accurate and generalized 3-D reconstruction of AD from CT-angiography can effectively assist clinical procedures and surgery plans, however, is clinically unavaliable due to the lacking of efficient tools. In this study, we presented a novel multi-stage segmentation framework for type B AD to extract true lumen (TL), false lumen (FL) and all branches (BR) as different classes. Two cascaded neural networks were used to segment the aortic trunk and branches and to separate the dual lumen, respectively. An aortic straightening method was designed based on the prior vascular anatomy of AD, simplifying the curved aortic shape before the second network. The straightening-based method achieved the mean Dice scores of 0.96, 0.95 and 0.89 for TL, FL, and BR on a multi-center dataset involving 120 patients, outperforming the end-to-end multi-class methods and the multi-stage methods without straightening on the dual-lumen segmentation, even using different network architectures. Both the global volumetric features of the aorta and the local characteristics of the primary tear could be better identified and quantified based on the straightening. Comparing to previous deep learning methods dealing with AD segmentations, the proposed framework presented advantages in segmentation accuracy

Vascular remodeling after endovascular treatment: quantitative analysis of medical images with a focus on aorta

In the last years, the convergence of advanced imaging techniques and endovascular procedures has revolutionized the practice of vascular surgery. However, regardless the anatomical district, several complications still occur after endovascular treatment and the impact of endovascular repair on vessel morphology remains unclear. Starting from this background, the aim of this thesis is to ll the gaps in the eld of vessel remodeling after endovascular procedure. Main focus of the work will be the repair of the aorta and, in particular thoracic and thoracoabdominal treatments. Furthermore an investigation of the impact of endovascular repair on femoro-popliteal arterial segment will be reported in the present work. Analyses of medical images will been conducted to extract anatomical geometric features and to compare the changes in morphology before treatment and during follow-up. After illustrating in detail the aims and the outline of the dissertation in Chapter 1, Chapter 2 will concern the anatomy and the physiology of the aorta along with the main aortic pathologies and the related surgical treatments. Subsequently, an overview of the medical image techniques for segmentation and vessel geometric quantication will be provided. Chapter 3 will introduce the concept of remodeling of the aorta after endovascular procedure. In particular, two types of aortic remodeling will be considered. On one side remodeling can be seen as the shrinkage of the aneurysmal sac or false lumen thrombosis. On the other side, aortic remodeling could be seen as the changes in the aortic morphology following endograft placement which could lead to complications. Chapter 4 will illustrate a study regarding the analysis of medical images to measure the geometrical changes in the pathological aorta during follow-up in patients with thoracoabdominal aortic aneurysms treated with endovascular procedure using a novel uncovered device, the Cardiatis Multilayer Flow Modulator. Chapter 5 will focus on the geometrical remodeling of the aortic arch and descending aorta in patients who underwent hybrid arch treatment to treat thoracic aneurysms. The goal of the work is to develop a pipeline for the processing of pre-operative and post-operative Computed Tomography images in order to detect the changes in the aortic arch physiological curvature due to endograft insertion. Chapter 6 will focuse on the use of 3D printing technology as valuable tool to support patient's follow-up. In particular, we report a case of a patient originally treated with endovascular procedure for type B aortic dissection and which experimented several complications during follow-up. 3D printing technology is used to show the remodeling of the aortic vasculature during time. Chapter 7 will concern patient-specic nite element simulations of aortic endovascular procedure. In particular, starting from a clinical case where complication developed during followup, the predictive value of computational simulations will be shown. Chapter 8 will illustrate a study concerning the evaluation of morphological changes of the femoro-popliteal arterial segment due to limb exion in patients undergoing endovascular treatment of popliteal artery aneurysms

True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart

Design of a testing device for an anatomical part of the ascending aorta

Aortic aneurysms are life-threatening pathologies that cause thousands of deaths worldwide. The current main clinical criteria for surgical intervention is aortic diameter, although a large percentage of patients with dissection or rupture has a normal diameter. Computation methods have been adopted to model the biomechanical behaviour of biological tissue in view of adding in the diagnosis of this pathology. Furthermore, experimental testing on aneurismatic aortic tissue has been performed to validate these models. The objective of this study is to integrate com- putational mechanical methods into an innovative experimental test with a specifically designed device where material parameters are obtained by inverse methods assisted by Digital Image Correlation (DIC). Axiomatic Design (AD) is taken into consideration to develop the testing device in a clear, methodical, and efficient way. A case study is analysed, and a patient-specific 3D geometry of an Ascending Thoracic Aortic Aneurysm (ATAA) is obtained by segmenting Computed Tomography Angiography (CTA) images. A methodology is presented by attribut- ing a hyperelastic constitutive model to the geometry and executing Finite Element Analysis (FEA). Future work should rely on real experimental tests where Finite Element Model Up- dating (FEMU) should be adopted to fit the constitutive model more accurately to the actual specimen material.O aneurisma da aorta é uma patologia de risco que provoca milhares de mortes mundialmente. O critério atual para intervenção cirúrgica é o diâmetro da aorta, no entanto, uma grande percentagem de pacientes com dissecção ou rutura da aorta apresenta um diâmetro normal. Métodos computacionais têm sido adotados para modelar o comportamento biomecânico de tecido biológico e auxiliar no diagnóstico desta patologia. Testes experimentais nestes tecidos são executados para validar os modelos. O objetivo deste estudo é um contributo para uma plataforma digital integrando métodos computacionais para o desenvolvimento de um mecan- ismo de ensaio experimental, cuja identificação de parâmetros material deve ser auxiliada pela técnica de correlação digital de imagem 3D. Esta abordagem segue um desenvolvimento de pro- duto orientado por simulação numérica, em que a análise computacional é totalmente integrada como parte do projeto mecânico. Teoria Axiomática de Projeto é tida em consideração para desenvolver o dispositivo de uma forma clara, metódica e eficiente. Um caso de estudo é anal- isado e uma geometria da peça anatómica 3D, específica de um paciente, é obtida através da segmentação de imagens de uma angiotomografia. Uma metodologia é apresentada atribuindo um modelo constitutivo hiperelástico ao material e executando análise de elementos finitos. Como trabalho futuro a identificação dos parametros constitutivos deve ser obtida com recurso a métodos inversos avançados baseados em campos de deformação obtidos por correlação digital de imagem

The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes

Geometric, biomechanical and molecular analyses of abdominal aortic aneurysm

Background Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal aorta of 30 mm in diameter or more. Main risk factors are smoking, age and male sex. Pathophysiological features include inflammation, smooth muscle cell loss and destruction of the extracellular matrix. The AAA is typically asymptomatic but can expand and eventually rupture, with a mortality of 70-80% as a result. Risk factors for rupture include a large diameter, female sex, active smoking, high blood pressure and low body mass index (BMI). There is no medical treatment to inhibit growth or rupture of AAA. The only measure to prevent rupture in a large AAA is aortic surgery. This intervention carries its own significant risk of morbidity and mortality, necessitating a risk stratification method. The diameter is currently used to decide when to operate on an AAA and it is repeatedly monitored until the threshold for surgery is reached. However, this measurement leaves room for improvement, as the individual aneurysm growth rate is difficult to predict and some large AAAs do not rupture while in other patients, small AAAs rupture during surveillance. Finite element analysis (FEA) is a method by which biomechanical rupture risk can be estimated based on patient characteristics and a computed tomography (CT)-derived 3D model of an AAA. Microarray analysis allows high-throughput analyses of tissue gene expression. Aims The overall aim of this thesis was to explore and develop new strategies to improve, refine and individualize management of patients with AAA, by applying geometric, biomechanical and molecular analyses. Methods and Results In study I, the CTs of 146 patients with AAAs of diameters between 40 and 60 mm were analyzed with three-dimensional (3D) segmentation and FEA. Simple and multiple regression analyses were performed. Female sex, patient height, lumen volume, body surface area (BSA) and low BMI were shown to be associated with the biomechanical rupture risk of AAA. Study II included 191 patients with AAAs of diameters between 40-50 mm. The AAAs were analyzed with 3D segmentation and FEA after which prediction algorithms were developed by use of machine learning strategies. More precise diameter measurements improved prediction of growth and four-year prognosis of small AAAs. Biomechanical indices and lumen diameter were predictive of future rupture or symptomatic AAA. Growth and rupture required different prediction models. In study III, 37 patients, 42 controls and a validation cohort of 51 patients were analyzed with respect to their circulating levels of neutrophil elastase-derived fibrin degradation products (E-XDP). The results showed that E-XDP was a sensitive marker for AAA, independently of examined comorbidities, and its concentration in peripheral blood correlated with the AAA diameter and the volume and mechanical stress of the intraluminal thrombus (ILT). It was further increased by the presence of coexisting aneurysms. Study IV included 246 tissue samples, divided into tunica media and adventitia, from 76 patients with AAA and 13 organ donor controls, analyzed by microarrays. There were large differences between the transcriptomes of AAA and control media and adventitia. Processes related to inflammation were transmural, whereas the upregulation of proteolysis, angiogenesis and apoptosis along with downregulation of smooth muscle- and differentiation-related gene sets were specific for the aneurysm media. Active smoking increased oxidative stress in all tissues and increased inflammation and lipid-related processes in AAA. The growth rate of the AAA diameter correlated with adaptive immunity in media and lipid processes in adventitia. Conclusions In this thesis, we show that known clinical risk factors and certain geometric properties are associated with biomechanical deterioration of AAAs. Furthermore, geometric and biomechanical analyses can enhance prediction of outcome. Importantly, there are differences between prediction of AAA growth and rupture. Finally, a biomarker was discovered and the transcriptome of AAA including effects of the ILT, smoking and rapid diameter growth rate, was mapped and we envision that the data may be used for future biomarker and drug target discovery

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate