Simulations of Biological Systems
Multi-Physics Models of Cancer Cells
Reconstruction of cardiac position using body surface potentials|
J. A. Bergquist, J. Coll-Font, B. Zenger, L. C. Rupp, W. W. Good, D. H. Brooks, R. S. MacLeod. In Computers in Biology and Medicine, Vol. 142, pp. 105174. 2022.
Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.
Translational computer science at the scientific computing and imaging institute|
C. R. Johnson. In Journal of Computational Science, Vol. 52, pp. 101217. 2021.
The Scientific Computing and Imaging (SCI) Institute at the University of Utah evolved from the SCI research group, started in 1994 by Professors Chris Johnson and Rob MacLeod. Over time, research centers funded by the National Institutes of Health, Department of Energy, and State of Utah significantly spurred growth, and SCI became a permanent interdisciplinary research institute in 2000. The SCI Institute is now home to more than 150 faculty, students, and staff. The history of the SCI Institute is underpinned by a culture of multidisciplinary, collaborative research, which led to its emergence as an internationally recognized leader in the development and use of visualization, scientific computing, and image analysis research to solve important problems in a broad range of domains in biomedicine, science, and engineering. A particular hallmark of SCI Institute research is the creation of open source software systems, including the SCIRun scientific problem-solving environment, Seg3D, ImageVis3D, Uintah, ViSUS, Nektar++, VisTrails, FluoRender, and FEBio. At this point, the SCI Institute has made more than 50 software packages broadly available to the scientific community under open-source licensing and supports them through web pages, documentation, and user groups. While the vast majority of academic research software is written and maintained by graduate students, the SCI Institute employs several professional software developers to help create, maintain, and document robust, tested, well-engineered open source software. The story of how and why we worked, and often struggled, to make professional software engineers an integral part of an academic research institute is crucial to the larger story of the SCI Institute’s success in translational computer science (TCS).
Prediction of Femoral Head Coverage from Articulated Statistical Shape Models of Patients with Developmental Dysplasia of the Hip|
P. R. Atkins, P. Agrawal, J. D. Mozingo, K. Uemura, K. Tokunaga, C. L. Peters, S. Y. Elhabian, R. T. Whitaker, A. E. Anderson. In Journal of Orthopaedic Research, Wiley, 2021.
Developmental dysplasia of the hip (DDH) is commonly described as reduced femoral head coverage due to anterolateral acetabular deficiency. Although reduced coverage is the defining trait of DDH, more subtle and localized anatomic features of the joint are also thought to contribute to symptom development and degeneration. These features are challenging to identify using conventional approaches. Herein, we assessed the morphology of the full femur and hemi-pelvis using an articulated statistical shape model (SSM). The model determined the morphological and pose-based variations associated with DDH in a population of Japanese females and established which of these variations predict coverage. Computed tomography images of 83 hips from 47 patients were segmented for input into a correspondence-based SSM. The dominant modes of variation in the model initially represented scale and pose. After removal of these factors through individual bone alignment, femoral version and neck-shaft angle, pelvic curvature, and acetabular version dominated the observed variation. Femoral head oblateness and prominence of the acetabular rim and various muscle attachment sites of the femur and hemi-pelvis were found to predict 3D CT-based coverage measurements (R2=0.5-0.7 for the full bones, R2=0.9 for the joint).
Uncertainty Quantification in Brain Stimulation using UncertainSCI|
J. Tate, S. Rampersad, C. Charlebois, Z. Liu, J. Bergquist, D. White, L. Rupp, D. Brooks, A. Narayan, R. MacLeod. In Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Vol. 14, No. 6, Elsevier, pp. 1659-1660. 2021.
Predicting the effects of brain stimulation with computer models presents many challenges, including estimating the possible error from the propagation of uncertain input parameters through the model. Quantification and control of these errors through uncertainty quantification (UQ) provide statistics on the likely impact of parameter variation on solution accuracy, including total variance and sensitivity associated to each parameter. While the need and importance of UQ in clinical modeling is generally accepted, tools for implementing UQ techniques remain limited or inaccessible for many researchers.
Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study|
W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. Gillett, N. Angel, D. Chou, G. Plank, R. S. MacLeod. In Journal of Electrocardiology, 2021.
To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique.
We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth.
Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively.
We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.
A Nonparametric Approach for Estimating Three-Dimensional Fiber Orientation Distribution Functions (ODFs) in Fibrous Materials|
A. Rauff, L.H. Timmins, R.T. Whitaker, J.A. Weiss. In IEEE Transactions on Medical Imaging, 2021.
Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.
Integrin-Based Mechanosensing through Conformational Deformation|
T.P. Driscoll, T.C. Bidone, S.J. Ahn, A. Yu, A. Groisman, G.A. Voth, M.A. Schwartz. In Biophysical Journal, 2021.
Conversion of integrins from low to high affinity states, termed activation, is important in biological processes including immunity, hemostasis, angiogenesis and embryonic development. Integrin activation is regulated by large-scale conformational transitions from closed, low affinity states to open, high affinity states. While it has been suggested that substrate stiffness shifts the conformational equilibrium of integrin and governs its unbinding, here we address the role of integrin conformational activation in cellular mechanosensing. Comparison of WT vs activating mutants of integrin αVβ3 show that activating mutants shift cell spreading, FAK activation, traction stress and force on talin toward high stiffness values at lower stiffness. Although all activated integrin mutants showed equivalent binding affinity for soluble ligands, the β3 S243E mutant showed the strongest shift in mechanical responses. To understand this behavior, we used coarse-grained computational models derived from molecular level information. The models predicted that wild type integrin αVβ3 displaces under force, and that activating mutations shift the required force toward lower values, with S243E showing the strongest effect. Cellular stiffness sensing thus correlates with computed effects of force on integrin conformation. Together, these data identify a role for force-induced integrin conformational deformation in cellular mechanosensing.
Reducing Line-of-block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods|
A.S. Rababah, L.R. Bear, Y.S. Dogrusoz, W. Good, J. Bergquist, J. Stoks, R. MacLeod, K. Rjoob, M. Jennings, J. Mclaughlin, D. D. Finlay. In Computers in Biology and Medicine, Vol. 136, pp. 104666. 2021.
Electrocardiographic imaging is an imaging modality that has been introduced recently to help in visualizing the electrical activity of the heart and consequently guide the ablation therapy for ventricular arrhythmias. One of the main challenges of this modality is that the electrocardiographic signals recorded at the torso surface are contaminated with noise from different sources. Low amplitude leads are more affected by noise due to their low peak-to-peak amplitude. In this paper, we have studied 6 datasets from two torso tank experiments (Bordeaux and Utah experiments) to investigate the impact of removing or interpolating these low amplitude leads on the inverse reconstruction of cardiac electrical activity. Body surface potential maps used were calculated by using the full set of recorded leads, removing 1, 6, 11, 16, or 21 low amplitude leads, or interpolating 1, 6, 11, 16, or 21 low amplitude leads using one of the three interpolation methods – Laplacian interpolation, hybrid interpolation, or the inverse-forward interpolation. The epicardial potential maps and activation time maps were computed from these body surface potential maps and compared with those recorded directly from the heart surface in the torso tank experiments. There was no significant change in the potential maps and activation time maps after the removal of up to 11 low amplitude leads. Laplacian interpolation and hybrid interpolation improved the inverse reconstruction in some datasets and worsened it in the rest. The inverse forward interpolation of low amplitude leads improved it in two out of 6 datasets and at least remained the same in the other datasets. It was noticed that after doing the inverse-forward interpolation, the selected lambda value was closer to the optimum lambda value that gives the inverse solution best correlated with the recorded one.
Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism|
B. Zenger, W. W. Good, J. A. Bergquist, L. C. Rupp, M. Perez, G. J. Stoddard, V. Sharma, R. S. MacLeod. In Journal of Electrocardiology, Churchill Livingstone, 2021.
Acute myocardial ischemia has several characteristic ECG findings, including clinically detectable ST-segment deviations. However, the sensitivity and specificity of diagnosis based on ST-segment changes are low. Furthermore, ST-segment deviations have been shown to be transient and spontaneously recover without any indication the ischemic event has subsided.
Assess the transient recovery of ST-segment deviations on remote recording electrodes during a partial occlusion cardiac stress test and compare them to intramyocardial ST-segment deviations.
We used a previously validated porcineBZ experimental model of acute myocardial ischemia with controllable ischemic load and simultaneous electrical measurements within the heart wall, on the epicardial surface, and on the torso surface. Simulated cardiac stress tests were induced by occluding a coronary artery while simultaneously pacing rapidly or infusing dobutamine to stimulate cardiac function. Postexperimental imaging created anatomical models for data visualization and quantification. Markers of ischemia were identified as deviations in the potentials measured at 40% of the ST-segment. Intramural cardiac conduction speed was also determined using the inverse gradient method. We assessed changes in intramyocardial ischemic volume proportion, conduction speed, clinical presence of ischemia on remote recording arrays, and regional changes to intramyocardial ischemia. We defined the peak deviation response time as the time interval after onset of ischemia at which maximum ST-segment deviation was achieved, and ST-recovery time was the interval when ST deviation returned to below thresholded of ST elevation.
In both epicardial and torso recordings, the peak ST-segment deviation response time was 4.9±1.1 min and the ST-recovery time was approximately 7.9±2.5 min, both well before the termination of the ischemic stress. At peak response time, conduction speed was reduced by 50% and returned to near baseline at ST-recovery. The overall ischemic volume proportion initially increased, on average, to 37% at peak response time; however, it recovered only to 30% at the ST-recovery time. By contrast, the subepicardial region of the myocardial wall showed 40% ischemic volume at peak response time and recovered much more strongly to 25% as epicardial ST-segment deviations returned to baseline.
Our data show that remote ischemic signal recovery correlates with a recovery of the subepicardial myocardium, while subendocardial ischemic development persists.
|Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction,
X. Jiang, J. C. Font, J. A. Bergquist, B. Zenger, W. W. Good, D. H. Brooks, R. S. MacLeod, L. Wang. In Functional Imaging and Modeling of the Heart: 11th International Conference, In Functional Imaging and Modeling of the Heart: 11th International Conference, Vol. 12738, Springer Nature, pp. 471. 2021.
Accuracy of estimating the heart’s electrical activity with Electrocardiographic Imaging (ECGI) is challenging due to using an error-prone physics-based model (forward model). While getting better results than the traditional numerical methods following the underlying physics, modern deep learning approaches ignore the physics behind the electrical propagation in the body and do not allow the use of patientspecific geometry. We introduce a deep-learning-based ECGI framework capable of understanding the underlying physics, aware of geometry, and adjustable to patient-specific data. Using a variational autoencoder (VAE), we uncover the forward model’s parameter space, and when solving the inverse problem, these parameters will be optimized to reduce the errors in the forward model. In both simulation and real data experiments, we demonstrated the ability of the presented framework to provide accurate reconstruction of the heart’s electrical potentials and localization of the earliest activation sites.
|Uncertainty Quantification of the Effects of Segmentation Variability in ECGI,
J. D. Tate, W. W. Good, N. Zemzemi, M. Boonstra, P. van Dam, D. H. Brooks, A. Narayan, R. S. MacLeod. In Functional Imaging and Modeling of the Heart, Springer International Publishing, pp. 515--522. 2021.
Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.
The Electrocardiographic Forward Problem: A Benchmark Study|
J. A. Bergquist, W. W. Good, B. Zenger, J. D. Tate, L. C. Rupp, R. S. MacLeod. In Computers in Biology and Medicine, Vol. 134, Pergamon, pp. 104476. 2021.
Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies|
V. Vedam-Mai, K. Deisseroth, J. Giordano, G. Lazaro-Munoz, W. Chiong, N. Suthana, J. Langevin, J. Gill, W. Goodman, N. R. Provenza, C. H. Halpern, R. S. Shivacharan, T. N. Cunningham, S. A. Sheth, N. Pouratian, K. W. Scangos, H. S. Mayberg, A. Horn, K. A. Johnson, C. R. Butson, R. Gilron, C. de Hemptinne, R. Wilt, M. Yaroshinsky, S. Little, P. Starr, G. Worrell, P. Shirvalkar, E. Chang, J. Volkmann, M. Muthuraman, S. Groppa, A. A. Kühn, L. Li, M. Johnson, K. J. Otto, R. Raike, S. Goetz, C. Wu, P. Silburn, B. Cheeran, Y. J. Pathak, M. Malekmohammadi, A. Gunduz, J. K. Wong, S. Cernera, A. W. Shukla, A. Ramirez-Zamora, W. Deeb, A. Patterson, K. D. Foote, M. S. Okun. In Frontiers in Human Neuroscience, Vol. 15, pp. 169. 2021.
We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.
Identification of Deep Brain Stimulation Targets for Neuropathic Pain After Spinal Cord Injury Using Localized Increases in White Matter Fiber Cross‐Section|
S. R. Black, A. Janson, M. Mahan, J. Anderson, C. R. Butson. In Neuromodulation: Technology at the Neural Interface, John Wiley & Sons, Inc., 2021.
Materials and MethodsTo classify localized neurostructural changes associated with NP in the SCI population, we compared white matter fiber density (FD) and cross‐section (FC) between SCI subjects with NP (N = 17) and SCI subjects without NP (N = 15) using diffusion‐weighted magnetic resonance imaging (MRI). We then identified theoretical target locations for DBS using fiber bundles connected to significantly altered regions of white matter. Finally, we used computational models of DBS to determine if our theoretical target locations could be used to feasibly activate our fiber bundles of interest.
ResultsWe identified significant increases in FC in the splenium of the corpus callosum in pain subjects when compared to controls. We then isolated five fiber bundles that were directly connected to the affected region of white matter. Our models were able to predict that our fiber bundles of interest can be feasibly activated with DBS at reasonable stimulation amplitudes and with clinically relevant implantation approaches.
ConclusionsAltogether, we identified neuroarchitectural changes associated with NP in the SCI cohort and implemented a novel, evidence‐driven target selection approach for DBS to guide future research in neuromodulation treatment of NP after SCI.
|3D Model of Cell Migration and Proliferation in a Tissue Scaffold,
S. H. Campbell, T. Bidone. In Biophysical Journal, Vol. 120, No. 3, Elsevier, pp. 265a. 2021.
Tissue scaffolds restore tissue functionality without the limitations of transplants. However, successful tissue growth depends on the interplay between scaffold properties and cell activities. It has been previously reported that scaffold porosity and Young's modulus affect cell migration and tissue generation. However, how the geometrical and mechanical properties of a scaffold exactly interplay with cell processes remain poorly understood and are essential for successful tissue growth. We developed a 3D computational model that simulates cell migration and proliferation on a scaffold. The model generates an adjustable 3D porous scaffold environment with a defined pore size and Young modulus. Cells are treated as explicit spherical particles comparable in size to bone-marrow cells and are initially seeded randomly throughout the scaffold. Cells can create adhesions, proliferate, and independently migrate across pores in a random walk. Cell adhesions during migration follow the molecular-clutch mechanism, where traction force from the cells against the scaffold stiffness reinforces adhesions lifetime up to a threshold. We used the model to test how variations in cell proliferation rate, scaffold Young's modulus, and porosity affect cell migration speed. At a low proliferation rate (1 x 10−7 s−1), the spread of cell speeds is larger than at a high replication rate (1 x 10−6 s−1). A biphasic relation between Young's modulus and cell speed is also observed reflecting the molecular-clutch mechanism at the level of individual adhesions. These observations are consistent with previous reports regarding fibroblast migration on collagen-glycosaminoglycan scaffolds. Additionally, our model shows that similar cell diameters and pore diameter induces a crowding effect decreasing cell speed. The results from our study provide important insights about biophysical mechanisms that govern cell motility on scaffolds with different properties for tissue engineering applications.
|Prestin Generates Instantaneous Force in Outer Hair Cell Membranes,
J. Sandhu, T. Bidone, R. D. Rabbitt. In Biophysical Journal, Vol. 120, No. 3, 2021.
Hearing occurs from sound reaching the inner ear cochlea, where electromotile Outer Hair Cells (OHCs) amplify vibrations by elongating and contracting rapidly in response to auditory frequency changes in membrane potential. OHCs can generate force cycle-by-cycle at frequencies exceeding 50kHz, but precisely how this is achieved is unclear. Electromotility requires expression of the transmembrane protein, prestin, which facilitates the electromechanical conversion through action of the Coulomb force acting on the anion Cl- bound at the core of the protein. However, recent experimental data suggests the charge displacement is too slow to support sound amplification at auditory frequencies. As a consequence, prestin electromechanics remain unclear at the molecular level. We hypothesize that prestin instantaneously transmits stress to the membrane, which subsequently drives charge displacement, membrane deformation, and OHC shape changes. To test the hypothesis, we examined the conformational dynamics of prestin and its effects on the motion of lipids under: (1) isometric conditions and (2) constant force conditions in order to mimic different regimes of membrane loading. All-atom molecular dynamics simulations of the prestin dimer embedded in POPC membranes were run and the trajectories analyzed. We discovered that under isometric conditions, the presence of a chloride ion in the electric field increased residue fluctuations. This trend was not observed under constant force conditions, supporting the idea that isometric conditions cause instantaneous force to be generated in the membrane. The analysis allowed us to identify the molecular mechanisms by which prestin allows electromechanical amplification by OHCs in the cochlea.
|Computational Model of E-cadherin Clustering under Cortical Tension,
Y. Chen, C. McNabb, T. Bidone. In Biophysical Journal, Vol. 120, No. 3, Elsevier, pp. 236a. 2021.
E-cadherins are adhesion proteins that play a critical role in the formation of cell-cell junctions for several physiological processes, including tissue development and homeostasis. The formation of E-cadherin clusters involves extracellular trans-and cis-associations between cadherin ectodomains and stabilization through intracellular coupling with the contractile actomyosin cortex. The dynamic remodeling of cell-cell junctions largely depends on cortical tension, but previous modeling frameworks did not incorporate this effect. In order to gain insights into the effects of cortical tension on the dynamic properties of E-cadherin clusters, here we developed a computational model based on Brownian dynamics. The model considers individual cadherins as explicit point particles undergoing cycles of lateral diffusion on two parallel surfaces that mimic the membrane of neighboring cells. E-cadherins transit between …
|Area Available for Atrial Fibrillation to Propagate Is an Important Determinant of Recurrence After Ablation,
R. Kamali, J. Kump, E. Ghafoori, M. Lange, N. Hu, T. J. Bunch, D. J. Dosdall, R. S. Macleod, R. Ranjan. In JACC: Clinical Electrophysiology, Elsevier, 2021.
This study sought to evaluate atrial fibrillation (AF) ablation outcomes based on scar patterns and contiguous area available for AF wavefronts to propagate.
Electrocardiographic imaging for atrial fibrillation: a perspective from computer models and animal experiments to clinical value|
J. Salinet, R. Molero, F. S. Schlindwein, J. Karel, M. Rodrigo, J. L. Rojo-Álvarez, O. Berenfeld, A. M. Climent, B. Zenger, F. Vanheusden, J. G. S. Paredes, R. MacLeod, F. Atienza, M. S. Guillem, M. Cluitmans, P. Bonizzi. In Frontiers in Physiology, Vol. 12, Frontiers Media, April, 2021.
Salinet et al. Electrocardiographic Imaging for Atrial Fibrillation treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.
Estimation and validation of cardiac conduction velocity and wavefront reconstruction using epicardial and volumetric data|
W. W. Good, K. Gillette, B. Zenger, J. Bergquist, L. C. Rupp, J. D. Tate, D. Anderson, M. Gsell, G. Plank, R. S. Macleod. In IEEE Transactions on Biomedical Engineering, IEEE, 2021.
Objective: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). Methods: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. Results: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. Conclusion: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. Significance: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.