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

Novel Imaging Approaches for the Detection of Hemodynamically Significant Coronary Artery Disease: Quantitative Flow Ratio and Artificial Intelligence-Based Ischemia Algorithm

ABSTRACT In coronary artery disease (CAD), the decision on revascularization is based on the hemodynamic significance of stenoses. However, this cannot directly be determined from the first-line anatomical imaging methods coronary computed tomography angiography (CCTA) in chronic coronary syndrome (CCS) or invasive coronary angiography (ICA) in acute coronary syndrome (ACS). The aim of this thesis was to investigate the prognostic value of two novel approaches to determine functionally significant CAD according to impaired invasive fractional flow reserve (FFR) directly from CCTA in CCS and ICA in ACS. Quantitative flow ratio (QFR) is a novel computational fluid dynamic-based technique to estimate the presence of impaired FFR from biplane ICA. In this study, QFR from untreated non-culprit lesions showed incremental 5-year prognostic value for major adverse cardiac events among ST-elevation myocardial infarction patients undergoing angiography-guided complete revascularization. However, non-culprit QFR did not independently predict non-target-vessel related events prior to planned staged percutaneous coronary intervention (PCI) in ACS patients, and the study does not provide conceptual evidence that QFR could be useful to refine the timing of staged PCI on top of clinical judgement. AI-QCTischemia is an artificial intelligence-based method to predict the probability of an impaired invasive FFR using 37 morphological features from CCTA. Among symptomatic patients with suspected CAD undergoing CCTA, AI-QCTischemia showed incremental prognostic value for the composite of death, myocardial infarction, or unstable angina pectoris throughout a median of 7 years follow-up. This risk stratification pertained especially to patients with no/non-obstructive disease. Patients with obstructive disease on CCTA were referred for downstream myocardial perfusion imaging with positron emission tomography (PET), and among those, AI-QCTischemia showed incremental risk stratification among patients with normal PET perfusion, but not among those with abnormal PET perfusion. KEYWORDS: coronary artery disease, quantitative flow ratio, coronary computed tomography angiography, artificial intelligence, prognosisTIIVISTELMÄ Sepelvaltimotaudissa revaskularisaatiopäätös perustuu hemodynaamisesti merkittävän ahtauman osoitukseen. Tätä ei voida kuitenkaan suoraan määrittää kaikilla kuvantamismenetelmillä, kuten sepelvaltimoiden tietokonetomografialla (TT) kroonisessa sepelvaltimo-oireyhtymässä tai kajoavalla angiografialla akuutissa sepelvaltimotautikohtauksessa. Tämän väitöskirjan tavoitteena oli tutkia kahden uuden sepelvaltimoahtauman hemodynaamisen merkityksen arvioimiseen käytettävän menetelmän ennusteellista arvoa: kajoavaan angiografiaan pohjautuva menetelmä akuutissa sepelvaltimotautikohtauksessa ja TT:aan pohjautuva menetelmä kroonisessa sepelvaltimo-oireyhtymässä. Kvantitatiivinen virtaussuhde (KVS) on uusi laskennalliseen virtausdynamiikkaan perustuva menetelmä, jolla kajoavaan painevaijerimittaukseen perustuvaa sydänlihas-iskemiaa pyritään arvioimaan suoraan tavanomaisista angiografiakuvista. Ei-revaskularisoidun non-culprit-ahtauman KVS:n määrityksellä osoitettiin ennusteellista lisäarvoa 5 vuoden sydän- ja verisuonitautitapahtumien suhteen ST-nousuinfarkti-potilailla, joille oli tehty angiografiaohjattu täydellinen revaskularisaatio. Non-culprit-ahtauman KVS ei kuitenkaan ennustanut kyseiseen suoneen liittyviä tapahtumia ennen suunniteltua viivästettyä non-culprit-ahtauman perkutaanista sepelvaltimotoimenpidettä, joten tämän tutkimuksen perusteella KVS ei vaikuta hyödylliseltä menetelmältä viivästetyn sepelvaltimotoimenpiteen ajoituksen optimoimiseksi. AI-QCTischemia on tekoälyyn perustuva menetelmä, jolla arvioidaan kajoavaan painevaijerimittaukseen perustuvan sydänlihasiskemian todennäköisyyttä käyttäen 37 morfologista sepelvaltimoiden TT:aan pohjautuvaa muuttujaa. Oireisilla potilailla, joille tehtiin TT-tutkimus sepelvaltimotaudin epäilyn vuoksi, AI-QCTischemia tarjosi ennusteellista lisäarvoa yhdistelmätapahtumalle (kuolema, sydäninfarkti tai epävakaa angina pectoris) 7 vuoden seurannan aikana. Tämä riskiluokittelu koski erityisesti potilaita, joilla ei todettu ahtauttavaa sepelvaltimotautia TT:ssa. Potilaat, joilla todettiin TT:n perusteella ahtauttava sepelvaltimotauti, ohjattiin sydänlihasperfuusion kuvantamiseen positroniemissiotomografialla (PET). Tässä joukossa AI-QCTischemia antoi ennusteellista lisätietoa potilailla, joilla oli normaali sydänlihasperfuusio, mutta ei niillä, joilla perfuusio oli alentunut. AVAINSANAT: sepelvaltimotauti, kvantitatiivinen virtaussuhde, tietokonetomografia, tekoäly, ennust

Follow-up of iatrogenic aorto-coronary "Dunning" dissections by cardiac computed tomography imaging

Background: Iatrogenic aorto-coronary dissections following percutaneous coronary interventions (PCI) represent a rare but potentially life threatening complication. This restrospective and observational study aims to describe our in-house experience for timely diagnostics and therapy including cardiovascular imaging to follow-up securely high-risk patients with Dunning dissections. Methods: Dunning dissections (DD) occurred during clinical routine PCIs, which were indicated according to current ESC guidelines. Diagnostic assessment, treatment and follow-up were based on coronary angiography with PCI or conservative treatment and cardiac computed tomography (cCTA) imaging. Results: A total of eight patients with iatrogenic DD were included. Median age was 69 years (IQR 65.8–74.5). Patients revealed a coronary multi-vessel-disease in 75% with a median SYNTAX-II-score of 35.3 (IQR 30.2–41.2). The most common type of DD was type III (50%), followed by type I (38%) and type II (13%). In most patients (88%) the DD involved the right coronary arterial ostium. 63% were treated by PCI, the remaining patients were treated conservatively. 88% of patients received at least one cCTA within 2 days, 50% were additionally followed-up by cCTA within a median of 6 months (range: 4–8 months) without any residual. Conclusion: Independently of the type of DD (I-III) it was demonstrated that cCTA represents a valuable imaging modality for detection and follow-up of patients with DDs

Digital Twin of Cardiovascular Systems

Patient specific modelling using numerical methods is widely used in understanding diseases and disorders. It produces medical analysis based on the current state of patient’s health. Concurrently, as a parallel development, emerging data driven Artificial Intelligence (AI) has accelerated patient care. It provides medical analysis using algorithms that rely upon knowledge from larger human population data. AI systems are also known to have the capacity to provide a prognosis with overallaccuracy levels that are better than those provided by trained professionals. When these two independent and robust methods are combined, the concept of human digital twins arise. A Digital Twin is a digital replica of any given system or process. They combine knowledge from general data with subject oriented knowledge for past, current and future analyses and predictions. Assumptions made during numerical modelling are compensated using knowledge from general data. For humans, they can provide an accurate current diagnosis as well as possible future outcomes. This allows forprecautions to be taken so as to avoid further degradation of patient’s health.In this thesis, we explore primary forms of human digital twins for the cardiovascular system, that are capable of replicating various aspects of the cardiovascular system using different types of data. Since different types of medical data are available, such as images, videos and waveforms, and the kinds of analysis required may be offline or online in nature, digital twin systems should be uniquely designed to capture each type of data for different kinds of analysis. Therefore, passive, active and semi-active digital twins, as the three primary forms of digital twins, for different kinds of applications are proposed in this thesis. By the virtue of applications and the kind of data involved ineach of these applications, the performance and importance of human digital twins for the cardiovascular system are demonstrated. The idea behind these twins is to allow for the application of the digital twin concept for online analysis, offline analysis or a combination of the two in healthcare. In active digital twins active data, such as signals, is analysed online in real-time; in semi-active digital twin some of the components being analysed are active but the analysis itself is carried out offline; and finally, passive digital twins perform offline analysis of data that involves no active component.For passive digital twin, an automatic workflow to calculate Fractional Flow Reserve (FFR) is proposed and tested on a cohort of 25 patients with acceptable results. For semi-active digital twin, detection of carotid stenosis and its severity using face videos is proposed and tested with satisfactory results from one carotid stenosis patient and a small cohort of healthy adults. Finally, for the active digital twin, an enabling model is proposed using inverse analysis and its application in the detection of Abdominal Aortic Aneurysm (AAA) and its severity, with the help of a virtual patient database. This enabling model detected artificially generated AAA with an accuracy as high as 99.91% and classified its severity with acceptable accuracy of 97.79%. Further, for active digital twin, a truly active model is proposed for continuous cardiovascular state monitoring. It is tested on a small cohort of five patients from a publicly available database for three 10-minute periods, wherein this model satisfactorily replicated and forecasted patients’ cardiovascular state. In addition to the three forms of human digital twins for the cardiovascular system, an additional work on patient prioritisation in pneumonia patients for ITU care using data-driven digital twin is also proposed. The severity indices calculated by these models are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that using these models, the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89

CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography

Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity and by computer scientists to create computer-aided diagnostic systems to help in such assessment. In addition, baseline classification methods are proposed and analyzed, validating the functionality of CADICA and giving the scientific community a starting point to improve CAD detection

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories

Carotid Artery Disease

This book will bring out the state of art of carotid stenosis in the basic and clinical approaches for better understanding of the mechanisms and useful therapies for these disease. We hope that would be a new current trend understanding new aspects regarding this scientific problematic involving not only anatomical, functional but also clinical questions