Publications
You can also find my articles on my Google Scholar profile.
Published in Journal of Open Source Software, 2023
This work presents SMART, a high-performance simulation package based on the FEniCS finite element library, for modeling and simulating spatially-varying reaction-transport processes in cellular systems. SMART allows for the specification of reaction pathways and supports complex cell geometries obtained from advanced microscopy and reconstruction methods. By addressing the challenges of high dimensionality, non-linearities, and coupling, SMART enables the detailed modeling of cell signaling pathways and the prediction of cellular function.
https://joss.theoj.org/papers/10.21105/joss.05580
Published in Annals of Biomedical Engineering, 2023
This study explores a novel measure, the standard deviation of regional wall stress at the time of mitral valve closure (SD_MVC), for assessing response to cardiac resynchronization therapy (CRT) in heart failure patients. Using a computational modeling framework, the researchers found that patients with lower SD_MVC responded better to CRT, with SD_MVC correlating with long-term response based on end-diastolic volume reduction. These findings suggest that SD_MVC could potentially improve patient selection criteria for CRT implantation. However, further studies with a larger patient cohort are needed to validate these results.
http://dx.doi.org/10.22541/au.160865713.30751611/v1
Published in Journal of Open Source Software, 2023
This paper presents a fully coupled electromechanical model to study the effects of cardioactive drugs on the heart. The model combines a human cell electromechanical model with a monodomain partial differential equation representation of the electrical substrate and an incompressible, anisotropic, hyperelastic continuum model. The model is implemented in the FEniCS framework and incorporates iterative conjugate gradient and sparse direct solvers to solve the equations.
http://dx.doi.org/10.21105/joss.04753
Published in Biomechanics and Modeling in Mechanobiology, 2023
This study introduces a mathematical and numerical framework for investigating tissue-level cardiac mechanics on a microscale by considering explicit three-dimensional geometrical representations of cells within a matrix. The model explores mechanical differences between the extracellular and intracellular spaces, and sensitivity analysis reveals the significance of extracellular matrix stiffness for intracellular stress under contraction. This work expands upon existing models and offers a new framework to explore complex cell-cell and cell-matrix interactions in cardiac mechanics.
Recommended citation: Telle, Åshild, et al. "A cell-based framework for modeling cardiac mechanics." Biomechanics and Modeling in Mechanobiology 22.2 (2023): 515-539. https://link.springer.com/article/10.1007/s10237-022-01660-8
Published in Computing in Cardiology Conference (CinC), 2022
This study presents a novel 3D electromechanical (EM) model to simulate the effects of inotropic drugs, specifically Omecamtiv Mecarbil (OM), on human cardiac tissue. The model successfully replicated concentration-dependent drug effects, such as increased active tension, but indicated the need for further development to capture the characteristic delay in time to peak contraction observed in experimental data.
http://dx.doi.org/10.22489/cinc.2022.033
Published in ACS Pharmacology & Translational Science, 2022
This study emphasizes the importance of evaluating arrhythmogenic drugs before market approval and highlights the limitations of current in vitro models using two-dimensional (2D) culture formats. The researchers present a three-dimensional (3D) cardiac microphysiological system (MPS) using human-induced pluripotent stem cell-derived cardiomyocytes, which successfully predicted drug cardiotoxicity risks based on changes in action potential duration, beat waveform, and occurrence of proarrhythmic events. The cardiac MPS outperformed existing 2D models and provides a promising platform for rapid and reliable screening of proarrhythmic drug risk.
http://dx.doi.org/10.1021/acsptsci.2c00088
Published in Frontiers in Pharmacology, 2021
This study highlights the challenges faced in finding effective treatments for COVID-19 and the need for rapid evaluation of potential therapeutics. The researchers developed a cardiac microphysiological system (MPS) that accurately predicted cardiac liabilities associated with hydroxychloroquine (HCQ) and azithromycin (AZM), including arrhythmias and QT interval prolongation. The MPS provides a high-content screening platform for assessing the cardiac safety of potential therapeutics, allowing for faster and safer access to effective treatments for COVID-19.
http://dx.doi.org/10.3389/fphar.2021.684252
Published in Clinical and Translational Science, 2021
This study presents a chronic preclinical drug screening platform, a cardiac microphysiological system, to assess the cardiotoxicity associated with repurposed hydroxychloroquine (HCQ) and azithromycin (AZM) polytherapy in the context of a mock phase I safety clinical trial. The platform accurately predicted clinical outcomes and identified biomarkers for negative effects on tissue function, morphology, and antioxidant protection, providing valuable insights for clinicians in designing trials and accelerating access to safe COVID-19 therapeutics.
https://ascpt.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cts.13038
Published in Frontiers in Pharmacology, 2020
This study aims to improve the usefulness of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) for drug screening applications by addressing their relative immaturity compared to adult cardiomyocytes. By utilizing an updated computational procedure, the methodology can more efficiently identify drug-induced changes and quantitate important metrics, ultimately enhancing the potential for employing stem cell-derived experimental tissues in elucidating drug effects in adult cardiomyocytes
http://dx.doi.org/10.3389/fphar.2019.01648
Published in American Journal of Physiology-Heart and Circulatory Physiology, 2019
This study examines the mechanical changes associated with pulmonary arterial hypertension (PAH) by analyzing clinical data and using computational modeling. The findings suggest that the ratio of right ventricle (RV) to left ventricle (LV) end-diastolic volume can be used as a clinical index for assessing disease severity and estimating RV contractility in PAH patients.
https://journals.physiology.org/doi/full/10.1152/ajpheart.00094.2019
Published in Journal of Open Source Software, 2019
pulse is a python package based on FEniCS for solving problems in cardiac mechanics. The user can pick any of the built-in meshes or choose a custom user-defined mesh. A collection of different demos showing how to use the pulse library is found in the repository.
http://dx.doi.org/10.21105/joss.01539
Published in Biomechanics and Modeling in Mechanobiology, 2018
This study introduces a data assimilation technique to estimate personalized models of cardiac mechanics with heterogeneous elastic material properties, specifically in the context of myocardial infarction. The method is tested using clinical data, demonstrating good matches to regional strains and providing insights into stress-strain relationships. This is the first application of adjoint-based data assimilation to estimate cardiac elastic heterogeneities in 3D from medical images.
http://dx.doi.org/10.1007/s10237-018-1028-5
Published in International Journal for Numerical Methods in Biomedical Engineering, 2018
This study presents a novel and efficient framework for creating personalized computational models of biventricular heart mechanics. The models, based on data assimilation and patient-specific parameters, accurately estimate important physiological quantities and provide insights into the role of fiber angles on heart function. The framework is highly efficient and may have diagnostic applications for patient-specific cardiac mechanics modeling.
http://dx.doi.org/10.1002/cnm.2982
Published in Patient-Specific Computational Modeling, 2018
In this thesis we have developed a framework to effectively build a virtual heart of the individual patient, so that measurements made in the clinic can be incorporated into the underlying mathematical model. Such virtual hearts have been used to study the mechanics of the heart in different patient groups. Furthermore, we evaluated different biomarkers that may have potential clinical value, and evaluated the performance of the method. These simulations can be performed on a regular laptop in just a few hours, which means that this framework can potentially be included as a diagnostic toolbox in the clinic.
https://www.duo.uio.no/bitstream/handle/10852/62015/PhD-Finsberg-2018.pdf
Published in Journal of Computational Science, 2018
Cardiac computational models, individually personalized, can provide clinicians with useful diagnostic information and aid in treatment planning. A major bottleneck in this process can be determining model parameters to fit created models to individual patient data. However, adjoint-based data assimilation techniques can now rapidly estimate high dimensional parameter sets. This method is used on a cohort of heart failure patients, capturing cardiac mechanical information and comparing it with a healthy control group. Excellent fit (R2 ≥ 0.95) to systolic strains is obtained, and analysis shows a significant difference in estimated contractility between the two groups.
http://dx.doi.org/10.1016/j.jocs.2017.07.013
Published in International Journal for Numerical Methods in Biomedical Engineering, 2017
This paper discusses the limitations of current data assimilation procedures for personalizing computational models of cardiac mechanics and proposes a new adjoint gradient-based method that can efficiently handle high-dimensional parameters. The method is tested on synthetic and clinical data, demonstrating its ability to produce accurate personalized models of cardiac mechanics.
http://dx.doi.org/10.1002/cnm.2863
Published in Institutt for matematiske fag, NTNU, 2014
In this thesis we will study wavelet techniques for image classification in ultrasound(US) images. The aim is to develop a method for classifying the degree of inflammation in finger-joints.We develop and apply the techniques of the windowed scattering transform. This is a wavelet-based technique which is proven to be very efficient in image classification problems. Both theoretical and numerical sides have been considered. We also discuss other possible techniques for classification of US images, in particular a method based on the area of inflammation.
https://brage.bibsys.no/xmlui/bitstream/handle/11250/259333/733307_FULLTEXT01.pdf
