The ability to reliably detect coronary artery disease based on the acousticnoises produced by a stenosis can provide a simple, non-invasive technique for diagnosis.Current research exploits the shear wave fields in b...The ability to reliably detect coronary artery disease based on the acousticnoises produced by a stenosis can provide a simple, non-invasive technique for diagnosis.Current research exploits the shear wave fields in body tissue to detect andanalyze coronary stenoses. The methods and ideas outlined in earlier efforts [6] includinga mathematical model utilizing an internal strain variable approximation tothe quasi-linear viscoelastic constitutive equation proposed by Fung in [19] is extendedhere. As an initial investigation, a homogeneous two-dimensional viscoelastic geometryis considered. Being uniform in θ, this geometry behaves as a one dimensionalmodel, and the results generated from it are compared to the one dimensional resultsfrom [6]. To allow for different assumptions on the elastic response, several variationsof the model are considered. A statistical significance test is employed to determine ifthe more complex models are significant improvements. After calibrating the modelwith a comparison to previous findings, more complicated geometries are considered.Simulations involving a heterogeneous geometrywith a uniformring running throughthe original medium, a θ-dependent model which considers a rigid partial occlusionformed along the inner radius of the geometry, and a model which combines the ringand occlusion are presented.展开更多
We consider the problem of detecting cardiac artery occlusions using stenosis driven viscoelastic(VE)waves propagated through biotissue to body surface sensors.We investigate possible statistical model formulations(o...We consider the problem of detecting cardiac artery occlusions using stenosis driven viscoelastic(VE)waves propagated through biotissue to body surface sensors.We investigate possible statistical model formulations(ordinary least squares(OLS),generalized least squares(GLS))and post analysis techniques(residual plots)to ascertain uncertainty in estimates as well as validity of the statistical models as part of a methodology for stenosis detection using viscoelastic waves.展开更多
基金the U.S.Air Force Office of Scientific Research under grant AFOSR-FA9550-04-1-0220 and in part by The David and Lucille Packard Foundation.
文摘The ability to reliably detect coronary artery disease based on the acousticnoises produced by a stenosis can provide a simple, non-invasive technique for diagnosis.Current research exploits the shear wave fields in body tissue to detect andanalyze coronary stenoses. The methods and ideas outlined in earlier efforts [6] includinga mathematical model utilizing an internal strain variable approximation tothe quasi-linear viscoelastic constitutive equation proposed by Fung in [19] is extendedhere. As an initial investigation, a homogeneous two-dimensional viscoelastic geometryis considered. Being uniform in θ, this geometry behaves as a one dimensionalmodel, and the results generated from it are compared to the one dimensional resultsfrom [6]. To allow for different assumptions on the elastic response, several variationsof the model are considered. A statistical significance test is employed to determine ifthe more complex models are significant improvements. After calibrating the modelwith a comparison to previous findings, more complicated geometries are considered.Simulations involving a heterogeneous geometrywith a uniformring running throughthe original medium, a θ-dependent model which considers a rigid partial occlusionformed along the inner radius of the geometry, and a model which combines the ringand occlusion are presented.
基金was supported in part by the U.S.Air Force Office of Scientific Research under grant AFOSR-FA9550-08-1-0147in part by the National Institute of Allergy and Infectious Disease under grant NIAID 9R01AI071915-05。
文摘We consider the problem of detecting cardiac artery occlusions using stenosis driven viscoelastic(VE)waves propagated through biotissue to body surface sensors.We investigate possible statistical model formulations(ordinary least squares(OLS),generalized least squares(GLS))and post analysis techniques(residual plots)to ascertain uncertainty in estimates as well as validity of the statistical models as part of a methodology for stenosis detection using viscoelastic waves.
