Atrial fibrillation is one of the most frequent cardiac arrhythmias in the industrialized world and ablation therapy is the method of choice for many patients. However, ablation scars alter the electrophysiological activation and the mechanical behavior of the affected atria. Different ablation strategies with the aim to terminate atrial fibrillation and prevent its recurrence exist but their impact on the hemodynamic performance of the heart has not been investigated thoroughly. In this work, we present a simulation study analyzing five commonly used ablation scar patterns and their combinations in the left atrium regarding their impact on the pumping function of the heart using an electromechanical whole-heart model. We analyzed how the altered atrial activation and increased stiffness due to the ablation scar affect atrial as well as ventricular contraction and relaxation. We found that systolic and diastolic function of the left atrium is impaired by ablation scars and that the reduction of atrial stroke volume of up to 11.43% depends linearly on the amount of inactivated tissue. Consequently, the end-diastolic volume of the left ventricle, and thus stroke volume, was reduced by up to 1.4% and 1.8%, respectively. During ventricular systole, left atrial pressure was increased by up to 20% due to changes in the atrial activation sequence and the stiffening of scar tissue. This study provides biomechanical evidence that atrial ablation has acute effects not only on atrial contraction but also on ventricular pumping function. Our results have the potential to help tailoring ablation strategies towards minimal global hemodynamic impairment.
Objective: The bidomain model and the finite element method are an established standard to mathematically describe cardiac electrophysiology, but are both suboptimal choices for fast and large-scale simulations due to high computational costs. We investigate to what extent simplified approaches for propagation models (monodomain, reaction-eikonal and eikonal) and forward calculation (boundary element and infinite volume conductor) deliver markedly accelerated, yet physiologically accurate simulation results in atrial electrophysiology. Methods: We compared action potential durations, local activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm simulations on healthy and fibrotically infiltrated atrial models. Results: All simplified model solutions yielded LATs and P waves in accurate accordance with the bidomain results. Only for the eikonal model with pre-computed action potential templates shifted in time to derive transmembrane voltages, repolarization behavior notably deviated from the bidomain results. ECGs calculated with the boundary element method were characterized by correlation coefficients >0.9 compared to the finite element method. The infinite volume conductor method led to lower correlation coefficients caused predominantly by systematic overestimations of P wave amplitudes in the precordial leads. Conclusion: Our results demonstrate that the eikonal model yields accurate LATs and combined with the boundary element method precise ECGs compared to markedly more expensive full bidomain simulations. However, for an accurate representation of atrial repolarization dynamics, diffusion terms must be accounted for in simplified models. Significance: Simulations of atrial LATs and ECGs can be notably accelerated to clinically feasible time frames at high accuracy by resorting to the eikonal and boundary element methods.
The assessment of craniofacial deformities requires patient data which is sparsely available. Statistical shape models provide realistic and synthetic data enabling comparisons of existing methods on a common dataset. We build the first publicly available statistical 3D head model of craniosynostosis patients and the first model focusing on infants younger than 1.5 years. For correspondence establishment, we test and evaluate four template morphing approaches. We further present an original, shape-model- based classification approach for craniosynostosis on photogrammetric surface scans. To the best of our knowledge, our study uses the largest dataset of craniosynostosis patients in a classification study for craniosynostosis and statistical shape modeling to date. We demonstrate that our shape model performs similar to other statistical shape models of the human head. Craniosynostosis-specific pathologies are represented in the first eigenmodes of the model. Regarding the automatic classification of craniosynostis, our classification approach yields an accuracy of 97.3 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Our publicly available, craniosynostosis-specific statistical shape model enables the assessment of craniosynostosis on realistic and synthetic data. We further present a state-of-the-art shape-model- based classification approach for a radiation-free diagnosis of craniosynostosis.
C. Nagel, M. Schaufelberger, O. Dössel, and A. Loewe. A Bi-atrial Statistical Shape Model as a Basis to Classify Left Atrial Enlargement from Simulated and Clinical 12-Lead ECGs. In Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, vol. 13131, pp. 38-47, 2022
Left atrial enlargement (LAE) is one of the risk factors for atrial fibrillation (AF). A non-invasive and automated detection of LAE with the 12-lead electrocardiogram (ECG) could therefore contribute to an improved AF risk stratification and an early detection of new-onset AF incidents. However, one major challenge when applying machine learning techniques to identify and classify cardiac diseases usually lies in the lack of large, reliably labeled and balanced clinical datasets. We therefore examined if the extension of clinical training data by simulated ECGs derived from a novel bi-atrial shape model could improve the automated detection of LAE based on P waves of the 12-lead ECG. We derived 95 volumetric geometries from the bi-atrial statistical shape model with continuously increasing left atrial volumes in the range of 30 ml to 65 ml. Electrophysiological simulations with 10 different conduction velocity settings and 2 different torso models were conducted. Extracting the P waves of the 12-lead ECG thus yielded a synthetic dataset of 1,900 signals. Besides the simulated data, 7,168 healthy and 309 LAE ECGs from a public clinical ECG database were available for training and testing of an LSTM network to identify LAE. The class imbalance of the training data could be reduced from 1:23 to 1:6 when adding simulated data to the training set. The accuracy evaluated on the test dataset comprising a subset of the clinical ECG recordings improved from 0.91 to 0.95 if simulated ECGs were included as an additional input for the training of the classifier. Our results suggest that using a bi-atrial statistical shape model as a basis for ECG simulations can help to overcome the drawbacks of clinical ECG recordings and can thus lead to an improved performance of machine learning classifiers to detect LAE based on the 12-lead ECG.
Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.
D. Nairn. Multi-Modality Correspondence to Enhance Arrhythmogenic Atrial Substrate Identification: Guiding Persistent Atrial Fibrillation Ablation Therapy. Karlsruher Institut für Technologie (KIT). Dissertation. 2022
Atrial fibrillation (AF) is one of the leading health challenges posing a significant burden not only to patients but also to the health care systems. While pulmonary vein isolation (PVI) is an effective therapy for paroxysmal AF patients, the success rate drops for patients with persistent AF. This is thought to be due to patients exhibiting atrial cardiomyopathy (ACM), specifically structural remodelling in the atria occurring during the progression of AF. Therefore, persistent AF patients exhibit additional pathological substrate in the atria, which maintains the arrhythmia. Unfortunately, the current approaches performing PVI plus additionally targeting the pathological substrate are still sub-optimal, with only 50-70\% of patients having long-term freedom from AF after catheter ablation. Hence, the optimal ablation strategy remains an open question demanding further research to identify promising ablation targets. Two approaches that have gained attention over the recent years are electro-anatomical mapping specifically targeting low voltage areas and areas showing contrast in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI). However, both are hindered by the lack of consensus regarding a precise method to identify the pathological substrate. Identification via low voltage mapping is limited due to a lack of understanding of the impact of catheter characteristics that influence the voltage aside from the pathological substrate. Additionally, voltage mapping can be performed during sinus rhythm (SR) or AF. Mapping in the latter case is beneficial as it reduces the need for potentially multiple cardioversions. However, there is no precise statistical evaluation for the cut-off values applied to determine low voltage areas. The advantage of using LGE-MRI instead is that it is a less invasive diagnostic method. However, the spatial resolution of LGE-MRI is limited. Moreover, the degree of accordance between MRI and voltage mapping to detect fibrosis remains disputed. The overall goal of this thesis is to compare mapping modalities to address the fore-mentioned limitations. Therefore, providing more robust and accurate methods to identify pathological substrate areas known for the maintenance of atrial fibrillation.In the first project, 28 persistent AF patients undergoing electro-anatomical mapping were studied. Statistical analysis was then applied, comparing each patient's bipolar and unipolar voltage maps. Specifically, the extent of agreement between methods was identified, finding the optimal unipolar thresholds to locate pathological substrate as determined by the bipolar voltage map. Additionally, the impact of the inter-electrode distances and regional discrepancies on the comparability was explored. For the second part of the project, simulations modelling electrodes of different sizes on a 2D patch and a lasso catheter in a 3D left atrial geometry were performed. This work identified that while the catheter characteristics influence the bipolar voltage values, they do not play a significant role in altering the location of the low voltage areas. The identified unipolar thresholds, which relate the bipolar and unipolar map, can help determine the extent of pathological substrate in an area. Additionally, it was found that larger electrodes deliver smaller voltages, providing techniques to compare results across studies and centres. In the second project, a patient cohort where patients underwent electro-anatomical mapping while in SR and AF was used. The two rhythms could then be compared in each patient, and AF global and regional thresholds relating the rhythms could be identified. Additionally, the effects of inducing AF in patients could be explored and the benefits of different voltage calculation methods analysed. Low voltage thresholds that can better relate mapping in AF with SR were proposed. It was identified that using the regional thresholds proposed in this work could help prevent a false representation of the extent of pathological substrate within an area. Furthermore, using the maximum voltage value in a signal will lead to higher concordance between methods and using a variability measure (sample entropy) can help identify complex propagation patterns distorting the signals in AF. Finally, the last project studied 36 patients who underwent both LGE-MRI and electro-anatomical mapping. Using this cohort, the concordance between different LGE-MRI mapping modalities and voltage and conduction velocity mapping could be investigated. Additionally, a new LGE-MRI analysis method could be developed to improve the agreement between the modalities. Spatial histograms showing typical low voltage and slow conduction regions were created in this work to help clinicians identify important regions to map during a procedure. Moreover, important discrepancies were found between methods, specifically on the posterior wall, which needs further investigation. Lastly, a new LGE-MRI thresholding method was developed, which could be used to identify patients with ACM. Therefore, providing a non-invasive approach which can help to determine whether additional mapping is needed in patients besides performing PVI. The work presented in this thesis provides the clinical community with a deeper understanding of how the different methods to identify pathological substrate compare. Additionally, providing techniques to relate the methods, account for variability between centres and potentially reduce procedure times. Moreover, it was identified that perhaps one-size-fits-all ablation strategies is limited. Thus, this thesis supports the implementation of more personalised ablation approaches.
J. P. Sánchez Arciniegas. A Multiscale In Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation : A Translational Study to Guide Ablation Therapy. KIT Scientific Publishing. Dissertation. 2022
Atrial fibrillation is the most common cardiac arrhythmia. During atrial fibrillation, the atrial substrate undergoes a series of electrical and structural remodeling processes. The electrical remodeling is characterized by the alteration of specific ionic channels, which changes the morphology of the transmembrane voltage known as action potential. Structural remodeling is a complex process involving the interaction of several signalling pathways, cellular interaction, and changes in the extracellular matrix. During structural remodeling, fibroblasts, abundant in the cardiac tissue, start to differentiate into myofibroblasts, which are responsible for maintaining the extracellular matrix structure by depositing collagen. Additionally, myofibroblasts paracrine signalling with surrounding myocytes will also affect ionic channels. Highly detailed computational models at different scales were used to study the effect of structural remodeling induced at the cellular and tissue levels. At the cellular level, a human fibroblast model was adapted to reproduce the myofibroblast electrophsyiology during atrial fibrillation. Additionally, the calcium handling in myofibroblast electrophysiology was assessed by fitting a calcium ion channel to experimental data. . At the tissue level, myofibroblast infiltration was studied to quantify the increase of vulnerability to cardiac arrhythmia. Myofibroblasts alter the dynamics of reentry. A low density of myofibroblasts allows the propagation through the fibrotic area and creates focal activity exit points and wave breaks inside this area. Moreover, fibrosis composition plays a key role in the alteration of the propagation pattern. The alteration of the propagation pattern affects the electrograms computed at the surface of the tissue. Electrogram morphology was altered depending on the arrangement and composition of the fibrotic tissue. Detailed cardiac tissue models were combined with realistic models of the commercially available mapping catheters to understand the clinically recorded signals. A noise model from clinical signals was generated to reproduce the signal artifacts in the model. Electrograms from highly detailed bidomain models were used to train a machine learning algorithm to characterize the atrial fibrotic substrate. Features that quantify the complexity of the signals were extracted to identify fibrotic density and fibrotic transmurality. Subsequently, fibrosis maps were generated using patient recordings as a proof of concept. A fibrosis map provides information about the fibrotic substrate without using a single cut-off voltage value of 0.5 mV. Furthermore, in this study, using information theory measurements such as transfer entropy combined with directed graphs, the wave propagation direction was tracked. Transfer entropy with directed graphs provides crucial information during electrophysiology to understand wave propagation dynamics during atrial fibrillation. In conclusion, this thesis presents a multiscale in silico study of atrial fibrillation mechanisms providing insight into the cellular mediators responsible for the extracellular matrix remodeling and its electrophysiology. Additionally, it provides a realistic setup to create in silico data that can be translated to clinical applications that could support ablation treatment.
S. F. Schuler. Novel Methods to Incorporate Physiological Prior Knowledge into the Inverse Problem of Electrocardiography - Application to Localization of Ventricular Excitation Origins. Karlsruher Institut für Technologie (KIT). Dissertation. 2022
17 million deaths a year worldwide are linked to cardiovascular diseases. Sudden cardiac death is one result in approximately 25% of all patients with cardiovascular diseases and may be connected to ventricular tachycardia. When treating the ventricular tachycardia with a catheter intervention, the detection of the so-called exit points, i.e. the spatial origin of the excitation, is a crucial step. As this procedure is very time consuming and skilled cardiologists are required, there is a need for assisting localization procedures, preferably automatic and non-invasive ones. Electrocardiographic imaging tries to meet these needs by reconstructing the electrical activity of the heart from body surface potential measurements. The resulting information can be used to reconstruct the excitation origin. However, current methods for solving this inverse problem show either low precision or poor robustness, which limits their clinical utility. This work first analyzes the forward problem in combination with two source models: transmembrane voltages and extracellular potentials. The mathematical properties of the relation between sources on the heart and the body surface potentials are analyzed systematically and the impact on the inverse problem is explained and visualized. Subsequently, this knowledge is used to solve the inverse problem. Three novel methods are introduced: Delay-based regularization, body surface potential regression, and deep learning-based localization. These three methods are compared to four state-of-the-art methods using one simulated and two clinical datasets. On the simulated as well as one clinical dataset, one of the novel methods outperformed the existing approaches, whereas on the remaining clinical dataset, Tikhonov regularization performed best. Potential reasons for these results are discussed and related to properties of the forward problem.
L. Baron. In Silico Modeling, Simulation and Optimization of Human Cardiac Motion. Karlsruher Institut für Technologie (KIT). Dissertation. 2022
Cardiac diseases are the number one reasons for death in the western world. Computa- tional simulations provide the opportunity to conduct experiments and predictions that are not possible in humans due to ethical and other reasons. High performance computa- tion allows the use of demanding coupled computational models of high complexity and a high level of detail, complying with a wide range of experimental data from the human heart. In this thesis, different aspects of computational heart modeling are covered: models describing passive tissue behavior, active contractile behavior, circulatory system modeling, influences of the pericardium and surrounding tissue on the heart as well as methods to obtain suitable parameters for these models. For each aspect, several modeling approaches are presented and compared. Finally, a scalability evaluation of the highly-parallelized implementation and an evaluation of the proper choice of mesh resolution for credible numerical results are covered. Concludingly, this thesis allows the reader to gain insights into the complexity of computational heart modeling and to make an appropriate choice of models and parameters suitable for specific applications.
Student Theses (7)
M. A. Vu. Analyse und Korrektur der Verzerrungseffekte an der Cornea in OCT-Bildern. Institut für Biomedizinische Technik, Karlsruher Institut für Technologie (KIT). Bachelorarbeit. 2022
Die optische Kohärenztomographie (OCT) ist ein nicht-invasives Bildgebungsverfahren zurVisualisierung von Gewebestrukturen und wird insbesondere in der Ophthalmologie, derAugenheilkunde, eingesetzt. Die ausgegebenen Bilddaten unterliegen aufgrund der Funktionsweiseder OCT und der Eigenschaften von Licht als Informationsträger diversen optischenVerzerrungseffekten. Unter anderem an der Cornea werden diese Phänomene sichtbar. Dieserschwert eine messtechnische Nutzung der Informationen, welche für die Diagnose vonKrankheiten und für den Einsatz von Assistenzsystemen bedeutsam ist. Ziel dieser Arbeit istdie Analyse und Korrektur dieser Verzerrungseffekte.Bei den auftretenden Effekten handelt es sich um die Darstellung in optischen Weglängen,die Brechung an Grenzflächen zwischen Medien mit unterschiedlicher optischer Dichtesowie die Verzerrung aufgrund der Scangeometrie des verwendeten Operationsmikroskops.Im Korrekturalgorithmus wird zuerst eine Segmentierung vorgenommen, um die Grenzflächender verschiedenen optischen Medien im Bild zu identifizieren. Zur weiteren Vorbereitungder Daten erfolgt eine Vorskalierung dieser. Für jeden der identifizierten Effekte wirdeine Korrekturmethodik entwickelt. Die Korrekturschritte bauen jeweils aufeinander aufund werden im Gesamten in den Algorithmus eingeordnet. Anschließend werden möglicheFehlerquellen identifiziert und deren Einfluss auf den Korrekturalgorithmus wird bewertet.Es wurde herausgefunden, dass eine Positionierung des zu untersuchenden Objekts abweichendeiner zentralen Ausrichtung kompensiert werden kann, indem die Bilddaten mithilfeeiner Verschiebung angepasst werden. Durch diese Kompensation kann der Einfluss derTranslation auf die Verzerrung verursacht durch die Scangeometrie verringert werden. Darüberhinaus zeigt die Bestimmung geometrischer Maße aus den korrigierten Bildern vonplankonvexen Modelllinsen, dass die Genauigkeit der Ergebnisse vom betrachteten Maßabhängt. Zudem deutet der Vergleich mit den Ergebnissen aus der Literatur darauf hin, dassaufgrund einiger fehlender Parameter des Strahlengangs die Korrektur der Scangeometrieeiner Optimierung bedarf. Zuletzt wird der Einfluss einer plankonvexen Linse auf die dargestelltePosition eines Instruments untersucht. Eine Gegenüberstellung der Betrachtung einerKanülenspitze mit und ohne Linse zeigt eine Abweichung von unter 100 μm auf.Zusammenfassend konnte gezeigt werden, dass auf Basis einer Analyse von Verzerrungseffekten in OCT-Bildern ein Algorithmus mithilfe der Methoden der Bildverarbeitung entwickeltwerden kann. Dies ermöglicht die Korrektur dieser Effekte und fördert die messtechnischeNutzung von OCT. Die dabei erzielte Genauigkeit hängt von dem Anwendungsfall ab.Zusammenfassend konnte gezeigt werden, dass auf Basis einer Analyse von Verzerrungseffektenin OCT-Bildern ein Algorithmus mithilfe der Methoden der Bildverarbeitung entwickeltwerden kann. Dies ermöglicht die Korrektur dieser Effekte, sodass durch Bildnachverarbeitunggeometrische Informationen deutlich genauer ermittelt werden können. Die dabeierzielte Genauigkeit hängt von dem Anwendungsfall ab.
L. Haide. Simulation of Lens Capsule Tearing for Reinforcement Learning in Automated Capsulorhexis. Institute for Anthropomatics and Robotics; Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2022
This work deals with the creation of a simulation environment for capsulorhexis, a particular step of cataract surgery. This simulation environment is prepared for the usage in Reinforcement Learning in order to automate the capsulorhexis. The simulation ad- dresses three main challenges of capsulorhexis: The elastic deformation of the capsular bag, the interaction between a surgical tool and the capsule, and the stress-based tearing. The capsule is modeled with the finite element method, allowing for an employment of material parameters that are based on real-world properties of the capsule. The simulation is implemented with the SOFA framework.The simulation is embedded into the context of Reinforcement Learning by creating an interface to a Reinforcement Learning agent. This interface allows the extraction of observations from the simulation and an application of actions with the surgical tool. With the interface, the simulation can be used as a training environment by future Reinforce- ment Learning agents.Finally, the resulting simulation is validated with tearing experiments on the Simul- Eye Simulorhexis, a commercially available human eye phantom. These tearing experi- ments are recreated in simulation. The tearing behavior is compared by detailed measure- ments. A major contribution of this work is that it validates the tearing of the capsular bag in simulation with measurements at experiments on the phantom eye. This allows for a systematic evaluation of the accuracy of the simulation. The results show a low deviation between the tearing in the simulation and on the phantom setup.
H. Welle. Parameter Optimisation for Eikonal Simulations of Ventricular Activation Based on 12-lead Electrocardiograms from a Clinical Cohort. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2022
In silico studies of ventricular electrophysiology bear the potential to produce a vast amount of synthetic electrocardiograms (ECGs) required for big data and machine learning approaches to identify cardiovascular pathologies. For large-scale simulations of ventricular ECGs, simulation parameters applicable to a variety of anatomical models need to be found such that the synthetic data resemble clinically observed recordings with high fidelity.In the thesis at hand, parameters for simulating ventricular activation by solving the Eikonal equation were optimised. For this purpose, 11,705 clinical 12-lead ECGs were filtered, annotated and cut, to generate representative QRS complex templates for each lead and observation. Those were then aligned, keeping inter-lead signal shifts, before reducing the dataset to its principal components (PCs). The suitability of the PCs to represent signal char- acteristics was validated through a classification distinguishing the healthy and pathological subjects of the dataset.Subsequently, ventricular activation parameters for a simplified representation of the His- Purkinje network were optimised. Those parameters include conduction velocities of the subendocardium and the myocardium, anisotropy ratio of the myocardial fibres as well as number, location, size and activation delay of initially activated regions (IARs) on the suben- docardium. The optimisation was conducted on the mean shape geometry of a ventricular shape model employing ventricular coordinates to define IARs geometry independent. ECGs were derived from the body surface potentials using the boundary-element method (BEM). The optimised objective function was based on the L2 error norm between the original signal and the reconstructed signal after projection to 20 PCs of the healthy clinical cohort. An evolutionary algorithm (EA) and a Bayesian optimisation algorithm were developed to opti- mise the parameters. Robustness was assessed by investigating the influence of ventricular geometry variations....
E. Oberschulte genannt Beckmann. A novel low-energy pacing approach to control Atrial Fibrillation induced by a phase singularity distribution. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Bachelorarbeit. 2022
Atrial fibrillation (AF) is the most common cardiac arrhythmia among cardiovascular diseases. It has a significant number of cases which lead to long-term damage or death. Due its complexity in comparison to other cardiovascular diseases it is poorly understood and there is no personalized successful therapy which terminates AF in every case. Current AF treatments are either painful (cardioversion), have major side effects (drugs) or are invasive methods in need of operation (ablation). Low-energy pacing is an approach to terminate AF using a septal pacing electrode which operates with stimulus pulses below the pain threshold. This project proposes a new pacing protocol to terminate AF. In comparison to other pacing protocol it does not use atrial fibrillation cycle length (AFCL) statistics which are currently only available in simulations and not in clinical environment. The proposed LAT informed pacing uses the Last Activation Time (LAT) at the pacing locations where the pacing electrodes are located. The other pacing protocol (mAFCL pacing) used the AFCL statistics of the previously induced AF and did not use the LAT of the stimulus location. The success rate of the two pacing protocols was compared regarding their success rate on their ability to continuously excite the atrial tissue around the pacing location without an interfering AF wave (local capture). The pacing duration for each simulation was 10 s. LAT informed pacing had a local capture success rate of 70.3 % (n = 384) and mAFCL pacing a success rate of 13.3 % (n = 30). This work demonstrated that the success of a low-energy pacing protocol might not rely on information of the AFCL and could improve by using the LAT of the pacing location which is already measurable by current pacemakers.
M. Yu. Study of the intra-cardiac electrogram signals using in-silico experiments to asses the impact of the atrial geometry. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2022
Atrial fibrillation is a common disease among the elderly. Atrial fibrillation is an irregular and often rapid heart rate. Ectopic activity could trigger atrial fibrillation, which induces fast pacing to create fibrotic regions. A treatment of AF is catheter ablation guided by electro-anatomical mapping. Computational modeling and in-silico experiments are an area that is continuing to grow, aiding in the understanding of electrophysiology and the relation between electrical propagation and intracardiac signals. 2D tissue patches simulations have been useful to understand the link between the depolarization propagation in the cardiac tissue and its corresponding electrograms. In previous studies, for 2 mm and 6 mm distant electrodes in simulated and clinical data, the value of bipolar electrogram changes depending on the direction of the catheter. The bipolar amplitudes are minimized when the wavefront propagation is perpendicular to the electrode pair. However, the influence a realistic geometry and a deformed catheter (reproducing a clinical procedure) has on the intracardiac signal has not been studied in detail. To explain why the directional dependence of bipolar signals is not passed on to bipolar voltage mapping, here is a hypothesis: in the clinical setting of the 1–5mm thick atrial wall  with multiple layers of myocardial fibers, instead, the waves always contain some degree of curvature in the three-dimensional space, so that the electrodes do not receive the signal at the same time point. In this work, to test this conjecture and observe the effect of the EGM signal by the endocardium of atrial geometry, a new set of algorithms extracting EGM signals and calculating wavefront angles in the human atria has been developed and implemented. For the 3D simulation, 2 different sets of simulations were created, with three different atrium catheter models in each set. The ground potential was selected in the blood mesh near the left atrial appendage. The stimulus point of the first simulation is chosen near Bachmann’s bundle, and the stimulus point of the second simulation is chosen near coronary sinus. So there are 6 groups of simulations in 3-dimensional geometry. For the 2D simulation, the stimulus point is selected in one corner of the patch, and the ground potential is selected in the blood mesh of the other corner. By comparing the unipolar amplitudes in the seven sets of data, the difference of in- terquartile range of the 3D unipolar is 1.11 mV larger than that of the 2D, indicating that the 3D unipolar does change according to the atrium geometry. 11 cases(18.3%) of a total of 60 3-dimensional simulations, bipolar amplitudes also appear to be 0, seven of these(63.6%) were where neither the two electrodes in one pair are not in contact with atrium. In other words, in the 3-dimensional simulations the wavefront can also appear at the same time to a pair of electrodes. The wave is always curved when it propagates on the 3D atrium, and there is no approx- imate planar wave. The curvature of the wavefront does not affect the bipolar amplitudes or the difference of activation times. But a variation in curvature, may indicate that several waves are transmitted to this electrode pair from different directions. It can be concluded from this paper that simulating a more realistic geometry helps to understand the true pattern of propagation within the atrium.
C. Kaiser. Ray-based Assessment of Craniosynostosis: Classification, Data Augmentation and Feature Analysis. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2022
Craniosynostosis is a congenital disease characterised by the premature closure of one or multiple sutures of the infant’s skull. Early diagnosis is crucial as it reduces possible damage to the brain and allows the usage of less invasive surgery. For the assessment, computer tomography scans are the gold standard. A promising, radiation-free alternative is the usage of 3D-photogrammetric scans, which provide a fast way to capture the shape of the head. In this work, an existing CNN-based classifier was improved substantially and compared to competing classical machine-learning-based classification approaches, two data augmenta- tion methods were evaluated in an environment of data-scarcity, and a feature analysis was performed to analyse the classification decisions in the CNN. Using a ray-based approach, distances between a central point and the surface of a triangular mesh of the photogrammetric scan were extracted and used as input features for classifi- cation models. Hyperparameters for training were optimised resulting in a classification improvement from 87 to 95%. Using a fine-tuning paradigm, in which all the weights can be adjusted, was identified as the main contributor to the improved accuracy. Different network architectures and comparison with classical machine learning approaches identified the Resnet18 as one of the optimal classifiers. To test data augmentation methods, a simulated case of data scarcity was created, by de- creasing the amount of training data. Two generative models (Statistical Shape Models and conditional deep convolution generative adversarial neural network) and one traditional data augmentation method (horizontal flipping) were incorporated into the test scenario. Anal- yses with two metrics (accuracy and F1-score) showed that no data augmentation showed consistent improvement to the model and revealed generally a little influence with respect to the classifiers. On purely synthetic training data, both methods failed to reproduce the original scores. Especially the generative adversarial network failed to capture the features of the training data. To further optimise the classifier , the number of rays could be reduced from 50176 to 784 while still achieving an accuracy of over 95% leading to a substantial speed-up in image generation. Using the integrated gradients to see which rays contributed the most to the classification models’ decision did not show a direct and clear correlation to each of the four classes. However, it revealed that areas prone to possible overfitting, like the ears, did not influence the final decision.
M. Franz. Simulation and analysis of fluid dynamic effects in the presence of plaque in the coronary arteries. Institute of Fluid Mechanics; Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Bachelorarbeit. 2022
The coronary arteries supply the heart muscle with oxygenated blood to ensure a proper heart function. When this process is disturbed, it can have detrimental impacts on the physical conditions of the human body and can be lethal in many cases. One main reason for the malfunction of the blood sustenance of the heart can be a coronary myocardial infaction which typically occurs in connection with plaque depositions within the coronary arteries. The label plaque is a hypernym for material depositions e.g. calcium and fat in the arteries for patients who suffer from coronary artery disease and can be divided in two categories: Critically stenotic plaque and vulnerable or high risk plaque. For that reason, it is of high clinical relevance to further examine the phenomena related to heart attacks which can be executed from a fluid mechanical standpoint through fluid flow simulations. In this thesis, the simulation software COMSOL Multiphysics which utilizes the finite element method (FEM) to obtain numerical solutions was used to create models to represent the artery geometry and the blood flow inside the lumen area. First of all, a model with an idealized, rigid, cylindrical geometry was built and the solutions were compared to the analytically calculated quantities for the occuring axial flow velocity and wall shear stress assuming laminar flow. With this, it could be determined that the FEM delivers acceptably accurate results and therefore the basic validation for the model was ensured. Second, an idealized rotational symmetrical, cylindical model for the plaque and artery geometries was set up wich allows to switch parametrically between different levels of stenosis and other geometric entities. Material properties were assigned to asses the fluid structure interaction between the laminar flow and the solid mechanical components with the multiphysics package in the software. To evaluate patient specific clinical data, a general imaging pipeline that descibes the different steps necessary in the workflow of building a model to analyze fluid mechanic effects on the base of computed tomography images was formulated. Finally, a seperate model based on computed tomography images that were provided by the cardiology clinic Theresienkrankenhaus Mannheim was implemented. For all simulations, a pulsatile pressure condition was used as a boundary condition to represent the pumping mechanism of the myocard. In the next step, the velocity profiles, the pressure distributions and developments over the artery length and the occuring shear stresses were calculated and visualized for the models, respectively. The results of the simulations indicate that the localization of critical stenosis based on the quantitative data for the shear rate, the pressure change and the blood velocity can be assisted. With the color coded visualization of the dimensions and the generation of relevant two dimensional plots, the mechanical phenomena can be illustrated. Lastly, the three dimensional wall shear stress animations over several cardiac cycles can support identifying especially critical regions of the arterial wall.