Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate

Comparing Autoencoder to Geometrical Features for Vascular Bifurcations Identification

The cerebrovascular tree is a complex anatomical structure that plays a crucial role in the brain irrigation. A precise identification of the bifurcations in the vascular network is essential for understanding various cerebral pathologies. Traditional methods often require manual intervention and are sensitive to variations in data quality. In recent years, deep learning techniques, and particularly autoencoders, have shown promising performances for feature extraction and pattern recognition in a variety of domains. In this paper, we propose two novel approaches for vascular bifurcation identification based respectiveley on Autoencoder and geometrical features. The performance and effectiveness of each method in terms of classification of vascular bifurcations using medical imaging data is presented. The evaluation was performed on a sample database composed of 91 TOF-MRA, using various evaluation measures, including accuracy, F1 score and confusion matrix.Comment: International Symposium on Image And Signal Processing and Analysis, Sep 2023, Rome, Ital

Hemodynamics of Cerebral Aneurysms: Computational Analyses of Aneurysm Progress and Treatment

The progression of a cerebral aneurysm involves degenerative arterial wall remodeling. Various hemodynamic parameters are suspected to be major mechanical factors related to the genesis and progression of vascular diseases. Flow alterations caused by the insertion of coils and stents for interventional aneurysm treatment may affect the aneurysm embolization process. Therefore, knowledge of hemodynamic parameters may provide physicians with an advanced understanding of aneurysm progression and rupture, as well as the effectiveness of endovascular treatments. Progress in medical imaging and information technology has enabled the prediction of flow fields in the patient-specific blood vessels using computational analysis. In this paper, recent computational hemodynamic studies on cerebral aneurysm initiation, progress, and rupture are reviewed. State-of-the-art computational aneurysmal flow analyses after coiling and stenting are also summarized. We expect the computational analysis of hemodynamics in cerebral aneurysms to provide valuable information for planning and follow-up decisions for treatment

Understanding the role of hemodynamics in the initiation, progression, rupture, and treatment outcome of cerebral aneurysm from medical iamge-based computational studies

About a decade ago, the first image-based computational hemodynamic studies of cerebral aneurysms were presented. Their potential for clinical applications was the results of a right combination of medical image processing, vascular reconstruction, and grid generation techniques used to reconstruct personalziaed domains for computational fluid and solid dynamics solvers and data analysis and visualization techniques. A considerable number of studies have captivated the attention of clinicians, neurosurgeons, and neuroradiologists, who realized the ability of those tools to help in understanding the role played by hemodynamics in the natural history and management of intracranial aneurysms. This paper intends to summarize the most relevant results in the filed reported during the last years.Fil: Castro, Marcelo Adrian. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms

Background: Using computational fluid dynamics (CFD) to compute the hemodynamics in cerebral aneurysms has received much attention in the last decade. The usability of these methods depends on the quality of the computations, highlighted in recent discussions. The purpose of this study is to investigate the convergence of common hemodynamic indicators with respect to numerical resolution. Methods: 38 middle cerebral artery bifurcation aneurysms were studied at two different resolutions (one comparable to most studies, and one finer). Relevant hemodynamic indicators were collected from two of the most cited studies, and were compared at the two refinements. In addition, correlation to rupture was investigated. Results: Most of the hemodynamic indicators were very well resolved at the coarser resolutions, correlating with the finest resolution with a correlation coefficient >0.95. The oscillatory shear index (OSI) had the lowest correlation coefficient of 0.83. A logarithmic Bland-Altman plot revealed noticeable variations in the proportion of the aneurysm under low shear, as well as in spatial and temporal gradients not captured by the correlation alone. Conclusion: Statistically, hemodynamic indicators agree well across the different resolutions studied here. However, there are clear outliers visible in several of the hemodynamic indicators, which suggests that special care should be taken when considering individual assessment

Role of Computational Fluid Dynamics in the Analysis of Haemodynamic and Morphological Characteristics of Intracranial Aneurysms

Aneurysmal subarachnoid hemorrhage (SAH) carries a high morbidity and mortality. The current protocols used to treat the unruptured Intracranial Aneurysms (IAs) are inadequate underscoring the need of finding new descriptors. As demonstrated by the studies performed in this manuscript, haemodynamics plays an important role in the aetiopathogenesis of IAs. An evaluation of haemodynamic indices can provide a useful alternative to predict the behavior of an unruptured IA at an early stage. Studies performed by me demonstrate that Computational Fluid Dynamics (CFD) can be used successfully to predict haemodynamic indices where detailed in vivo measurement of haemodynamic flow variables is not possible owing to technical limitations. European Commission funded Project @neurIST was the first project of it’s kind that brought together a number of multidisciplinary professionals from 32 European institutions and made possible development of state-of-the-art tools for personalised risk assessment and treatment IAs using CFD. These tools have been constantly improved and amended in the light of feedback gathered from their controlled exposures conducted world over, as described in the manuscript. However, need of a well-designed Randomized Controlled Trial in this context cannot be overemphasized, before these tools can be accepted by clinicians and patients. In my study on the validation of different concepts used in CFD, I demonstrated that there is no added advantage of complex Womersley-flow-profile over the much simpler plug-flow profile. One of my studies on initiation and rupture of IAs showed that the haemodynamic patterns of IAs during these two phases are significantly different with values of supra-physiological Wall Shear Stress (WSS) being higher in initiation while lower in rupture phase. I also investigated the effects of pharmacological agents on the aetiopathogenesis of IAs and found that heparin induces significant derangements in the haemodynamics of both, pre-aneurysmal as well as ruptured IA. I propose that heparin (and its derivatives) can, on the one hand may facilitate the rupture of existing IAs, on the other hand they may suppress the formation of new IAs. I have also found significant differences in the results using patient-specific vs. Modeled Boundary Conditions and showed that the 1D circulation model adopted by @neurIST performs better than other approaches found in the literature. I also proposed a novel mechanism of increase in Blood Viscosity leading to high WSS as one of the important underlying mechanisms responsible for the increased incidence of IA formation in smokers and hypertensive patients. In my study on patients with pre-existing Coarctation of Aorta (CoA) and Intracranial Aneurysms, I demonstrated that the cerebral flow-rates in CoA patients were significantly higher when compared to average flow-rates in healthy population. It was also seen that the values and the area affected by supraphysiological WSS (>15Pa) were exponentially higher in patients with CoA indicating the possible role of increased haemodynamic WSS secondary to the increased flow-rates playing an important role in the pathogenesis and rupture of IAs in CoA patients

Hemodynamic study in a real intracranial aneurysm: an in vitro and in silico approach

Mestrado de dupla diplomação com o Centro Federal de Educação Tecnológica Celso Suckow da Fonseca - Cefet/RJIntracranial aneurysm (IA) is a cerebrovascular disease with high rates of mortality and morbidity when it ruptures. It is known that changes in the intra-aneurysmal hemodynamic load play a significant factor in the development and rupture of IA. However, these factors are not fully understood. In this sense, the main objective of this work is to study the hemodynamic behavior during the blood analogues flow inside an AI and to determine its influence on the evolution of this pathology. To this end, experimental and numerical studies were carried out, using a real AI model obtained using computerized angiography. In the experimental approach, it was necessary, in the initial phase, to develop and manufacture biomodels from medical images of real aneurysms. Two techniques were used to manufacture the biomodels: rapid prototyping and gravity casting. The materials used to obtain the biomodels were of low cost. After manufacture, the biomodels were compared to each other for their transparency and final structure and proved to be suitable for testing flow visualizations. Numerical studies were performed with the aid of the Ansys Fluent software, using computational fluid dynamics (CFD), using the finite volume method. Subsequently, flow tests were performed experimentally and numerically using flow rates calculated from the velocity curve of a patient's doppler test. The experimental and numerical tests, in steady-state, made it possible to visualize the three-dimensional behavior of the flow inside the aneurysm, identifying the vortex zones created throughout the cardiac cycle. Correlating the results obtained in the two analyzes, it was possible to identify that the areas of vortexes are characterized by low speed and with increasing the fluid flow, the vortexes are positioned closer to the wall. These characteristics are associated with the rupture of an intracranial aneurysm. There was also a good qualitative correlation between numerical and experimental results.O aneurisma intracraniano (AI) é uma patologia cerebrovascular com altas taxas de mortalidade e morbidade quando se rompe. Sabe-se que alterações na carga hemodinâmica intra-aneurismática exerce um fator significativo no desenvolvimento e ruptura de AI, porém, esses fatores não estão totalmente compreendidos. Nesse sentido, o objetivo principal deste trabalho é o de estudar o comportamento hemodinâmico durante o escoamento de fluidos análogos do sangue no interior de um AI e determinar a sua influência na evolução da patologia. Para tal, foram realizados estudos experimentais e numéricos, utilizando um modelo de AI real obtido por meio de uma angiografia computadorizada. Na abordagem experimental foi necessário, na fase inicial, desenvolver e fabricar biomodelos a partir de imagens médicas de um aneurisma real. No fabrico dos biomodelos foram utilizadas duas técnicas: a prototipagem rápida e o vazamento por gravidade. Os materiais utilizados para a obtenção dos biomodelos foram de baixo custo. Após a fabricação, os biomodelos foram comparados entre si quanto à sua transparência e estrutura final e verificou-se serem adequados para testes de visualizações do fluxo. Os estudos numéricos foram realizados com recurso ao software Ansys Fluent, utilizando a dinâmica dos fluidos computacional (CFD), através do método dos volumes finitos. Posteriormente, foram realizados testes de escoamento experimentais e numéricos, utilizando caudais determinados a partir da curva de velocidades do ensaio doppler de um paciente. Os testes experimentais e numéricos, em regime permanente, possibilitaram a visualização do comportamento tridimensional do fluxo no interior do aneurisma, identificando as zonas de vórtices criadas ao longo do ciclo cardíaco. Correlacionando os resultados obtidos nas duas análises, foi possível identificar que as áreas de vórtices são caracterizadas por uma baixa velocidade e com o aumento do caudal os vórtices posicionam-se mais próximos da parede. Essas características apresentadas estão associadas com a ruptura de aneurisma intracraniano. Verificou-se, também, uma boa correlação qualitativa entre os resultados numéricos e experimentais

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

MRI detection of brain abnormality in sickle cell disease

Introduction: Over the past decades, neuroimaging studies have clarified that a significant proportion of patients with sickle cell disease (SCD) have functionally significant brain abnormalities. Clinically, structural magnetic resonance imaging (MRI) sequences (T2, FLAIR, diffusion-weighted imaging) have been used by radiologists to diagnose chronic and acute cerebral infarction (both overt and clinically silent), while magnetic resonance angiography and venography have been used to diagnose arteriopathy and venous thrombosis. In research settings, imaging scientists are increasingly applying quantitative techniques to shine further light on underlying mechanisms. Areas covered: From a June 2020 PubMed search of ‘magnetic’ or ‘MRI’ and ‘sickle’ over the previous 5 years, we selected manuscripts on T1-based morphometric analysis, diffusion tensor imaging, arterial spin labeling, T2-oximetry, quantitative susceptibility, and connectivity. Expert Opinion: Quantitative MRI techniques are identifying structural and hemodynamic biomarkers associated with risk of neurological and neurocognitive complications. A growing body of evidence suggests that these biomarkers are sensitive to change with treatments, such as blood transfusion and hydroxyurea, indicating that they may hold promise as endpoints in future randomized clinical trials of novel approaches including hemoglobin F upregulation, reduction of polymerization, and gene therapy. With further validation, such techniques may eventually also improve neurological and neurocognitive risk stratification in this vulnerable population