Patient-specific CFD simulation of intraventricular haemodynamics based on 3D ultrasound imaging

Background: The goal of this paper is to present a computational fluid dynamic (CFD) model with moving boundaries to study the intraventricular flows in a patient-specific framework. Starting from the segmentation of real-time transesophageal echocardiographic images, a CFD model including the complete left ventricle and the moving 3D mitral valve was realized. Their motion, known as a function of time from the segmented ultrasound images, was imposed as a boundary condition in an Arbitrary Lagrangian-Eulerian framework. Results: The model allowed for a realistic description of the displacement of the structures of interest and for an effective analysis of the intraventricular flows throughout the cardiac cycle. The model provides detailed intraventricular flow features, and highlights the importance of the 3D valve apparatus for the vortex dynamics and apical flow. Conclusions: The proposed method could describe the haemodynamics of the left ventricle during the cardiac cycle. The methodology might therefore be of particular importance in patient treatment planning to assess the impact of mitral valve treatment on intraventricular flow dynamics

NOVEL STRATEGIES FOR THE MORPHOLOGICAL AND BIOMECHANICAL ANALYSIS OF THE CARDIAC VALVES BASED ON VOLUMETRIC CLINICAL IMAGES

This work was focused on the morphological and biomechanical analysis of the heart valves exploiting the volumetric data. Novel methods were implemented to perform cardiac valve structure and sub-structure segmentation by defining long axis planes evenly rotated around the long axis of the valve. These methods were exploited to successfully reconstruct the 3D geometry of the mitral, tricuspid and aortic valve structures. Firstly, the reconstructed models were used for the morphological analysis providing a detailed description of the geometry of the valve structures, also computing novel indexes that could improve the description of the valvular apparatus and help their clinical assessment. Additionally, the models obtained for the mitral valve complex were adopted for the development of a novel biomechanical approach to simulate the systolic closure of the valve, relying on highly-efficient mass-spring models thus obtaining a good trade-off between the accuracy and the computational cost of the numerical simulations. In specific: \u2022 First, an innovative and semi-automated method was implemented to generate the 3D model of the aortic valve and of its calcifications, to quantitively describe its 3D morphology and to compute the anatomical aortic valve area (AVA) based on multi-detector computed tomography images. The comparison of the obtained results vs. effective AVA measurements showed a good correlation. Additionally, these methods accounted for asymmetries or anatomical derangements, which would be difficult to correctly capture through either effective AVA or planimetric AVA. \u2022 Second, a tool to quantitively assess the geometry of the tricuspid valve during the cardiac cycle using multidetector CT was developed, in particular focusing on the 3D spatial relationship between the tricuspid annulus and the right coronary artery. The morphological analysis of the annulus and leaflets confirmed data reported in literature. The qualitative and quantitative analysis of the spatial relationship could standardize the analysis protocol and be pivotal in the procedure planning of the percutaneous device implantation that interact with the tricuspid annulus. \u2022 Third, we simulated the systolic closure of three patient specific mitral valve models, derived from CMR datasets, by means of the mass spring model approach. The comparison of the obtained results vs. finite element analyses (considered as the gold-standard) was performed tuning the parameters of the mass spring model, so to obtain the best trade-off between computational expense and accuracy of the results. A configuration mismatch between the two models lower than two times the in-plane resolution of starting imaging data was yielded using a mass spring model set-up that requires, on average, only ten minutes to simulate the valve closure. \u2022 Finally, in the last chapter, we performed a comprehensive analysis which aimed at exploring the morphological and mechanical changes induced by the myxomatous pathologies in the mitral valve tissue. The analysis of mitral valve thickness confirmed the data and patterns reported in literature, while the mechanical test accurately described the behavior of the pathological tissue. A preliminary implementation of this data into finite element simulations suggested that the use of more reliable patient-specific and pathology-specific characterization of the model could improve the realism and the accuracy of the biomechanical simulations

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of 0.82 +/- 0.06, mean surface distance of 0.43 +/- 0.14 mm and 95% Hausdorff Distance (HD) of 3.57 +/- 1.56 mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

Investigation and Validation of Imaging Techniques for Mitral Valve Disease Diagnosis and Intervention

Mitral Valve Disease (MVD) describes a variety of pathologies that result in regurgitation of blood during the systolic phase of the cardiac cycle. Decisions in valvular disease management rely heavily on non-invasive imaging. Transesophageal echocardiography (TEE) is widely recognized as the key evaluation technique where backflow of high velocity blood can be visualized under Doppler. In most cases, TEE imaging is adequate for identifying mitral valve pathology, though the modality is often limited from signal dropout, artifacts and a restricted field of view. Quantitative analysis is an integral part of the overall assessment of valve morphology and gives objective evidence for both classification and guiding intervention of regurgitation. In addition, patient-specific models derived from diagnostic TEE images allow clinicians to gain insight into uniquely intricate anatomy prior to surgery. However, the heavy reliance on TEE segmentation for diagnosis and modelling has necessitated an evaluation of the accuracy of the oft-used mitral valve imaging modality. Dynamic cardiac 4D-Computed Tomography (4D-CT) is emerging as a valuable tool for diagnosis, quantification and assessment of cardiac diseases. This modality has the potential to provide a high quality rendering of the mitral valve and subvalvular apparatus, to provide a more complete picture of the underlying morphology. However, application of dynamic CT to mitral valve imaging is especially challenging due to the large and rapid motion of the valve leaflets. It is therefore necessary to investigate the accuracy and level of precision by which dynamic CT captures mitral valve motion throughout the cardiac cycle. To do this, we design and construct a silicone and bovine quasi-static mitral valve phantom which can simulate a range of ECG-gated heart rates and reproduce physiologic valve motion over the cardiac cycle. In this study, we discovered that the dynamic CT accurately captures the underlying valve movement, but with a higher prevalence of image artifacts as leaflet and chordae motion increases due to elevated heart rates. In a subsequent study, we acquire simultaneous CT and TEE images of both a silicone mitral valve phantom and an iodine-stained bovine mitral valve. We propose a pipeline to use CT as the ground truth to study the relationship between TEE intensities and the underlying valve morphology. Preliminary results demonstrate that with an optimized threshold selection based solely on TEE pixel intensities, only 40\% of pixels are correctly classified as part of the valve. In addition, we have shown that emphasizing the centre-line rather than the boundaries of high intensity TEE image regions provides a better representation and segmentation of the valve morphology. This work has the potential to inform and augment the use of TEE for diagnosis and modelling of the mitral valve in the clinical workflow for MVD

DEVELOPMENT AND IMPLEMENTATION OF NOVEL STRATEGIES TO EXPLOIT 3D ULTRASOUND IMAGING IN CARDIOVASCULAR COMPUTATIONAL BIOMECHANICS

Introduction In the past two decades, major advances have been made in cardiovascular diseases assessment and treatment owing to the advent of sophisticated and more accurate imaging techniques, allowing for better understanding the complexity of 3D anatomical cardiovascular structures1. Volumetric acquisition enables the visualization of cardiac districts from virtually any perspective, better appreciating patient-specific anatomical complexity, as well as an accurate quantitative functional evaluation of chamber volumes and mass avoiding geometric assumptions2. Additionally, this scenario also allowed the evolution from generic to patient-specific 3D cardiac models that, based on in vivo imaging, faithfully represent the anatomy and different cardiac features of a given alive subject, being pivotal either in diagnosis and in planning guidance3. Precise morphological and functional knowledge about either the heart valves\u2019 apparatus and the surrounding structures is crucial when dealing with diagnosis as well as preprocedural planning4. To date, computed tomography (CT) and real-time 3D echocardiography (rt3DE) are typically exploited in this scenario since they allow for encoding comprehensive structural and dynamic information even in the fourth dimension (i.e., time)5,6. However, owing to its cost-effectiveness and very low invasiveness, 3D echocardiography has become the method of choice in most situations for performing the evaluation of cardiac function, developing geometrical models which can provide quantitative anatomical assessment7. Complementing this scenario, computational models have been introduced as numerical engineering tools aiming at adding qualitative and quantitative information on the biomechanical behavior in terms of stress-strain response and other multifactorial parameters8. In particular, over the two last decades, their applications have been ranging from elucidating the heart biomechanics underlying different patho-physiological conditions9 to predicting the effects of either surgical or percutaneous procedures, even comparing several implantation techniques and devices10. At the early stage, most of the studies focused on FE modeling in cardiac environment were based on paradigmatic models11\u201315, being mainly exploited to explore and investigate biomechanical alterations following a specific pathological scenario or again to better understand whether a surgical treatment is better or worse than another one. Differently, nowadays the current generation of computational models heavily exploits the detailed anatomical information yielded by medical imaging to provide patient-specific analyses, paving the way toward the development of virtual surgical-planning tools16\u201319. In this direction, cardiac magnetic resonance (CMR) and CT/micro-CT are the mostly accomplished imaging modality, since they can provide well-defined images thanks to their spatial and temporal resolutions20\u201325. Nonetheless, they cannot be applied routinely in clinical practice, as it can be differently done with rt3DE, progressively became the modality of choice26 since it has no harmful effects on the patient and no radiopaque contrast agent is needed. Despite these advantages, 3D volumetric ultrasound imaging shows intrinsic limitations beyond its limited resolution: i) the deficiency of morphological detail owing to either not so easy achievable detection (e.g., tricuspid valve) or not proper acoustic window, ii) the challenge of tailoring computational models to the patient-specific scenario mimicking the morphology as well as the functionality of the investigated cardiac district (e.g., tethering effect exerted by chordal apparatus in mitral valve insufficiency associated to left ventricular dilation), and iii) the needing to systematically analyse devices performances when dealing with real-life cases where ultrasound imaging is the only performable technique but lacking of standardized acquisition protocol. Main findings In the just described scenario, the main aim of this work was focused on the implementation, development and testing of numerical strategies in order to overcome issues when dealing with 3D ultrasound imaging exploitation towards predictive patient-specific modelling approaches focused on both morphological and biomechanical analyses. Specifically, the first specific objective was the development of a novel approach integrating in vitro imaging and finite element (FE) modeling to evaluate tricuspid valve (TV) biomechanics, facing with the lack of information on anatomical features owing to the clinically evident demanding detection of this anatomical district through in vivo imaging. \u2022 An innovative and semi-automated framework was implemented to generate 3D model of TV, to quantitively describe its 3D morphology and to assess its biomechanical behaviour. At this aim, an image-based in vitro experimental approach was integrated with numerical models based on FE strategy. Experimental measurements directly performed on the benchmark (mock circulation loop) were compared with geometrical features computed on the 3D reconstructed model, pinpointing a global good consistency. Furthermore, obtained realistic reconstructions were used as the input of the FE models, even accounting for proper description of TV leaflets\u2019 anisotropic mechanical response. As done experimentally, simulations reproduced both \u201cincompetent\u201d (FTR) and \u201ccompetent-induced\u201d (PMA), proving the efficiency of such a treatment and suggesting translational potential to the clinic. The second specific aim was the implementation of a computational framework able to reproduce a functionally equivalent model of the mitral valve (MV) sub-valvular apparatus through chordae tendineae topology optimization, aiming at chordae rest length arrangement to be able to include their pre-stress state associated to specific ventricular conformation. \u2022 We sought to establish a framework to build geometrically tractable, functionally equivalent models of the MV chordae tendineae, addressing one of the main topics of the computational scientific literature towards the development of faithful patient-specific models from in vivo imaging. Exploiting the mass spring model (MSM) approach, an iterative tool was proposed aiming to the topology optimization of a paradigmatic chordal apparatus of MVs affected by functional regurgitation, in order to be able to equivalently account for tethering effect exerted by the chordae themselves. The results have shown that the algorithm actually lowered the error between the simulated valve and ground truth data, although the intensity of this improvement is strongly valve-dependent.Finally, the last specific aim was the creation of a numerical strategy able to allow for patient-specific geometrical reconstruction both pre- and post- LVAD implantation, in a specific high-risk clinical scenario being rt3DE the only available imaging technique to be used but without any acquisition protocol. \u2022 We proposed a numerical approach which allowed for a systematic and selective analysis of the mechanism associated to intraventricular thrombus formation and thrombogenic complications in a LVAD-treated dilated left ventricle (LV). Ad-hoc geometry reconstruction workflow was implemented to overcome limitations associated to imaging acquisition in this specific scenario, thus being able to generate computational model of the LV assisted with LVAD. In details, results suggested that blood stasis is influenced either by LVAD flow rate and, to a greater extent, by LV residual contractility, being the positioning of the inflow cannula insertion mandatory to be considered when dealing with LVAD thrombogenic potential assessment

Towards Patient Specific Mitral Valve Modelling via Dynamic 3D Transesophageal Echocardiography

Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume compounding and automated image analysis to more accurately and quickly capture the relevant valve geometry needed to produce patient-specific mitral valve models. Volume compounding enables multiple ultrasound acquisitions from different orientations and locations to be aligned and blended to form a single volume with improved resolution and field-of-view. A series of overlapping transgastric views are acquired that are then registered together with the standard en-face image and are combined using a blending function. The resulting compounded ultrasound volumes allow the visualization of a wider range of anatomical features within the left heart, enhancing the capabilities of a standard TEE probe. In this thesis, I first describe a semi-automatic segmentation algorithm based on active contours designed to produce segmentations from end-diastole suitable for deriving 3D printable molds. Subsequently I describe the development of DeepMitral, a fully automatic segmentation pipeline which leverages deep learning to produce very accurate segmentations with a runtime of less than ten seconds. DeepMitral is the first reported method using convolutional neural networks (CNNs) on 3D TEE for mitral valve segmentations. The results demonstrate very accurate leaflet segmentations, and a reduction in the time and complexity to produce a patient-specific mitral valve replica. Finally, a real-time annulus tracking system using CNNs to predict the annulus coordinates in the spatial frequency domain was developed. This method facilitates the use of mitral annulus tracking in real-time guidance systems, and further simplifies mitral valve modelling through the automatic detection of the annulus, which is a key structure for valve quantification, and reproducing accurate leaflet dynamics

Modelling mitral valvular dynamics–current trend and future directions

Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance

Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations. Purpose. In this work, we particularly look at mitral valve reconstruction (MVR) surgery, which is to re-establish the functionality of an incompetent mitral valve (MV) through implantation of an artificial ring that reshapes the valvular morphology. We aim at supporting MVR by providing surgeons with biomechanical FEM-based MVR surgery simulations that enable them to assess the simulated behavior of the MV after an MVR. However, according to the above requirements, such surgery simulation is really beneficial to surgeons only if it is patient-specific, surgical expert knowledge-based, comprehensive in terms of the underlying model and the patient’s data, and if its setup and execution is fully automated and integrated into the surgical treatment workflow. Methods. This PhD work conducts research on simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. First, we derive a biomechanical MV/MVR model and develop an FEM-based MVR surgery simulation using the FEM software toolkit HiFlow3. Following, we outline the functionality and features of the Medical Simulation Markup Language (MSML) and how it simplifies the biomechanical modeling workflow. It is then detailed, how, by means of the MSML and a set of dedicated MVR simulation reprocessing operators, patient-individual medical data can comprehensively be analyzed and processed in order for the fully automated setup of MVR simulation scenarios. Finally, the presented work is integrated into the cognitive system architecture of the joint research project Cognition-Guided Surgery. We particularly look at its semantic knowledge and data infrastructure as well as at the setup of its cognitive software components, which eventually facilitate cognition-guidance and patient-specifity for the overall simulation-enhanced MVR assistance pipeline. Results and Discussion. We have proposed and implemented, for the first time, a prototypic system for simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. The overall system was evaluated in terms of functionality and performance. Through its cognitive, data-driven pipeline setup, medical patient data and surgical information is analyzed and processed comprehensively, efficiently and fully automatically, and the hence set-up simulation scenarios yield reliable, patient-specific MVR surgery simulation results. This indicates the system’s usability and applicability. The proposed work thus presents an important step towards a simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance, and can – once operative – be expected to significantly enhance MVR surgery. Concluding, we discuss possible further research contents and promising applications to build upon the presented work