Cardiac Magnetic Resonance (CMR) imaging is widely used to personalize heart models for cardiac digital twin analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. In this work, we introduce MorphiNet, a novel network that reproduces heart anatomy learned from high-resolution Computed Tomography (CT) images, unpaired with CMR images. MorphiNet encodes the anatomical structure as gradient fields, deforming template meshes into patient-specific geometries. A multilayer graph subdivision network refines these geometries while maintaining dense point correspondence, suitable for downstream computational analysis. MorphiNet achieved the strongest overall trade-off in bi-ventricular myocardium reconstruction on CMR patients with tetralogy of Fallot, with 0.3 higher Dice score and 2.6 lower Hausdorff distance compared to the best existing template-based methods, while achieving comparable geometric accuracy to neural implicit function methods on CT data at $50\times $ faster inference. Cross-dataset validation on the Automated Cardiac Diagnosis Challenge confirmed robust generalization, achieving a 0.7 Dice score with 30% improvement over previous template-based approaches. We validate our anatomical learning approach through the successful restoration of missing cardiac structures and demonstrate significant improvement over standard Loop subdivision. Motion tracking experiments further confirm MorphiNet's capability for cardiac function analysis, including ejection-fraction estimates that correctly identify myocardial dysfunction in tetralogy of Fallot patients. Code and checkpoints are available at https://github.com/MalikTeng/MorphiNetV2.

Scientific data visualization is essential for analysis, communication, and storytelling in research. While Blender offers a powerful rendering engine and a flexible 3D environment, its steep learning curve and general-purpose interface can hinder scientific workflows. To address this gap, we present SciBlend, a Python-based toolkit that extends Blender for data visualization. It provides specialized add-ons for multiple computational data files import, annotation, shading, and scene composition, enabling both photorealistic (Cycles) and real-time (EEVEE) rendering of large-scale and time-varying data. By combining a streamlined workflow with physically based rendering, SciBlend supports advanced visualization tasks while preserving essential scientific attributes. Comparative evaluations across multiple case studies show improvements in rendering performance, clarity, and reproducibility relative to traditional tools. This modular and user-oriented design offers a robust solution for creating publication-ready visuals of complex computational data.

Atrial fibrillation (AF), impacting nearly 50 million individuals globally, is a major contributor to ischaemic strokes, predominantly originating from the left atrial appendage (LAA). Current clinical scores like CHA₂DS₂-VASc, while useful, provide limited insight into the pro-thrombotic mechanisms of Virchow's triad-blood stasis, endothelial damage, and hypercoagulability. This study leverages biophysical computational modelling to deepen our understanding of thrombogenesis in AF patients. Utilising high temporal resolution Cine magnetic resonance imaging (MRI), a 3D patient-specific modelling pipeline for simulating patient-specific flow in the left atrium was developed. This computational fluid dynamics (CFD) approach was coupled with reaction-diffusion-convection equations for key clotting proteins, leading to an innovative risk stratification score that combines clinical and modelling data. This approach categorises thrombogenic risk into low (A), moderate (B), and high (C) levels. Applied to a cohort of nine patients, pre- and post-catheter ablation therapy, this approach generates novel risk scores of thrombus formation, which are based of mechanistic characterisation of all aspects of the Virchow's triad. Currently, thrombogenesis mechanisms are not factored in widespread clinical risks scores based on demographic characteristics and co-morbidities. Notably, some patients with a CHA₂DS₂-VASc score of 0 (lowest clinical risk) exhibited much higher risks once the individual pathophysiology was accounted for. This discrepancy highlights the limitations of the CHA₂DS₂-VASc score in providing detailed mechanistic insights into patient-specific thrombogenic risk. This work introduces a comprehensive method for assessing thrombus formation risks in AF patients, emphasising the value of integrating biophysical modelling with clinical scores to enhance personalised stroke prevention strategies.

In atrial fibrillation (AF), atrial biomechanics are altered, reducing atrial movement. It remains unclear whether these changes are due to altered anatomy, myocardial stiffness, or constraints from surrounding structures. Understanding the causes of changed atrial deformation in AF could enhance tissue characterization and inform AF diagnosis, stratification, and treatment. We created patient-specific anatomical models of the left atrium (LA) from CT images. Passive LA biomechanics were simulated using finite deformation continuum mechanics equations. LA stiffness was represented by the Guccione material law, where α scaled the anisotropic stiffness parameters. Regional passive stiffness parameters were calibrated to peak regional deformation during the reservoir phase and validated against deformation transients derived from retrospective gated CT images during the reservoir and conduit phase. Physiological LA deformation varies regionally, with the roof deforming significantly less than other regions during the reservoir phase. The fitted model matched peak patient deformations globally and regionally with an average error of [Formula: see text] mm over our cohort. We compared deformation transients through the reservoir and conduit phases and found that the simulated deformation transients were within an average of [Formula: see text] mm per unit time of the CT-derived deformation transients. Regional stiffness varied across the atria with average α values of 1.8, 1.6, 2.2, 1.6 and 2.1 across the cohort in the anterior, posterior, septum, lateral and roof regions respectively. Using mixed effect models, we found no correlation between regional patient LA deformation and regional estimates of wall thickness or regional volumes of epicardial adipose tissue. We found a significant correlation between regionally calibrated stiffness and CT-derived LA biomechanics (p = 0.023). We have shown that regional heterogeneity in stiffness contributes to regional LA biomechanics, while anatomical features appeared less important. These findings provide insight into the underlying causes of altered LA biomechanics in AF.

Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. We found that the SHAP and LIME methods and Chi-squared test all worked well together with the native Random forest and Logistic regression feature rankings. Some methods gave inconsistent results, which included the Maximum Relevance Minimum Redundancy and Neighbourhood Component Analysis methods. The permutation-based methods generally performed quite poorly. A surprising result was found in the case of left bundle branch block, where T-wave morphology features were consistently identified as being important for diagnosis, but are not used by clinicians.

Transseptal puncture (TSP) is a prerequisite for left atrial catheter ablation for atrial fibrillation, requiring access from the right side of the heart. It is a demanding procedural step associated with complications, including inadvertent puncturing and application of large forces on the tissue wall. Robotic systems have shown great potential to overcome such challenges by introducing force-sensing capabilities and increased precision and localization accuracy. Therefore, this work introduces the design and development of a novel robotic system developed to perform TSP. We integrated optoelectronic sensors into the tools' fixtures, measuring tissue contact and puncture forces along one axis. The novelty of this design is in the system's ability to manipulate a Brockenbrough (BRK) needle and dilator-sheath simultaneously and measure tissue contact and puncture forces. In performing puncture experiments on anthropomorphic tissue models, an average puncture force of 3.97 ± 0.45 N (1SD) was established - similar to the force reported in literature on the manual procedure. This research highlights the potential for improving patient safety by enforcing force constraints, paving the way to more automated and safer TSP.

Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.

Biophysical models of the atrium provide a physically constrained framework for describing the current state of an atrium and allow predictions of how that atrium will respond to therapy. We propose a work flow to simulate patient specific electrophysiological heterogeneity from clinical data and validate the resulting biophysical models. In 7 patients, we recorded the atrial anatomy with an electroanatomical mapping system (St Jude Velocity); we then applied an S1-S2 electrical stimulation protocol from the coronary sinus (CS) and the high right atrium (HRA) whilst recording the activation patterns using a PentaRay catheter with 10 bipolar electrodes at 12 ± 2 sites across the atrium. Using only the activation times measured with a PentaRay catheter and caused by a stimulus applied in the CS with a remote catheter we fitted the four parameters for a modified Mitchell-Schaeffer model and the tissue conductivity to the recorded local conduction velocity restitution curve and estimated local effective refractory period. Model parameters were then interpolated across each atrium. The fitted model recapitulated the S1-S2 activation times for CS pacing giving a correlation ranging between 0.81 and 0.98. The model was validated by comparing simulated activations times with the independently recorded HRA pacing S1-S2 activation times, giving a correlation ranging between 0.65 and 0.96. The resulting work flow provides the first validated cohort of models that capture clinically measured patient specific electrophysiological heterogeneity.