Meshless electrophysiological modeling of cardiac resynchronization therapy—benchmark analysis with finite-element methods in experimental data

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). © 2022 by the authors. Licensee MDPI, Basel, Switzerland

MRI methods for predicting response to cardiac resynchronization therapy

Cardiac Resynchronization Therapy (CRT) is a treatment option for heart failure patients with ventricular dyssynchrony. CRT corrects for dyssynchrony by electrically stimulating the septal and lateral walls of the left ventricle (LV), forcing synchronous con- traction and improving cardiac output. Current selection criteria for CRT rely upon the QRS duration, measured from a surface electrocardiogram, as a marker of electrical dyssynchrony. Unfortunately, 30-40% of patients undergoing CRT fail to benefit from the treatment. A multitude of studies have shown that presence of mechanical dyssynchrony in the LV is an important factor in determining if a patient will benefit from CRT. Furthermore, recent evidence suggests that patient response can be improved by placing the LV pacing lead in the most dyssynchronous or latest contracting segment. The overall goal of this project was to develop methods that allow for accurate assessment and display of regional mechanical dyssynchrony throughout the LV and at the site of the LV pacing lead. To accomplish this goal, we developed a method for quantifying regional dyssynchrony from standard short-axis cine magnetic resonance (MR) images. To assess the effects of LV lead placement, we developed a registration method that allows us to project the LV lead location from dual-plane fluoroscopy onto MR measurements of cardiac function. By applying these techniques in patients undergoing CRT, we were able to investigate the relationship between regional dyssynchrony, LV pacing lead location, and CRT response.Ph.D

Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 +/- 1.21 mm and 2.27 +/- 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.FCT—Fundacão para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/95438/2013 (P Morais) and SFRH/BD/93443/2013 (S Queirós). This work was supported by the projects NORTE-07-0124-FEDER-000017 and NORTE-01-0145-FEDER-000013, co-funded by Programa Operacional Regional do Norte, Quadro de Referência Estratégico Nacional, through Fundo Europeu de Desenvolvimento Regional (FEDER). The authors would also like to acknowledge the EU (FP7) framework program, for the financial support of the DOPPLER-CIP project (grant no. 223615)info:eu-repo/semantics/publishedVersio

Cardiovascular magnetic resonance of the right ventricle

Introduction: Whilst most of the attention has been devoted to the left ventricle in cardiovascular disease, the right ventricle has been somewhat neglected. In the last decades, there has been a renewal of interest in the right ventricle, in part driven by advances in cardiovascular imaging. Methods: Cardiovascular magnetic resonance is arguably the best imaging modality for the study of the right ventricle. In this research thesis, cardiovascular magnetic resonance was used as the primary research tool to assess the right ventricle in different conditions and settings. Results: This thesis encompasses five studies that have been published as peer - reviewed articles. The results of these studies were the following: 1)Right ventricular dilatation and dysfunction was found in a group of patients with Marfan syndrome, further supporting the existence of a Marfan - related cardiomyopathy; 2) In thalassaemia major, right ventricular volumes and ejection fraction differed from healthy controls, and new reference ranges based on patients without iron overload were derived; 3) Myocardial iron loading in thalassaemia major was associated with progressive right ventricular dysfunction; 4) Right ventricular dysfunction due to myocardial siderosis was reversible with effective iron chelation therapy, and; 5) In advanced heart failure, right ventricular function was a predictor of response and outcomes in patients undergoing cardiac resynchronization therapy. Conclusion: The right ventricle is an essential component of the circulatory system, and should be more widely evaluated in patients with cardiopulmonary disease

Vectorcardiographic evaluation of electrical dyssynchrony and its role in predicting response to cardiac resynchronization therapy

[Doctor of Medicine thesis]. Cardiac resynchronization therapy (CRT) has become an important therapeutic strategy for heart failure (HF) patients with impaired left ventricular (LV) systolic function and prolonged QRS duration. The benefit of CRT as an adjunct to pharmacological therapy is now well established, with sustained improvements in quality of life, hospitalization rates and mortality. However, even in carefully selected patients, the response to CRT is often unpredictable with a considerable number of nonresponders (30-50%). Although the reasons for this nonresponse are not entirely clear, studies have suggested that non-optimal left ventricular (LV) lead positioning, lack of electrical dyssynchrony, suboptimal device programming and myocardial scar burden play an important role. More recently, a wealth of evidence has pointed to the limitations of 12-lead electrocardiography, suggesting it may not accurately reflect the presence or complexities of electrical dyssynchrony in the failing heart. As the efficacy of CRT is primarily achieved through LV resynchronization, there has been renewed interest in the development of techniques that enable better characterization of cardiac electric activation patterns and identification of electrical dyssynchrony. This approach would appear logical, given that CRT is primarily an ‘electrical therapy’, designed to treat an underlying electrical conduction abnormality. Vectorcardiography (VCG), which was first described in 1920, offers an alternative interpretation of the 12-lead ECG. Its resurgence in the field of CRT has emerged from the recognition that VCG parameters can provide information on dyssynchrony beyond that currently provided by the 12-lead ECG. Prominent amongst these is vectorcardiographic QRS area (QRSarea), which has been shown to be superior to QRSd and QRS morphology in predicting response to CRT. The work presented herein is structured into two major sections. First, we investigate the role of QRSarea as a novel predictor of response to CRT. Using a combination of different study designs, our results demonstrate that QRSarea is a better predictor of CRT response than QRSd and QRS morphology. We also show that CRT-induced ΔQRSarea can be used to help quantify LV resynchronization and to predict long-term clinical outcomes following CRT. Importantly, we are the first to show that a concomitant reduction in both QRSarea and QRSd is associated with the best clinical outcomes after CRT, indicating that ECG and VCG can be used in conjunction to help improve patient selection for CRT. In the second part of this thesis, we focus on the development and validation of a novel, vector-based 3D electroanatomical modelling system. Using a novel computational method, ECGSync combines the surface ECG-derived vectorcardiogram with cardiac magnetic resonance imaging to estimate, by inverse solution, the 3-dimensional sequence of LV activation. Accordingly, we show that ECGSync can noninvasively map ventricular electrical activity and accurately locate the site of latest electrical activation prior to CRT implantation. Furthermore, we demonstrate that novel ECGSync-derived markers of dyssynchrony can help predict CRT response. Our findings suggest that VCG may have great potential to improve the clinical application of CRT

Planification de l’ablation radiofréquence des arythmies cardiaques en combinant modélisation et apprentissage automatique

Cardiac arrhythmias are heart rhythm disruptions which can lead to sudden cardiac death. They require a deeper understanding for appropriate treatment planning. In this thesis, we integrate personalized structural and functional data into a 3D tetrahedral mesh of the biventricular myocardium. Next, the Mitchell-Schaeffer (MS) simplified biophysical model is used to study the spatial heterogeneity of electrophysiological (EP) tissue properties and their role in arrhythmogenesis. Radiofrequency ablation (RFA) with the elimination of local abnormal ventricular activities (LAVA) has recently arisen as a potentially curative treatment for ventricular tachycardia but the EP studies required to locate LAVA are lengthy and invasive. LAVA are commonly found within the heterogeneous scar, which can be imaged non-invasively with 3D delayed enhanced magnetic resonance imaging (DE-MRI). We evaluate the use of advanced image features in a random forest machine learning framework to identify areas of LAVA-inducing tissue. Furthermore, we detail the dataset’s inherent error sources and their formal integration in the training process. Finally, we construct MRI-based structural patient-specific heart models and couple them with the MS model. We model a recording catheter using a dipole approach and generate distinct normal and LAVA-like electrograms at locations where they have been found in clinics. This enriches our predictions of the locations of LAVA-inducing tissue obtained through image-based learning. Confidence maps can be generated and analyzed prior to RFA to guide the intervention. These contributions have led to promising results and proofs of concepts.Les arythmies sont des perturbations du rythme cardiaque qui peuvent entrainer la mort subite et requièrent une meilleure compréhension pour planifier leur traitement. Dans cette thèse, nous intégrons des données structurelles et fonctionnelles à un maillage 3D tétraédrique biventriculaire. Le modèle biophysique simplifié de Mitchell-Schaeffer (MS) est utilisé pour étudier l’hétérogénéité des propriétés électrophysiologiques (EP) du tissu et leur rôle sur l’arythmogénèse. L’ablation par radiofréquence (ARF) en éliminant les activités ventriculaires anormales locales (LAVA) est un traitement potentiellement curatif pour la tachycardie ventriculaire, mais les études EP requises pour localiser les LAVA sont longues et invasives. Les LAVA se trouvent autour de cicatrices hétérogènes qui peuvent être imagées de façon non-invasive par IRM à rehaussement tardif. Nous utilisons des caractéristiques d’image dans un contexte d’apprentissage automatique avec des forêts aléatoires pour identifier des aires de tissu qui induisent des LAVA. Nous détaillons les sources d’erreur inhérentes aux données et leur intégration dans le processus d’apprentissage. Finalement, nous couplons le modèle MS avec des géométries du coeur spécifiques aux patients et nous modélisons le cathéter avec une approche par un dipôle pour générer des électrogrammes normaux et des LAVA aux endroits où ils ont été localisés en clinique. Cela améliore la prédiction de localisation du tissu induisant des LAVA obtenue par apprentissage sur l’image. Des cartes de confiance sont générées et peuvent être utilisées avant une ARF pour guider l’intervention. Les contributions de cette thèse ont conduit à des résultats et des preuves de concepts prometteurs

Comparison of T1-maps and late gadolinium enhancement images in the detection of Myocardial Fibrosis in Hypertrophic Cardiomyopathy

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2021, Universidade de Lisboa, Faculdade de CiênciasHypertrophic Cardiomyopathy (HCM) is characterized as an abnormal and heterogeneous thickening of the Left Ventricle (LV) wall. HCM is the leading cause of sudden cardiac death in children and young people, with an estimated prevalence of 1:500 in the general population. Myocardial fibrosis is the key histopathological hallmark in HCM and is presented in different patterns: interstitial diffuse fibrosis which, if not treated, evolves to replacement fibrosis. Cardiac Magnetic Resonance (CMR) imaging has been used for the detection and quantification of myocardial fibrosis. The Late Gadolinium Enhancement (LGE) technique is the primary tool for non-invasive tissue characterization, particularly for replacement fibrosis. Conversely, T1 mapping is commonly used for the detection of diffuse interstitial fibrosis, frequently missed using LGE. The clear disadvantage of LGE relies on the need to inject contrast agents that, despite being considered safe, may accumulate in the body for years and potentially cause nephrogenic systemic fibrosis in end-stage chronic kidney disease patients. The capability of native T1 mapping identifying not only diffuse interstitial but also replacement fibrosis would play a pivotal role in HCM diagnosis. The potential of native T1 mapping for a cheaper and non-contrast HCM assessment needs to be further studied. A database of 15 HCM patients, without and with fibrosis, was acquired at Hospital da Luz, Lisboa. In this project, (1) an extensive image preprocessing pipeline was applied to aim for the best possible spatial alignment of the myocardium between the two modalities (native T1 mapping and LGE); (2) the mean native T1 values of individuals without and with the presence of scarred tissue were examined; (3) a pixel-by-pixel analysis was performed to investigate if there is a correlation between fibrotic tissue in LGE and hyperintense regions in native T1 mapping; (4) a Texture Analysis (TA) was performed to study if texture information of native T1 mapping could provide differential diagnosis or prognostic information beyond mean T1 values. The first step was the most longstanding and challenging process. The registration of T1 and LGE images is difficult due to the different intensity profiles. The registration of the myocardial masks using a model with rigid, affine, and free-form deformation transformations revealed to be the best methodology. Mean native T1 values were not increased in patients with scarred tissue. Regarding the third aim, no clear intensity correlation between techniques was observed, which suggests the need for the TA. Seven features (in a total of 350) were selected to distinguish between cardiac segments without and with fibrotic tissue using a ML (Machine Learning) algorithm that finds the features that most contribute to distinguish the two groups. Four first-order features distinguish the cohorts due to the presence of scarred tissue - hyperintense zones - and three texture features suggest that the fibrotic remodeling in the myocardium of HCM patients might be associated with a more heterogeneous tissue texture. A Receiver Operating Characteristics (ROC) analysis was performed and revealed that the Cluster Prominence is the feature that best distinguishes sections without and with fibrotic tissue (accuracy of 70%) but with low sensitivity (65%) and low specifity (64%). A model with the 90th Percentile feature revealed an accuracy of 64%, sensitivity of 71% and specificity of 57%. Studying the Variance feature, the achieved accuracy was 63%, with 66% of sensitivity and 60% of specificity. The remaining features yielded lower accuracy values than the ones previously mentioned, but all of them higher than 50%. The low sensitivity and specificity of the best three models suggest that analysing these values considering these features may help cardiologists to identify focal fibrosis regions and avoid contrast injection methods but may not provide an accurate diagnosis of the presence of fibrotic tissue alone. Further research on the correlation of native T1 mapping and LGE cardiac images is highly recommended to develop a contrast-agent-free technology to replace LGE.A Cardiomiopatia Hipertrófica (do inglês, HCM) é descrita por um espessamento anormal e heterogéneo da parede do ventrículo esquerdo (do inglês, LV). A HCM é a principal causa de morte súbita cardíaca em crianças e jovens, com uma prevalência estimada de 1:500 na população em geral. Esta doença é, na sua maioria, hereditária, e causada por variantes nos genes da proteína do sarcómero (predominantemente MYH7 e MYBPC3). A fibrose do miocárdio é a principal marca histopatológica da HCM e apresenta-se em diferentes padrões: fibrose intersticial difusa que, se não tratada, evolui para fibrose focal. A fibrose é caracterizada por um aumento da deposição de colagénio, que afeta a viabilidade do miocárdio. A imagem de Ressonância Magnética Cardíaca (do inglês, CMR) tem sido usada para a deteção e quantificação de fibrose do miocárdio. A técnica de Realce Tardio (do inglês, LGE) é a principal ferramenta para caracterização não invasiva de tecidos, particularmente de fibrose focal. Em contrapartida, o mapeamento T1 é a técnica mais utilizada para deteção de fibrose intersticial difusa, frequentemente não detetada usando LGE. A clara desvantagem do LGE reside na necessidade de injeção de agentes de contraste. Apesar destes agentes serem considerados seguros, frequentemente causam alergias, podem-se acumular no corpo, por anos, e podem causar fibrose sistémica nefrogénica em pacientes com doença renal crónica terminal. A capacidade do mapeamento T1 nativo identificar, não só a fibrose intersticial difusa mas também a fibrose focal, desempenharia um papel fundamental no diagnóstico da HCM. Consequentemente, é de extrema importância estudar o potencial do mapeamento T1 nativo para uma avaliação desta patologia sem contraste e, desta forma, eliminar os riscos associados à injeção de contraste e reduzir os custos e tempo de preparação associados à utilização de gadolínio. Uma base de dados de 15 pacientes com HCM, com e sem fibrose, previamente adquirida no Hospital da Luz, Lisboa, foi analisada. Neste projeto, (1) aplicou-se um extenso conjunto de passos de pré-processamento de imagem para alcançar a melhor técnica possível de alinhamento espacial do miocárdio entre as duas modalidades (mapeamento T1 nativo e Realce Tardio); (2) após a divisão do miocárdio em 6 secções, como sugerido pela American Heart Association, examinaram-se os valores médios de T1, para cada secção, de indivíduos sem e com presença de tecido cicatricial; (3) realizou-se uma análise pixel a pixel para investigar se existe uma correlação entre o tecido fibrótico em LGE e as regiões hiperintensas no mapeamento T1 nativo; (4) realizou-se uma análise de textura para estudar se a informação de textura do mapeamento T1 nativo poderia fornecer um diagnóstico diferencial ou informação prognóstica além dos valores médios de T1 nativo. A primeira etapa revelou ser o processo mais demorado e desafiante. O batimento cardíaco e o ciclo respiratório representam dois desafios no registo de imagens cardíacas. Para além dos comuns desafios em alinhamento de imagens cardíacas da mesma modalidade, alinhar imagens de diferentes modalidades torna-se um processo mais complexo. Em primeiro lugar, o registo de imagens T1 e de LGE é dificultado pelos distintos perfis de intensidade das duas modalidades. Em segundo lugar, a aquisição de imagens de Realce Tardio ocorre cerca de 7 minutos após a aquisição do mapeamento T1, e o movimento dos pacientes durante este intervalo de tempo é uma fonte adicional de erro. Diferentes softwares foram utilizados, e uma imagem sintética ponderada em T1 foi criada, com o intuito de apresentar intensidades mais similares à imagem a ser alinhada (imagem de LGE). O registo das máscaras miocárdicas por meio de um modelo com transformações rígida, afim e deformações livres mostrou ser a melhor metodologia a aplicar. Os valores médios de T1 nativo não aumentaram significativamente em pacientes com tecido cicatricial, apesar de haver um aumento dos valores de T1 nativo em determinadas secções, em cortes basais e intermédios. Relativamente ao terceiro objetivo abordado, não foi observada uma clara correlação de intensidades entre as técnicas, o que reforçou a necessidade de uma análise de textura (do inglês, TA). Esta análise revelou as sete melhores características (num total de 350) que distinguem segmentos cardíacos sem e com tecido fibrótico, aplicando um método de Machine Learning (do inglês, ML) que identificou, sequencialmente, as features que adicionavam mais informação ao modelo que distinguia os dois grupos de segmentos. Quatro características de primeira ordem distinguem os segmentos devido à presença de tecido cicatricial - zonas hiperintensas - e três características de textura sugerem que a remodelação fibrótica no miocárdio de pacientes com HCM pode estar associada a uma textura mais heterogénea. Foi implementada uma análise ao desempenho de modelos com as features selecionadas, que revelou que a Cluster Prominence é a característica que melhor distingue secções sem e com tecido fibrótico, apesar de com baixa sensibilidade (65%) e baixa especificidade (64%). Um modelo que analisa o Percentil 90 revelou uma precisão de 64%, sensibilidade de 71% e especificidade de 57%. No estudo da Variância, a precisão foi de 63%, a sensibilidade 66% e a especificidade 60%. As restantes features apresentaram valores de precisão inferiores aos mencionados mas acima de 50%. Um modelo com a combinação das sete features selecionadas não melhorou a performance do modelo (precisão de 62%, sensibilidade de 75% e 49% de especificidade). A baixa sensibilidade e especificidade sugerem que a análise desses valores nessas características pode ajudar os cardiologistas a identificar regiões focais de fibrose e evitar métodos de injeção de contraste, mas pode não fornecer um diagnóstico preciso da presença de tecido fibrótico por si só. Em futuras aquisições, encontrar valores semelhantes nas features acima mencionadas, principalmente na Cluster Prominence, em novos dados, poderia ajudar os cardiologistas a identificar regiões de fibrose focal. Desta forma, não seria necessário analisar imagens de Realce Tardio, o que se traduziria na eliminação de injeção de agentes de contraste. Pesquisas adicionais focadas na correlação do mapeamento T1 nativo e imagens cardíacas de LGE são de extrema importância para desenvolver uma tecnologia independente da injeção de agentes de contraste, que substitua o Realce Tardio

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Characterizing Cardiac Electrophysiology during Radiofrequency Ablation : An Integrative Ex vivo, In silico, and In vivo Approach

Catheter ablation is a major treatment for atrial tachycardias. Hereby, the precise monitoring of the lesion formation is an important success factor. This book presents computational, wet-lab, and clinical studies with the aim of evaluating the signal characteristics of the intracardiac electrograms (IEGMs) recorded around ablation lesions from different perspectives. The detailed analysis of the IEGMs can optimize the description of durable and complex lesions during the ablation procedure

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches