D.J. Swenson, S.E. Geneser, J.G. Stinstra, R.M. Kirby, R.S. MacLeod. Cardiac Position Sensitivity Study in the Electrocardiographic Forward Problem Using Stochastic Collocation and Boundary Element Methods, In Annals of Biomedical Engineering, Vol. 39, No. 12, pp. 2900--2910. 2011.
PubMed ID: 21909818
PubMed Central ID: PMC336204
The electrocardiogram (ECG) is ubiquitously employed as a diagnostic and monitoring tool for patients experiencing cardiac distress and/or disease. It is widely known that changes in heart position resulting from, for example, posture of the patient (sitting, standing, lying) and respiration significantly affect the body-surface potentials; however, few studies have quantitatively and systematically evaluated the effects of heart displacement on the ECG. The goal of this study was to evaluate the impact of positional changes of the heart on the ECG in the specific clinical setting of myocardial ischemia. To carry out the necessary comprehensive sensitivity analysis, we applied a relatively novel and highly efficient statistical approach, the generalized polynomial chaos-stochastic collocation method, to a boundary element formulation of the electrocardiographic forward problem, and we drove these simulations with measured epicardial potentials from whole-heart experiments. Results of the analysis identified regions on the body-surface where the potentials were especially sensitive to realistic heart motion. The standard deviation (STD) of ST-segment voltage changes caused by the apex of a normal heart, swinging forward and backward or side-to-side was approximately 0.2 mV. Variations were even larger, 0.3 mV, for a heart exhibiting elevated ischemic potentials. These variations could be large enough to mask or to mimic signs of ischemia in the ECG. Our results suggest possible modifications to ECG protocols that could reduce the diagnostic error related to postural changes in patients possibly suffering from myocardial ischemia.
D. Wang, R.M. Kirby, C.R. Johnson. Finite Element Based Discretization and Regularization Strategies for 3D Inverse Electrocardiography, In IEEE Transactions for Biomedical Engineering, Vol. 58, No. 6, pp. 1827--1838. 2011.
PubMed ID: 21382763
PubMed Central ID: PMC3109267
D. Wang, R.M. Kirby, R.S. Macleod, C.R. Johnson. An optimization framework for inversely estimating myocardial transmembrane potentials and localizing ischemia, In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 1680--1683. 2011.
PubMed ID: 22254648
PubMed Central ID: PMC3336368
C. Yang, D. Xiu, R.M. Kirby. Visualization of Covariance and Cross-covariance Field, In International Journal for Uncertainty Quantification, Vol. 3, No. 1, pp. 25--38. 2011.
T. Etiene, L.G. Nonato, C.E. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva. Topology Verification for Isosurface Extraction, SCI Technical Report, No. UUSCI-2010-003, SCI Institute, University of Utah, 2010.
S.E. Geneser, J.D. Hinkle, R.M. Kirby, Brian Wang, B. Salter, S. Joshi. Quantifying Variability in Radiation Dose Due to Respiratory-Induced Tumor Motion, In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2010.
P.K. Jimack, R.M. Kirby. Towards the Development on an h-p-Refinement Strategy Based Upon Error Estimate Sensitivity, In Computers and Fluids, Vol. 46, No. 1, pp. 277--281. 2010.
The use of (a posteriori) error estimates is a fundamental tool in the application of adaptive numerical methods across a range of fluid flow problems. Such estimates are incomplete however, in that they do not necessarily indicate where to refine in order to achieve the most impact on the error, nor what type of refinement (for example h-refinement or p-refinement) will be best. This paper extends preliminary work of the authors (Comm Comp Phys, 2010;7:631–8), which uses adjoint-based sensitivity estimates in order to address these questions, to include application with p-refinement to arbitrary order and the use of practical a posteriori estimates. Results are presented which demonstrate that the proposed approach can guide both the h-refinement and the p-refinement processes, to yield improvements in the adaptive strategy compared to the use of more orthodox criteria.
H. Mirzaee, J.K. Ryan, R.M. Kirby. Quantificiation of Errors Introduced in the Numerical Approximation and Implementation of Smoothness-Increasing Accuracy Conserving (SIAC) Filtering of Discontinuous Galerkin (DG) Fields, In Journal of Scientific Computing, Vol. 45, pp. 447-470. 2010.
M. Steffen, R.M. Kirby, M. Berzins. Decoupling and Balancing of Space and Time Errors in the Material Point Method (MPM), In International Journal for Numerical Methods in Engineering, Vol. 82, No. 10, pp. 1207--1243. 2010.
P.E.J. Vos, S.J. Sherwin, R.M. Kirby. h-p Efficiently: Implementing Finite and Spectral/hp Element Methods to Achieve Optimal Performance for Low- and High-Order Discretisations, In Journal of Computational Physics, Vol. 229, No. 13, pp. 5161--5181. 2010.
D.F. Wang, R.M. Kirby, C.R. Johnson. Resolution Strategies for the Finite-Element-Based Solution of the ECG Inverse Problem, In IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp. 220--237. February, 2010.
D.F. Wang, R.M. Kirby, R.S. MacLeod, C.R. Johnson. A New Family of Variational-Form-Based Regularizers for Reconstructing Epicardial Potentials from Body-Surface Mapping, In Computing in Cardiology, 2010, pp. 93--96. 2010.
S.E. Geneser, R.M. Kirby, Brian Wang, B. Salter, S. Joshi. Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy, In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 688--700. 2009.
PubMed ID: 19694304
H. Mirzaee, C. Eskilsson, S.J. Sherwin, R.M. Kirby. Comparison of Consistent Integration Versus Adaptive Quadrature for Taming Aliasing Errors, SCI Technical Report, No. UUSCI-2009-008, SCI Institute, University of Utah, 2009.
J.S. Preston, T. Tasdizen, C.M. Terry, A.K. Cheung, R.M. Kirby. Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability, In IEEE Transactions on Biomedical Engineering, Vol. 56, No. 3, Note: Epub 2008 Dec 2, pp. 609--620. 2009.
PubMed ID: 19272865
A.R. Sanderson, M.D. Meyer, R.M. Kirby, C.R. Johnson. A Framework for Exploring Numerical Solutions of Advection Reaction Diffusion Equations using a GPU Based Approach, In Journal of Computing and Visualization in Science, Vol. 12, pp. 155--170. 2009.
A. Vo, S. Vakkalanka, M. Delisi, G. Gopalakrishnan, R.M. Kirby, R. Thakur. Formal Verification of Practical MPI Programs, In Proceedings of 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), Raleigh, NC, pp. 261--270. February 14-18, 2009.
D.F. Wang, R.M. Kirby, C.R. Johnson. Finite Element Discretization Strategies for the Inverse Electrocardiographic (ECG) Problem, In Proceedings of the 11th World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, Vol. 25/2, pp. 729-732. September, 2009.
D.F. Wang, R.M. Kirby, C.R. Johnson. Finite Element Refinements for Inverse Electrocardiography: Hybrid-Shaped Elements, High-Order Element Truncation and Variational Gradient Operator, In Proceeding of Computers in Cardiology 2009, Park City, September, 2009.
S.E. Geneser, R.M. Kirby, R.S. MacLeod. Application of Stochastic Finite Element Methods to Study the Sensitivity of ECG Forward Modeling to Organ Conductivity, In IEEE Transations on Biomedical Engineering, Vol. 55, No. 1, pp. 31--40. January, 2008.