Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.Comment: 23 page

Systemic Amyloidosis – Insights by Cardiovascular Magnetic Resonance

Systemic amyloidosis is the exemplar infiltrative, extracellular disease. Although it is a multi-organ disorder, cardiac involvement drives prognosis. Survival is worst in the AL amyloidosis subtype. It can affect any age and any race. There is no direct test for amyloid burden and there is no treatment for amyloidosis, there is only treatment for the underlying condition. Earlier diagnosis permits prompt treatment and improves survival. A number of imaging modalities exist to non-invasively detect cardiac disease but all have limitations. Cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) imaging provides the highest sensitivity for early detection. However, this also has its shortcomings. There is currently no non-invasive method of directly measuring amyloid burden in the extracellular space. New therapies are pending – but their development needs new surrogate endpoints and new tests are therefore desperately needed. T1 mapping permits tissue abnormalities to be directly visualised in a simple scan – the colour changes being instantly recognisable, either before contrast (pre contrast or native T1 mapping) or after, when the myocardial extracellular volume (ECV) can be measured. In a collaboration between the National Amyloidosis Centre and the Heart Hospital, I explored the possibility and potential that T1 mapping might measure cardiac (and other organ) involvement in systemic amyloidosis using EQ-MRI. In early clinical exploration in systemic AL amyloid, I showed that native myocardial T1 was elevated in cardiac amyloidosis and tracked disease, particularly early disease. Mean pre contrast myocardial T1 as measured by ShMOLLI was higher in patients at 1086 ± 90msec, compared to healthy volunteers of 958 ± 20msec (P 0.9 for both the FLASH IR and ShMOLLI techniques of T1 mapping and good agreement of ECV derived from both techniques. In pilot studies, I also demonstrated by serial scanning that changes (including regression) over time could be measured. In other organs, I showed that the amyloid burden could be measured and was higher in amyloidosis compared to healthy volunteer: ECV 0.32 vs 0.29 (P<0.001) for liver, 0.39 vs 0.34 (P<0.001) for spleen and 0.16 vs 0.09 (P<0.001) for skeletal muscle. These ECVs also tracked current conventional measures of disease severity by nuclear scintigraphy. These results demonstrate that the interstitial volume in patients with systemic AL amyloidosis can be measured non invasively in the heart, liver, spleen and skeletal muscle and that this correlates with existing markers of disease and survival. Pre contrast myocardial T1 was a good alternative measure for the heart. In conclusion, the work in this thesis has enabled a deeper understanding of cardiac amyloidosis, disease processes and stages. It has pioneered a new prognostic marker that is also able to identify some patients with cardiac involvement that were previously unrecognised. Novel subtypes are now recognised (e.g. cardiac amyloidosis with no LVH) and it has also allowed direct quantification of the liver and spleen. ECV is a new and powerful biomarker that has already been adopted by industry allowing development of new therapies and providing hope that an end to the scourge of this disease is near

Computertomographie-basierte Bestimmung von Aortenklappenkalk und seine Assoziation mit Komplikationen nach interventioneller Aortenklappenimplantation (TAVI)

Background: Severe aortic valve calcification (AVC) has generally been recognized as a key factor in the occurrence of adverse events after transcatheter aortic valve implantation (TAVI). To date, however, a consensus on a standardized calcium detection threshold for aortic valve calcium quantification in contrast-enhanced computed tomography angiography (CTA) is still lacking. The present thesis aimed at comparing two different approaches for quantifying AVC in CTA scans based on their predictive power for adverse events and survival after a TAVI procedure.   Methods: The extensive dataset of this study included 198 characteristics for each of the 965 prospectively included patients who had undergone TAVI between November 2012 and December 2019 at the German Heart Center Berlin (DHZB). AVC quantification in CTA scans was performed at a fixed Hounsfield Unit (HU) threshold of 850 HU (HU 850 approach) and at a patient-specific threshold, where the HU threshold was set by multiplying the mean luminal attenuation of the ascending aorta by 2 (+100 % HUAorta approach). The primary endpoint of this study consisted of a combination of post-TAVI outcomes (paravalvular leak ≥ mild, implant-related conduction disturbances, 30-day mortality, post-procedural stroke, annulus rupture, and device migration). The Akaike information criterion was used to select variables for the multivariable regression model. Multivariable analysis was carried out to determine the predictive power of the investigated approaches.   Results: Multivariable analyses showed that a fixed threshold of 850 HU (calcium volume cut-off 146 mm3) was unable to predict the composite clinical endpoint post-TAVI (OR=1.13, 95 % CI 0.87 to 1.48, p=0.35). In contrast, the +100 % HUAorta approach (calcium volume cut-off 1421 mm3) enabled independent prediction of the composite clinical endpoint post-TAVI (OR=2, 95 % CI 1.52 to 2.64, p=9.2x10-7). No significant difference in the Kaplan-Meier survival analysis was observed for either of the approaches.   Conclusions: The patient-specific calcium detection threshold +100 % HUAorta is more predictive of post-TAVI adverse events included in the combined clinical endpoint than the fixed HU 850 approach. For the +100 % HUAorta approach, a calcium volume cut-off of 1421 mm3 of the aortic valve had the highest predictive value.Hintergrund: Ein wichtiger Auslöser von Komplikationen nach einer Transkatheter-Aortenklappen-Implantation (TAVI) sind ausgeprägte Kalkablagerung an der Aortenklappe. Dennoch erfolgte bisher keine Einigung auf ein standardisiertes Messverfahren zur Quantifizierung der Kalklast der Aortenklappe in einer kontrastverstärkten dynamischen computertomographischen Angiographie (CTA). Die vorliegende Dissertation untersucht, inwieweit die Wahl des Analyseverfahrens zur Quantifizierung von Kalkablagerungen in der Aortenklappe die Prognose von Komplikationen und der Überlebensdauer nach einer TAVI beeinflusst.   Methodik: Der Untersuchung liegt ein umfangreicher Datensatz von 965 Patienten mit 198 Merkmalen pro Patienten zugrunde, welche sich zwischen 2012 und 2019 am Deutschen Herzzentrum Berlin einer TAVI unterzogen haben. Die Quantifizierung der Kalkablagerung an der Aortenklappe mittels CTA wurde einerseits mit einem starren Grenzwert von 850 Hounsfield Einheiten (HU) (HU 850 Verfahren) und andererseits anhand eines individuellen Grenzwertes bemessen. Letzterer ergibt sich aus der HU-Dämpfung in dem Lumen der Aorta ascendens multipliziert mit 2 (+100 % HUAorta Verfahren). Der primäre klinische Endpunkt dieser Dissertation besteht aus einem aus sechs Variablen zusammengesetzten klinischen Endpunkt, welcher ungewünschte Ereignisse nach einer TAVI abbildet (paravalvuläre Leckage ≥mild, Herzrhythmusstörungen nach einer TAVI, Tod innerhalb von 30 Tagen, post-TAVI Schlaganfall, Ruptur des Annulus und Prothesendislokation). Mögliche Störfaktoren, die auf das Eintreten der Komplikationen nach TAVI Einfluss haben, wurden durch den Einsatz des Akaike Informationskriterium ermittelt. Um die Vorhersagekraft von Komplikationen nach einer TAVI durch beide Verfahren zu ermitteln, wurde eine multivariate Regressionsanalyse durchgeführt.   Ergebnisse: Die multivariaten logistischen Regressionen zeigen, dass die Messung der Kalkablagerungen anhand der HU 850 Messung (Kalklast Grenzwert von 146 mm3) die Komplikationen und die Überlebensdauer nicht vorhersagen konnten (OR=1.13, 95 % CI 0.87 bis 1.48, p=0.35). Die nach dem +100 % HUAorta Verfahren (Kalklast Grenzwert von 1421 mm3) individualisierte Kalkmessung erwies sich hingegen als sehr aussagekräftig, da hiermit Komplikationen nach einer TAVI signifikant vorhergesagt werden konnten (OR=2, 95 % CI 1.52 bis 2.64, p=9.2x10-7). In Hinblick auf die postoperative Kaplan-Meier Überlebenszeitanalyse kann auch mit dem +100 % HUAorta Verfahren keine Vorhersage getroffen werden.   Fazit: Aus der Dissertation ergibt sich die Empfehlung, die Messung von Kalkablagerungen nach dem +100 % HUAorta Verfahren vorzunehmen, da Komplikationen wesentlich besser und zuverlässiger als nach der gängigen HU 850 Messmethode vorhergesagt werden können. Für das +100 % HUAorta Verfahren lag der optimale Kalklast Grenzwert bei 1421 mm3

The effects of running, cycling, and duathlon exercise performance on cardiac function, haemodynamics and regulation

This thesis examined the effects of prolonged exercise, specifically Olympic Distance (OD)duathlon upon ultrasound derived indices of cardiac function, cardiac autonomic regulation measured via heart rate variability (HRV), and high-sensitivity cardiac troponin T (hs-cTnT)release. The primary aims were to (1) ascertain the influence of Olympic distance (OD) duathlon performance on cardiac function; (2) to investigate potential relationships between autonomic regulation, hs-cTnT release, and cardiac function, and (3) to investigate the effect of the individual legs of an OD duathlon on post-exercise cardiac function and to quantify the potential performance reserve of highly-trained endurance athletes when completing standalone legs of the duathlon. Findings from a systematic review and meta-analysis(Chapter 1) on research that performed serial echocardiographic and troponin measurements before and after exercise, intensity predicted changes in post-exercise cardiac troponin release and diastolic function. The findings agreed with previous meta-analyses using a more recent sample of studies; however, the recommendation for future studies to implement advanced cardiac imaging techniques, such as myocardial speckle tracking into their data collection would provide a more sensitive measure of post-exercise cardiac function. Whilst a large degree of heterogeneity in the results exists, this was in part explained by study exercise heart rate, participant age, and the prevalence of cardiac troponin release above the clinical detection threshold. The study performed in Chapter 3 was the first to investigate the effects of OD duathlon exercise on immediate and 24 hours post-exercise cardiac function. Additionally, a second OD duathlon was performed by participants with intra-leg measurements of cardiac function. In a highly trained cohort, there was evidence of transient post-exercise reductions in cardiac function and elevated serum high-sensitivity cardiac troponin T (hs-cTnT) above the clinical reference value, which was largely resolved within 24h of recovery. This study also demonstrated the reliability of lab-based duathlon exercise in a highly trained cohort and identified the pacing features of experienced multi-sport athletes that partially explained the different findings between the running and cycling legs of the duathlon. By investigating each leg of the duathlon individually (10k run, 5k run, 40k cycle), both at duathlon race-pace (DM) and maximal (Max) intensity on separate occasions, the performance reserve of the highly-trained cohort was quantified and further explored. The studies presented in Chapters 4 and 5 revealed that experienced duathletes were able to improve their speed across each leg by between 5-15% in a laboratory setting, compared to the duathlon effort. Additionally, the maximal effort 10k run leg provoked the most persistent changes to cardiac function that were present at 6h of recovery. Changes in cardiac function post DM 10k confirmed the findings of Chapter 3 that the greatest magnitude of cardiac perturbations occur following the initial 10k run leg. Aside from the Max 10k run and 40k cycle trials, all perturbations had resolved within 6h of recovery after each bout of exercise, highlighting the importance of recovery following maximal intensity efforts. The lack of 6h and 24h recovery data in Chapter 4, and Chapters 5 and 6, respectively is a shortcoming of these findings and therefore limits interpretation in the context of providing athletic guidance. Future research in this area should endeavour to include 6h and 24h recovery measures as standard, as multi-sport athletes typically perform multiple daily training sessions. The implications of substantial cardiac fatigue accumulation over many years of endurance training history are still unclear, and athletes may benefit from preventingits occurrence

Right ventricular biomechanics in pulmonary hypertension

As outcome in pulmonary hypertension is strongly associated with progressive right ventricular dysfunction, the work in this thesis seeks to determine the regional distribution of forces on the right ventricle, its geometry, and deformations subsequent to load. This thesis contributes to the understanding of how circulating biomarkers of energy metabolism and stress-response pathways are related to adverse cardiac remodelling and functional decompensation. A numerical model of the heart was used to derive a three-dimensional representation of right ventricular morphology, function and wall stress in pulmonary hypertension patients. This approach was tested by modelling the effect of pulmonary endarterectomy in patients with chronic thromboembolic disease. The relationship between the cardiac phenotype and 10 circulating metabolites, known to be associated with all-cause mortality, was assessed using mass univariate regression. Increasing afterload (mean pulmonary artery pressure) was significantly associated with hypertrophy of the right ventricular inlet and dilatation, indicative of global eccentric remodelling, and decreased systolic excursion. Right ventricular ejection fraction was found to be negatively associated with 3-hydroxy-3-methylglutarate, N-formylmethionine, and fumarate. Wall stress was related to all-cause mortality and its decrease after pulmonary endarterectomy was associated with a fall in brain natriuretic peptide. Six metabolites were associated with elevated end-systolic wall stress: dehydroepiandrosterone sulfate, N2,N2-dimethylguanosine, N1-methylinosine, 3-hydroxy-3-methylglutarate, N-acetylmethionine, and N-formylmethionine. Metabolic profiles related to energy metabolism and stress-response are associated with elevations in right ventricular end-systolic wall stress that have prognostic significance in pulmonary hypertension patients. These results show that statistical parametric mapping can give regional information on the right ventricle and that metabolic phenotyping, as well as predicting outcomes, provides markers informative of the biomechanical status of the right ventricle in pulmonary hypertension.Open Acces

Electrocardiographic predictors of clinical outcome in ST-elevation myocardial infarction

Malignant ventricular arrhythmias, particularly ventricular fibrillation (VF), remain an important contributor to mortality in ST-elevation myocardial infarction (STEMI). The size of myocardial injury is one more important factor influencing the prognosis of STEMI patients. The search for new non-invasive markers, which can be relatively simply calculated using conventional ECG recording and can predict the degree of myocardial injury and impending VF, is promising. This work is aimed at investigating cardiac repolarization and depolarization abnormalities and predictors and prognosis of ventricular arrhythmias during the course of STEMI. The thesis is composed of the experimental part (Studies I, II, III) and clinical register-based retrospective studies (Studies IV and V). Closed-chest porcine model of myocardial infarction (MI) was used in the experimental part. Occlusion of left descending artery (LAD) lasted 40 minutes and was followed by reperfusion, and ECG was continuously recorded. QRSduration and morphology, dynamics of ST-segment and T-wave alternans (TWA) were calculated, and myocardial area at risk (MaR) and infarct size (IS) were assessed. Predictors and prognostic impact of early VF in STEMI was assessed in a register-based study of 1,718 consecutive patients admitted for primary PCI during 2007-2009 who were followed up for one year. In experimental MI, the maximal level of TWA during occlusion period was associated with both MaR and IS (Study II). Rapid and marked transient increase in QRS duration associated with appearance of J-wave pattern predicted impending VF in acute ischemia (Study III). Restoration of blood flow in infarct-related artery was accompanied by reperfusion peak in all animals, and the magnitude of ST elevation at reperfusion peak was associated with infarct size (Study I). In clinical studies IV and V, the risk of VF in acute period of STEMI was higher in patients with MI history, cardiovascular risk factors such as smoking and left main stenosis, resulting in a large infarct area. Besides MI history and left main stenosis, the risk of VF at reperfusion was associated with inferior localization of STEMI, hypokalemia, high ST-elevation and shorter symptom-toballoon time. The magnitude of ST-elevation before PCI for STEMI independently predicted reperfusion VF. Patients successfully resuscitated after VF and alive at 48 hours had higher in-hospital mortality (12% vs. 2%, p<0.001). However, in VF patients who were discharged alive, 1-year mortality did not differ compared with patients without V

Machine learning approaches to model cardiac shape in large-scale imaging studies

Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces

Genetic association of high-dimensional traits

Over the past ten years, more than 4,000 genome-wide association studies (GWAS) have helped to shed light on the genetic architecture of complex traits and diseases. In recent years, phenotyping of the samples has often gone beyond single traits and it has become common to record multi- to high-dimensional phenotypes for individu- als. Whilst these rich datasets offer the potential to analyse complex trait structures and pleiotropic effects at a genome-wide level, novel analytic challenges arise. This thesis summarises my research into genetic associations for high-dimensional phen- otype data. First, I developed a novel and computationally efficient approach for multivari- ate analysis of high-dimensional phenotypes based on linear mixed models, com- bined with bootstrapping (LiMMBo). Both in simulation studies and on real data, I demonstrate the statistical validity of LiMMBo and that it can scale to hundreds of phenotypes. I show the gain in power of multivariate analyses for high-dimensional phenotypes compared to univariate approaches, and illustrate that LiMMBo allows for detecting pleiotropy in a large number of phenotypic traits. Aside from their computational challenges in GWAS, the true dimensionality of very high-dimensional phenotypes is often unknown and lies hidden in high-dimen- sional space. Retaining maximum power for association studies of such phenotype data relies on using an appropriate phenotype representation. I systematically ana- lysed twelve unsupervised dimensionality reduction methods based on their per- formance in finding a robust phenotype representation in simulated data of different structure and size. I propose a stability criteria for choosing low-dimensional phen- otype representations and demonstrate that stable phenotypes can recover genetic associations. Finally, I analysed genetic variants for associations to high-dimensional cardiac phenotypes based on MRI data from 1,500 healthy individuals. I used an unsuper- vised approach to extract a low-dimensional representation of cardiac wall thickness and conducted a GWAS on this representation. In addition, I investigated genetic associations to a trabeculation phenotype generated from a supervised feature ex- traction approach on the cardiac MRI data. In summary, this thesis highlights and overcomes some of the challenges in per- forming genetic association studies on high-dimensional phenotypes. It describes new approaches for phenotype processing, and genotype to phenotype mapping for high-dimensional datasets, as well as providing new insights in the genetic structure of cardiac morphology in humans.European Molecular Biology Laborator

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)