Markov chain Monte Carlo techniques and spatial-temporal modelling for medical EIT.

摘要: Many imaging problems such as with electrical impedance tomography (EIT) can be shown to inverse problems: that is either there no unique solution or the does not depend continuously on data. As a consequence of based measured data alone unstable, particularly if mapping between distribution and measurements also nonlinear in EIT. To deliver practical stable solution, it necessary make considerable use prior information regularization techniques. The role Bayesian approach therefore fundamental importance, especially when coupled Markov chain Monte Carlo (MCMC) sampling provide about behaviour.Spatial smoothing commonly used regularization. In human thorax EIT example considered here nonlinearity increases difficulty imaging, using only boundary data, leading reconstructions which are often rather too smooth. particular, medical resistivity usually contains substantial jumps at boundaries different anatomical regions. With spatial these masked by blurring.This paper focuses application monitor lung cardiac function uses explicit geometric regarding structure incorporates temporal correlation. Some simple properties assumed known, least reliably estimated from separate studies, whereas others voltage measurements. This structural formulation will allow direct estimation clinically important quantities, ejection fraction residual capacity, along assessment precision.