Simulations of Biological Systems
Multi-Physics Models of Cancer Cells
|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.
Quantifying the spatiotemporal influence of acute myocardial ischemia on volumetric conduction velocity|
W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. K. Gillette, M. A.F. Gsell, G. Plank, R. S. MacLeod. In Journal of Electrocardiology, Vol. 66, Churchill Livingstone, pp. 86-94. 2021.
MethodsWe estimated conduction velocity (CV) and ischemic stress prior to and throughout 20 episodes of experimentally induced ischemia in order to quantify the progression and correlation of volumetric conduction changes during ischemia. To estimate volumetric CV, we 1) reconstructed the activation wavefront; 2) calculated the elementwise gradient to approximate propagation direction; and 3) estimated conduction speed (CS) with an inverse-gradient technique.
ResultsWe found that acute ischemia induces significant conduction slowing, reducing the global median speed by 20 cm/s. We observed a biphasic response in CS (acceleration then deceleration) early in some ischemic episodes. Furthermore, we noted a high temporal correlation between ST-segment changes and CS slowing; however, when comparing these changes over space, we found only moderate correlation (corr. = 0.60).
DiscussionThis study is the first to report volumetric CS changes (acceleration and slowing) during episodes of acute ischemia in the whole heart. We showed that while CS changes progress in a similar time course to ischemic stress (measured by ST-segment shifts), the spatial overlap is complex and variable, showing extreme conduction slowing both in and around regions experiencing severe ischemia.
Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome|
K. A. Johnson, G. Duffley, D. Nesterovich Anderson, J. L. Ostrem, M. Welter, J. C. Baldermann, J. Kuhn, D. Huys, V. Visser-Vandewalle, T. Foltynie, L. Zrinzo, M. Hariz, A. F. G. Leentjens, A. Y. Mogilner, M. H. Pourfar, L. Almeida, A. Gunduz, K. D. Foote, M. S. Okun, C. R. Butson. In Brain, July, 2020.
Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate ‘reverse’ tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.
A systematic exploration of parameters affecting evoked intracranial potentials in patients with epilepsy|
B. Kundu, T. S. Davis, B. Philip, E. H. Smith, A. Arain, A. Peters, B. Newman, C. R. Butson, J. D. Rolston. In Brain Stimulation, Vol. 13, No. 5, pp. 1232-1244. 2020.
Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival|
S. P. Ponnapalli, M. W. Bradley, K. Devine, J. Bowen, S. E. Coppens, K. M. Leraas, B. A. Milash, F. Li, H. Luo, S. Qiu, K. Wu, H. Yang, C. T. Wittwer, C. A. Palmer, R. L. Jensen, J. M. Gastier-Foster, H. A. Hanson, J. S. Barnholtz-Sloan, O. Alter. In Applied Physics Letters (APL) Bioengineering, Vol. 4, No. 2, May, 2020.
Modeling of genomic profiles from the Cancer Genome Atlas (TCGA) by using recently developed mathematical frameworks has associated a genome-wide pattern of DNA copy-number alterations with a shorter, roughly one-year, median survival time in glioblastoma (GBM) patients. Here, to experimentally test this relationship, we whole-genome sequenced DNA from tumor samples of patients. We show that the patients represent the U.S. adult GBM population in terms of most normal and disease phenotypes. Intratumor heterogeneity affects ≈11% and profiling technology and reference human genome specifics affect <1% of the classifications of the tumors by the pattern, where experimental batch effects normally reduce the reproducibility, i.e., precision, of classifications based upon between one to a few hundred genomic loci by >30%. With a 2.25-year Kaplan–Meier median survival difference, a 3.5 univariate Cox hazard ratio, and a 0.78 concordance index, i.e., accuracy, the pattern predicts survival better than and independent of age at diagnosis, which has been the best indicator since 1950. The prognostic classification by the pattern may, therefore, help to manage GBM pseudoprogression. The diagnostic classification may help drugs progress to regulatory approval. The therapeutic predictions, of previously unrecognized targets that are correlated with survival, may lead to new drugs. Other methods missed this relationship in the roughly 3B-nucleotide genomes of the small, order of magnitude of 100, patient cohorts, e.g., from TCGA. Previous attempts to associate GBM genotypes with patient phenotypes were unsuccessful. This is a proof of principle that the frameworks are uniquely suitable for discovering clinically actionable genotype–phenotype relationships.
Activation robustness with directional leads and multi-lead configurations in deep brain stimulation|
A. P. Janson, D. N. Anderson, C. R. Butson. In Journal of Neural Engineering, Vol. 17, No. 2, IOP Publishing, pp. 026012. March, 2020.
Objective: Clinical outcomes from deep brain stimulation (DBS) can be highly variable, and two critical factors underlying this variability are the location and type of stimulation. In this study we quantified how robustly DBS activates a target region when taking into account a range of different lead designs and realistic variations in placement. The objective of the study is to assess the likelihood of achieving target activation.
|Personalized Virtual-heart Technology for Guiding the Ablation of Infarct-related Ventricular Tachycardia,
A. Prakosa, H.J. Arevalo, D. Deng, P.M. Boyle, P.P. Nikolov, H. Ashikaga, J.E. Blauer, E. Ghafoori, C.J. Park, R.C. Blake III, F.T. Han, R.S. MacLeod, H.R. Halperin, D.J. Callans, R. Ranjan, J. Chrispin, S. Nazarian,, N.A. Trayanova. In Nature Biomedical Engineering, Vol. 2, pp. 732–740. 2019.
Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.
The μDBS: Multiresolution, Directional Deep Brain Stimulation for Improved Targeting of Small Diameter Fibers|
D. N. Anderson, C. Anderson, N. Lanka, R. Sharma, C. R. Butson, B. W. Baker, A. D. Dorval. In Frontiers in Neuroscience, Vol. 13, October, 2019.
Directional deep brain stimulation (DBS) leads have recently been approved and used in patients, and growing evidence suggests that directional contacts can increase the therapeutic window by redirecting stimulation to the target region while avoiding side-effect-inducing regions. We outline the design, fabrication, and testing of a novel directional DBS lead, theμDBS, which utilizes microscale contacts to increase the spatial resolution of stimulation steering and improve the selectivity in targeting small diameter fibers. We outline the steps of fabrication of theμDBS, from an integrated circuit design to post-processing and validation testing. We tested the onboard digital circuitry for programming fidelity, characterized impedance for a variety of electrode sizes, and demonstrated functionality in a saline bath. In a computational experiment,we determined that reduced electrode sizes focus the stimulation effect on small, nearby fibers. Smaller electrode sizes allow for a relative decrease in small-diameter axon thresholds compared to thresholds of large-diameter fibers, demonstrating a focusing of the stimulation effect within small, and possibly therapeutic, fibers. This principle of selectivity could be useful in further widening the window of therapy. TheμDBS offers a unique, multi resolution design in which any combination of microscale contacts can be used together to function as electrodes of various shapes and sizes. Multiscale electrodes could be useful in selective neural targeting for established neurological targets and in exploring novel treatment targets for new neurological indications.
Which Two-dimensional Radiographic Measurements of Cam Femoroacetabular Impingement Best Describe the Three-dimensional Shape of the Proximal Femur?|
P. R. Atkins, Y. Shin, P. Agrawal, S. Y. Elhabian, R. T. Whitaker, J. A. Weiss, S. K. Aoki, C. L. Peters, A. E. Anderson. In Clinical Orthopaedics and Related Research, Vol. 477, No. 1, 2019.
|Interactive computation and visualization of deep brain stimulation effects using Duality,
J. Vorwerk, D. McCann, J. Krüger, C.R. Butson. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis, 2019.
Deep brain stimulation (DBS) is an established treatment for movement disorders such as Parkinson’s disease or essential tremor. Currently, the selection of optimal stimulation settings is performed by iteratively adjusting the stimulation parameters and is a time consuming procedure that requires multiple clinic visits of several hours. Recently, computational models to predict and visualize the effect of DBS have been developed with the goal to simplify and accelerate this procedure by providing visual guidance and such models have been made available also on mobile devices. However, currently available visualization software still either lacks mobility, i.e. it is running on desktop computers and no easily available in clinical praxis, or flexibility, as the simulations that are visualized on mobile devices have to be precomputed. The goal of the pipeline presented in this paper is to close this gap: Using Duality, a newly developed software for the interactive visualization of simulation results, we implemented a pipeline that allows to compute DBS simulations in near-real time and instantaneously visualize the result on a tablet computer. We carry out a performance analysis and present the results of a case study in which the pipeline was applied.