Abstract:

Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharing patient data. This approach has been applied to complex tasks such as medical image-to-image (I2I) translation, including MRI-to-synthetic CT (sCT) generation. However, existing federated I2I frameworks often assume privacy preservation as an inherent property of FL rather than a requirement to be explicitly validated, leaving their robustness to representative adversarial threat scenarios largely unexplored. In this study, we evaluated the vulnerability of a federated MRI-to-sCT translation framework (FedSynthCT-Brain) to three representative attack classes: Deep Leakage from Gradients (DLG), Federated Membership Inference Attack (FedMIA), and data poisoning. The efficacy of corresponding defense mechanisms, such as Secure Aggregation (SecAgg) and Byzantine-robust median aggregation (FedMedian), were assessed. DLG enabled only the recovery of coarse anatomical structures, with no clinically identifiable details (SSIM ≤ 0.16, PSNR ≤ 11 dB) across clients, suggesting limited vulnerability under the evaluated DLG setting. In contrast, FedMIA achieved high membership discrimination, with AUC scores between 0.92 and 0.99, revealing a critical privacy vulnerability. The introduction of SecAgg reduced AUC values to near-random levels (0.23–0.56) across all centers without impacting synthesis quality. Under high-noise poisoning, the standard federated averaging (FedAvg) aggregation rendered the federation inoperative, while FedMedian restored performance close to the no-poisoning baseline in most scenarios, with significant residual degradation in specific center configurations. At low noise levels, the advantage of FedMedian was less consistent, as low-level noise injection may be indistinguishable from natural heterogeneity across centers, potentially enabling stealthy degradation. These findings demonstrate that federated I2I translation frameworks are not inherently secure and require explicit, multi-layered evaluation. As FL is increasingly adopted in clinical workflows, our results underscore the necessity of integrating cryptographic, algorithmic, and infrastructural safeguards for secure deployment.

Abstract:

Regular activation of the heart originates from cyclic spontaneous depolarisations of sinoatrial node cells (SANCs). Variations in electrolyte levels, commonly observed in haemodialysis (HD) patients, and the autonomic nervous system (ANS) profoundly affect the SANC function. Thus we investigated the effects of hypocalcaemia and sympathetic stimulation on the SANC beating rate (BR). The β-adrenergic receptor (β-AR) signalling cascade, as described by Behar et al., was incorporated into the SANC models of Severi et al. (rabbit) and Fabbri et al. (human). Simulations were conducted across various extracellular calcium ([Ca]) (0.6-1.8 mM) and isoprenaline concentrations [ISO] (0-1000 nM) for a sufficient period of time to allow transient oscillations to equilibrate and reach a limit cycle. The β-AR cell response of the extended models was validated against new Langendorff-perfused rabbit heart experiments and literature data. The extended models revealed that decreased [Ca] necessitated an exponential-like increase in [ISO] to restore the basal BR. Specifically at 1.2 mM [Ca], the Severi and Fabbri models required 28.0 and 9.6 nM [ISO], respectively, to restore the initial BR. Further reduction in [Ca] to 0.6 mM required 170.0 and 43.6 nM [ISO] to compensate for hypocalcaemia. A sudden loss of sympathetic tone at low [Ca] resulted in a loss of automaticity within seconds. These findings suggest that hypocalcaemic bradycardia can be compensated for by an elevated sympathetic tone. The integration of the β-AR pathways led to a logarithmic BR increase and offers insights into potential pathomechanisms underlying sudden cardiac death (SCD) in HD patients. KEY POINTS: We extended the sinoatrial node cell (SANC) models of Severi et al. (rabbit) and Fabbri et al. (human) using the β-adrenergic receptor (β-AR) signalling cascade Behar et al. described. Simulations were conducted across various extracellular calcium ([Ca]) (0.6-1.8 mM) and isoprenaline concentrations [ISO] (0-1000 nM) to reflect conditions in haemodialysis (HD) patients. An exponential-like increase in [ISO] compensated for hypocalcaemia-induced bradycardia in both models, whereas interspecies differences increased the sensitivity of the extended Fabbri model towards hypocalcaemia and increased sympathetic tone. The extended models may help to further understand the pathomechanisms of several cardiovascular diseases affecting pacemaking, such as the high occurrence of sudden cardiac death (SCD) in chronic kidney disease (CKD) patients.

Abstract:

Background: Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT is characterized by increased noise, limited soft-tissue contrast, and artifacts. These issues result in unreliable Hounsfield unit (HU) values, which limits electron density estimation for direct dose calculation. These issues have been addressed by deriving synthetic CT (sCT) from CBCT, particularly by adopting deep learning (DL) methods. However, existing DL approaches are hindered by institutional heterogeneity, scanner-dependent variations, and data privacy regulations that prevented multi-center data sharing.Methods: To overcome these challenges, we propose a cross-silo federated learning (FL) approach for CBCT-to-sCT synthesis in the head and neck region. This approach extends the original FedSynthCT framework to a different image modality and anatomical region. A conditional generative adversarial network (cGAN) was trained using data from three European medical centers within the SynthRAD2025 public challenge dataset while maintaining data privacy at each institution. A combination of the FedAvg and FedProx aggregation strategies, alongside a standardized preprocessing pipeline, was adopted to federate the DL model.Results: The federated model effectively generalized across participating centers, as evidenced by the mean absolute error (MAE) ranging from 64.38±13.63 to 85.90±7.10 HU, the structural similarity index (SSIM) ranging from 0.88±0.02 to 0.92±0.04, and the peak signal-to-noise ratio (PSNR) ranging from 32.86±0.94 to 34.91±1.04 dB. Notably, performance on an external validation dataset of 60 patients yielded comparable metrics: a MAE of 75.22±11.81 HU, an SSIM of 0.90±0.03 and a PSNR of 33.52±2.06, confirming robust cross-center generalization despite differences in imaging protocols and scanner types, without additional training. Furthermore, a visual analysis of the results revealed that the obtained metrics were influenced by registration errors.Conclusions: Our findings demonstrated the technical feasibility of FL for CBCT-to-sCT synthesis task while preserving data privacy, offering a collaborative solution for developing generalizable models across institutions without requiring data sharing or center-specific models.

Abstract:

Brain tumour segmentation is a key application of AI in neuroimaging. Recently, federated learning (FL) has emerged as a strategic and increasingly relevant paradigm in neural computing due to its ability to address key challenges in large-scale neural network training, such as data access, privacy, collaborative learning, and model robustness. However, its adoption is currently hindered by high communication costs and the heterogeneity of client data. In this study, we investigated an efficient FL framework for brain tumour segmentation based on communication-aware optimization. We evaluated FedWSOComp, which integrates sparsification, quantization, and entropy-based encoding, in combination with a 3D U-Net architecture under both homogeneous and heterogeneous data distributions. The multi-institutional FeTS 2024 dataset was employed and partitioned into independent and identically distributed (IID) and non-IID settings, with an independent test set of 67 patients. An overall of 18 configurations combined sparsification rates and quantization levels. Performance was measured using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). Experimental results demonstrated that aggressive compression caused severe degradation in segmentation quality, with HD95 exceeding 60 mm. In contrast, higher retention with finer quantization achieved the best balance between efficiency and accuracy, reaching a DSC and HD95 mm on the test set under non-IID conditions. The findings demonstrated that, when configured with moderate-to-fine quantization and high sparsification retention, FedWSOComp enabled accurate and communication-efficient federated brain tumour segmentation. This study provides quantitative evidence and practical guidance for the deployment of FL-based segmentation models in privacy-sensitive and bandwidth-constrained clinical settings.

Abstract:

Coronary artery disease remains a leading cause of mortality worldwide. Coronary computed tomography angiography (CCTA) provides a non-invasive basis for diagnosis; however, accurate and connectivity-preserving segmentation of the coronary artery tree is essential for robust automated and quantitative analysis. CNN-based architectures, in particular nnU-Net, achieve strong volumetric accuracy but often produce fragmented vessel trees when segmenting thin tubular structures such as coronary arteries. Skeleton Recall (SR) loss has recently been proposed to address these limitations by explicitly penalizing missing centerline segments, yet its effectiveness for coronary artery tree segmentation remains to be established. This work compares a generic baseline loss with an SR-augmented variant within a standard nnU-Net 3D segmentation pipeline for coronary artery segmentation from CCTA. Evaluation is performed using five-fold cross-validation on 98 CCTA volumes from the public ASOCA dataset and an additional in-house cohort. Performance is evaluated using complementary volumetric and connectivity metrics, with quantitative findings contextualized through targeted qualitative visualizations. Results show that augmenting the generic loss with SR significantly improves coronary artery tree connectivity in a practically relevant manner, while maintaining comparable volumetric overlap. Overall, SR constitutes an effective and efficient loss that is simple to integrate, making it a practically viable choice for connectivity-preserving coronary artery tree segmentation.

Abstract:

Improving the success of radiation therapy requires a precise understanding of the interactions between radiation and cell tissue. While 3D cell cultures, such as tumor spheroids, better reflect the complexity of natural tissues than conventional 2D cultures \cite{Costa-2016}, characterizing their radiation response with high spatial and temporal resolution remains a challenge. Tkachenko’s work \cite{Andrey-2025} shows that combining optical coherence tomography (OCT) with radiomics and machine learning offers a promising alternative for predicting the delivered radiation dose.This study aims to investigate whether and how the features determined by radiomic analysis differ for core and rim and whether the predictive power of the approach described by Tkachenko and based on the research of my supervisor Mark Daniel Arndt (see Arndt et al. is further improved when the radiation damages are characterized separately for core and rim based on the optical coherence tomography scans of entire spheroids. The underlying expectation is that segmenting OCT scans of irradiated tumor spheroids into regions, where radiation damage is more homogeneous, yields region-specific datasets that improve the machine learning model’s power to predict the delivered radiation dose, in contrast to using OCT scans of entire spheroids. For this study, cell spheroids derived from the SAS cell line were cultured and irradiated with X-rays at doses ranging from 0 to 12 Gy. The spheroids were scanned at Delta t_OCT days after irradiation using a spectral domain OCT system, which yielded a speckle variance and a mean intensity voxel grid for each spheroid. A preprocessing pipeline was developed to create individual masks for the entire spheroid, as well as for the rim and core regions. Quantitative radiomic features were extracted from the regions defined by the masks, including descriptors for texture, shape, and intensity distributions. These different feature datasets were used to train different machine learning models using five different learning algorithms: Random Forest, Extra Trees, XGBoost, LightGBM, and Support Vector Regression. For each model, the predictive accuracy of the delivered radiation dose and the rank of the features used for prediction were obtained.The comparison of the different predictive accuracies shows that models trained on the fit rim mask feature dataset exhibit significantly better predictive accuracy than models based on the entire spheroid mask feature dataset. The comparison of the radiomic features showed that they differed significantly in composition between the rim and core models, suggesting that local biological differences are reflected in the OCT radiomics profile. Additionally, the comparison of the overall feature importance shows that the speckle variance voxel grids seem to contain more information for dose prediction than the mean intensity voxel grids.

Abstract:

Particle therapy offers high precision but remains sensitive to range uncertainties causedby respiratory motion. In proton radiography (pRad), the sequential spot-scanning deliveryleads to complex interplay artifacts that degrade the accuracy of water-equivalent thickness(WET) maps. Current Monte Carlo tools, such as TOol for PArticle Simulation (TOPAS),support static geometries but lack the synchronization required to couple discrete pencilbeam scanning (PBS) delivery with free-breathing motion.This thesis addresses these limitations by developing a novel, synchronized free-breathingsimulation framework. For the first time, a simulation environment has been establishedthat maintains temporal synchronization between discrete PBS delivery and continuousrespiratory-induced anatomical deformation. Unlike previous models, this framework accounts for physiological motion during both active beam delivery and the mechanicallatencies of the imaging system.The framework was applied to the extended cardiac-torso (XCAT) phantom and patientdatasets (4 cases), enabling the first successful three-dimensional (3D) proton imaging on adynamically changing thoracic volume and capturing pattern-specific distortions and timedependent WET fluctuations that were previously inaccessible to static modeling.An important application of the proposed framework is the quantitative assessment ofacquisition strategies under realistic motion conditions. As an illustrative example, uprightpositioning showed improved performance compared to the standard supine setup. Themedian root-mean-square error (RMSE) was reduced from 4.09 mm in the supine position to2.05 mm in the upright position, corresponding to a 49.9% reduction in WET error.While the general benefit of upright positioning is consistent with prior observations, theframework enables detailed quantification of motion-induced reconstruction errors. In particular, the upright configuration exhibited increased robustness with respect to the respiratoryphase at scan initiation, resulting in more consistent image quality.In summary, this work establishes a framework for proton imaging, parameterized by thebeam delivery specifications of the Heidelberg Ion-Beam Therapy Center (HIT) facility andthe technical characteristics of a clinical proton imaging detector (protonVDA). By bridgingthe gap between dynamic pencil beam delivery and time-resolved anatomical deformation,the framework provides a baseline for the development of motion-mitigation strategies.

Abstract:

Use of temporary mechanical circulatory support (tMCS) has grown considerably overrecent decades for the stabilization of patients with potentially reversible cardiogenic shock(CS), yet mortality remains high. Although progress has been made in defining shockseverity and combining pharmacologic and mechanical therapies, randomized studies havenot shown a consistent survival benefit for tMCS. Among available options, veno-arterial(VA)-extracorporeal membrane oxygenation (ECMO) is used most frequently because itprovides rapid cardiopulmonary support. However, its continuous-flow mechanism restrictsthe opening of the aortic valve, which increases left ventricular afterload and myocardialstress. This may partly explain why outcomes remain poor despite the restoration of systemicperfusion.To address this limitation, the clinical use of the synchronized cardiac support (SCS) elec-trocardiogram (ECG)-triggered pulsatile VA-ECMO mode is currently guided mainly bymanual adjustment and visual assessment of diastolic augmentation. Since this response isstrongly patient-specific, optimization still relies largely on clinical experience. However, aquantitative evaluation of the underlying hemodynamic factors is still lacking and is neededto support a more objective assessment and future automation.This thesis presents a method for the quantitative analysis of pulsatile VA-ECMO using acontrolled in-vitro environment. A mock circulatory loop (MCL) coupled to a clinical VA-ECMO system is developed to reproduce the hemodynamic conditions of CS and to enablesystematic variation of pulsation delay, flow rate, heart rate, and cannulation configuration.Based on recorded aortic and extracorporeal circuit pressure signals, a set of waveform-basedparameters is computed to characterize pulsatility and diastolic augmentation.The results demonstrate that pulsation delay primarily influences the morphology of theaortic pressure waveform, while conventional parameters such as mean arterial pressure(MAP), and diastolic blood pressure (DBP) remain largely governed by flow conditions.In contrast, dedicated waveform-based metrics, particularly the modified pulsatility index(PPI’) and the diastolic-to-systolic area ratio, show clear sensitivity to delay and provide amore physiologically meaningful quantification of pulsatile support. The most pronouncedaugmentation is observed when the pulsatile contribution is aligned with the onset of diastole,indicating that optimal timing depends on the relative position within the cardiac cycle.Analysis of extracorporeal circuit pressure signals reveals that these are dominated by pump-induced dynamics. Although delay-dependent changes are present, they correlate poorlywith aortic pulsatility, limiting their suitability as standalone indicators of therapy quality.Consequently, aortic pressure-derived metrics are identified as the more reliable basis forassessing pulsatile VA-ECMO performance.Overall, this work establishes a reproducible framework for the quantitative evaluation ofpulsatile VA-ECMO and highlights the importance of waveform-based analysis for capturingphysiologically relevant effects. The findings provide a foundation for objective assessmentand represent a step toward future automated control strategies for ECG-synchronizedpulsatile support.

Abstract:

Stereoelectroencephalography is a minimally invasive neurosurgical technique used to lo-calize the epileptogenic zone (region responsible for seizure generation) in patients withdrug-resistant epilepsy. The procedure requires the placement of multiple intracranial elec-trodes along carefully planned trajectories that reach predefined brain targets while avoidingcritical structures such as blood vessels. Planning these trajectories is complex and timeconsuming, relying heavily on manual inspection of medical images and surgical expertise.Consequently, there is increasing interest in computer-assisted approaches that can supportclinicians during the planning process.This thesis investigates the feasibility of using deep reinforcement learning to automatethe trajectory generation step of the SEEG planning workflow. The proposed frameworkintegrates medical imaging, vascular modeling, and a reinforcement learning agent into amodular pipeline. The trajectory planning task is then formulated as a sequential decision-making problem in which an agent iteratively adjusts electrode entry and target points. Inorder to approximate the action-value function and guide the search through the anatomicalspace, a Deep Q-Network is used, together with a reward formulation that incorporates safetyand geometric constraints, particularly vessel avoidance.Experimental evaluation using patient imaging data demonstrates that the RL agent cangenerate candidate electrode trajectories that reach predefined targets while maintaining safedistances from vascular structures. In this work, 2000 candidate trajectories were generatedand evaluated, with feasibility rates ranging from about 26% to 52% depending on anatomicalcomplexity. Generation of trajectories requires only a few seconds per electrode, indicatingthe potential for substantial reductions in planning time.Overall, the results demonstrate that reinforcement learning can effectively automate thegeneration of candidate SEEG trajectories and provide a foundation for future computer-assisted surgical planning systems.

Abstract:

To ensure adequate cerebral oxygenation, bypass surgery may be required to reroute blood around an occlusion or stenosis. Therefore, intraoperative evaluation of blood flow is crucial. Current methods are ultrasound-based and require direct vessel contact.Therefore, non-contact optical methods based on Optical Coherence Tomography (OCT) are investigated. OCT provides depth-resolved structural imaging. By applying the so-called Speckle Variance (SV) algorithm, which calculates the variance of the OCT signal over time, moving scatterers such as red blood cells within vessels can be distinguished from the surrounding static tissue, thereby enabling vessel visualization, which is referred to as OCT angiography (OCTA). While the SV algorithm enables visualization of vessels, the broader objective is the quantification of red blood cell flow velocity within the vessels.The SV signal depends on flow velocity and exhibits a sigmoidal shape at low flow velocities and decreases again at high velocities after reaching saturation. The influence of several factors on this curve was investigated. First, the A-scan rate was varied, where exposure time and interscan time are identical. In a second step, the effects of exposure time and interscan time were examined separately in order to assess their individual impact.To evaluate the influence of these parameters, two velocities were defined for objective assessment: the slowest detectable velocity (SDV) and the fastest distinguishable velocity (FDV). Experiments were performed using a milk–water mixture as a blood analogue, pumped through a microchannel with a syringe pump, and scans were acquired at seven A-scan rates (5, 10, 25, 50, 100, 200, and 248 kHz).Higher A-scan rates increased both SDV and FDV, and also expanded the measurable velocity range, while low A-scan rates improved sensitivity for slow flows but reduced the overall range. Post-processing showed that higher interscan times increase sensitivity for slow flows but lowers the FDV, whereas increasing exposure time decreases the SDV without a clear effect on FDV.Furthermore, it was investigated that the drop in SV signal after saturation is mainly caused by the washout effect, which reduces the intensity of the OCT signal and consequently the variance. This was demonstrated by normalizing the velocity response curve by the square of the intensity. On the normalized curve, no drop was observed.In conclusion, this thesis provides a quantified basis for selecting OCTA measurement parameters for clinical applications such as intraoperative bypass surgery.

Abstract:

Lung collapse is an important physiological process that occurs when the pleural cavity is opened during thoracic surgery[1, 2], and it is also intentionally induced through artificial pneumothorax to improve surgical exposure and operating space[3, 4]. Reconstructing this phenomenon in a controlled and observable manner is therefore essential for surgical simulation and collapsed lung modelling[5], enabling safer training conditions and more reliable evaluation of operative strategies. However, most existing lung phantom models focus primarily on anatomical form or material elasticity and do not reproduce the dynamic pressure behavior that governs real collapse[6, 7], which limits their usefulness for physiology oriented studies. This study presents an experimental platform developed to simulate pressure variations associated with lung inflation and collapse. The system integrates medical image processing, mechanical and electronic design, embedded system development, product level prototyping, and experimental data analysis into a unified workflow. Lung geometry was first segmented from CT data using 3D Slicer[8] and was then further simplified in Autodesk Fusion 360, AutoCAD, and Blender to achieve a physiologically appropriate scale while ccommodating manufacturability and surgical access constraints such as incision and opening placement. The physical model was fabricated through 3D printing with iterative efinement in material selection, SLA and FDM evaluation[9, 10], and optimization of printing accuracy and reliability. A pneumatic system driven by air pumps and relay control was mplemented to generate controlled pressure environments representing different collapse conditions. A pressure sensor was integrated to monitor internal pressure changes over time, enabling the capture of characteristic pressure trends. Electronic schematics were designed in EasyEDA, while control logic was programmed using Arduino IDE in C++. Python cripts developed in Visual Studio Code provided automated serial data acquisition, CSV storage, and line-plot visualization[11], forming the basis for interpreting and comparing xperimental results. To evaluate physiological consistency, three categories of collapse simulation experiments were conducted: controllable collapse produced by airway suction[2], uncontrollable col-lapse driven by pleural-side inflow[4], and a more surgery oriented pneumothorax pattern achieved through alternating pleural side inflow. Although the platform rovides trend-level pressure information rather than absolute physiological values, the resulting pressure–time curves clearly reflect the transition from a supported state toward collapse as the pressure environment moves toward equilibrium[12]. Based on these findings, the model is capable of reproducing a range of collapse patterns, including normal inflation, full collapse, and behavior similar to artificial pneumothorax[3], demonstrating its suitability for studying both controlled and uncontrolled collapse mechanisms.Overall, the platform offers a practical and low cost tool for investigating collapse dynamics[5], supports thoracic surgical education and hands on training[2], and provides a solid foundation for the future development of higher fidelity and physiology based thoracic simulation systems.

Abstract:

Stereotactic arrhythmia radioablation (STAR) is a promising treatment for patients withventricular arrhythmias who are unresponsive to conventional therapy. Proton therapy mayimprove STAR planning by offering better dose conformity, while upright CT imaging mayincrease the accessibility of proton therapy and improve treatment accuracy by reducingrespiratory-induced target motion. However, clinical workflows are designed for supineimaging, introducing anatomical discrepancies. This work aims to develop and evaluatemethods for reliable upright-to-supine image registration to support upright-based treatmentplanning.In this work, a complete processing pipeline was developed to investigate posture-inducedanatomical differences relevant for proton-based STAR. Upright and supine patient data werepreprocessed, phase-sorted, and converted into structured studies. Organ segmentation wasperformed using a deep learning–based approach with additional manual refinement, whiletarget propagation between different CT modalities and respiratory phases was achievedusing rigid and deformable image registration, and the resulting transformations were appliedacross all breathing phases. Anatomical metrics—including centroid displacement, volumevariation, and phase-dependent motion—were extracted and analyzed for both patients posi-tions orientations.The registration pipeline achieved an alignment accuracy of approximately X mm acrossphases. Posture-related anatomical shifts were observed, with heart centroid displacements ofup to X mm, target centroid differences of X mm, ITV volume differences of X ,Phase-wiseanalysis further revealed breathing-dependent structural variations, with upright imagingshowing consistently smaller thoracic expansion compared with the supine orientation.These findings demonstrate that posture has a measurable impact on thoracic anatomyrelevant for STAR proton therapy planning. The developed registration pipeline enablessystematic comparison of upright and supine geometries and provides a foundation forimproving posture-aware treatment planning. Future work should include larger patientcohorts and dose-based evaluation to fully assess the clinical implications of using uprightimaging for STAR.

Abstract:

Diagnostic bronchoscopy is an uncomfortable procedure that requires careful navigation through narrow, highly branching airways. High-fidelity simulation can improve clinician training and enable the development of vision-based navigation methods, but realistic bronchoscopic video data is dicult to obtain in practice: clinical recordings are often inaccessible due to privacy constraints and strict hospital governance. Existing common unpaired baselines typically require substantial target data and often degrade over long sequences due to temporal drift and flicker, making them poorly suited to the practical realities of bronchoscopy constraints where target data is scarce and stability over long, clinically realistic durations is essential.To address these limitations, we introduce a pipeline that learns from fewer than 500 target frames and performs inference using a single target reference frame. The proposed method produces stable long-horizon translations from CT-driven virtual bronchoscopic videos, preserves airway geometry—especially small bifurcations critical for navigation—and reduces temporal artifacts over extended sequences through world consistency, while still enabling controllable transfer of discrete appearance modes without requiring separate training for each style.This thesis studies unpaired video-to-video translation for bronchoscopic simulation in an extreme low-data setting, with an explicit focus on clinically relevant constraints. Rather than aiming for stylization alone, the work prioritizes: (1) airway structure and geometry preservation, keeping lumen boundaries and fine bifurcations stable to avoid misleading navigation cues; (2) temporal consistency over long sequences, mitigating flicker and drift; and (3) appearance transfer as a secondary objective, aligning texture, color, and wet-surface highlights to the chosen reference.

Abstract:

Atrial fibrillation (AFib) is the most common clinically relevant cardiac arrhythmia. A promising treatment modality is pulsed field ablation (PFA), a non-thermal catheter ablation technique that offers increased tissue selectivity and a reduced risk of collateral damage compared to thermal ablation methods. PFA induces irreversible electroporation, whereby electric pulses create pores in the cell membrane, ultimately leading to cell death. While electroporation depends on pulse parameters, field strength, and cell orientation, previous studies have focused on single cells. This work addresses the question: How do orientation and pulse parameters affect electroporation in coupled cardiomyocytes? This thesis investigates the influence of anisotropy on electroporation through development and extension of a unicellular model to two-cell and five-cell models of electrically coupled cardiomyocytes. Electrical coupling between the cells was modelled by incorporating gap junction conductivity and its reduction due to electroporation. Finite element simulations were performed for six different pulse durations (100 ns, 1 μs, 10 μs, 100 μs, 1 ms, 10 ms) and electric field strengths in the range 10 to 10^5 V/cm for both parallel and perpendicular cell orientations relative to the electric field. The contribution of gap junctions to the induced TMV and pore density was assessed by comparing simulations with functional and inhibited gap junctions. The two-cardiomyocyte model exhibited electroporation onset and extent consistent with unicellular studies, with pore formation occurring at lower field strengths in the parallel orientation for pulse durations of 100 μs and longer. In contrast, the five-cardiomyocyte model demonstrated electroporation only for 1 ms and 10 ms pulses at field strengths exceeding 10^4 V/cm, with shorter pulses failing to induce electroporation. In this five-cell configuration, electroporation onset was unaffected by cell orientation, though orientation-dependent differences in electroporation extent persisted. Gap junction mediated intercellular coupling influenced membrane voltage approximately twice as strongly when the intercalated disc was aligned parallel to the electric field. Key limitations of the model include the use of a fixed, real-valued membrane capacitance, and the simplified representation of the intercalated disc as a single-capacitor element. These simplifications led to altered TMV dynamics for nanosecond pulses and exaggerated immediate effects of gap junction decoupling. Overall, this work provides insight into anisotropic electroporation in coupled cardiomyocytes and demonstrates the importance of incorporating multicellular effects when designing pulse protocols. The findings suggest that short pulses reduce orientation dependence and may improve lesion uniformity, while gap junction coupling modulates electroporation thresholds and extent. By linking unicellular phenomena to multicellular behaviour, this thesis offers a foundation for optimising PFA protocols to achieve more predictable and effective ablation outcomes.

Abstract:

The extracellular–membrane–intracellular (EMI) model enables cardiac electrophysiologysimulations with explicit resolution of individual cardiomyocytes and their microstructuralinteractions. By directly representing intracellular, extracellular, and membrane domains, theEMI framework allows the investigation of cell-level conduction mechanisms that cannot becaptured by homogenized mono- or bidomain models. However, this increased physiologicalfidelity comes at the cost of substantially higher computational demands due to the largenumber of degrees of freedom required for spatial discretization.The main objective of this bachelor’s thesis is to systematically investigate the influenceof spatial mesh resolution on simulation accuracy and computational performance in EMIbased cardiac electrophysiology simulations using the openCARP framework. For thispurpose, a convergence analysis was performed. Starting from a high-resolution referencemesh, a large set of coarsened meshes was generated by varying edge length constraintsand geometric deviation parameters. To assess numerical convergence, three clinically andphysiologically relevant metrics were analyzed: unipolar electrograms, conduction velocities(CVs), and upstroke velocities of the action potentials. The finest mesh configuration servedas a reference solution against which all other configurations were compared.The results demonstrated that substantial reductions in computational cost can be achievedthrough spatial coarsening while maintaining acceptable accuracy, defined by individuallimits depending on the chosen metric. For the optimal mesh configuration with a meaneffective edge length of hedge = 10.3µm (σedge = 2.98µm), computational runtime wasreduced by approximately 80% compared to the highest-resolution reference mesh whilepreserving electrophysiological accuracy for all considered metrics. Based on the meshconfiguration identified as the optimal trade-off for CV evaluation, a parameter tuningstudy was conducted to obtain physiologically realistic longitudinal CV values within theEMI framework in the openCARP environment. Specifically, physiologically realisticlongitudinal conduction velocities of approximately 1m/s were obtained by selecting eithera gap junction resistance of Rg = 0.00613kΩcm2 at an intracellular conductivity of σi =0.4S/m, or an intracellular conductivity of σi = 0.1575S/m at a gap junction resistance ofRg =0.003kΩcm2.Overall, this work provides practical guidelines for selecting mesh resolution and parametervalues in EMI-based simulations and contributes to the validation and efficient application ofmicrostructural cardiac electrophysiology models.