Uncertainty Visualization in Forward and Inverse Cardiac Models.
Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.
Brett M
BM
Burton
Bioengineering Department, University of Utah, Salt Lake City, Utah, USA.
Burak
B
Erem
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Kristin
K
Potter
Computational Radiology Laboratory, Children's Hospital, Boston, MA, USA.
Paul
P
Rosen
Chris R
CR
Johnson
Dana H
DH
Brooks
Rob S
RS
Macleod