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As an emerging technology for continuous monitoring of a bounded domain, electrical impedance tomography (EIT) gains increasing popularity in various applications. Despite unprecedented progress, the EIT inverse solvers at the present stage are incompetent to guarantee sufficient fidelity as well as efficient investigation of the internal impedance dynamics. In this context, this paper introduces a spatiotemporal structure-aware sparse Bayesian learning (SA-SBL) framework for solving the time-continuous EIT inverse problems. Specifically, in the process of reconstructing EIT time sequence, both intra-frame spatial clustering and inter-frame temporal continuity are explored and exploited in an unsupervised manner by using hierarchical Bayesian model and structure-aware priors. A multiple measurement vector model is established to capture the spatiotemporal correlations and describe the underlying multi-dimensional reconstruction problem. The resultant large-scale inversion is efficiently solved by applying the approximate message passing to the expectation updating. A speedup ratio of O(N2M) is achieved compared with original SA-SBL. Simulation results indicate that, the proposed algorithm exhibits superior reconstruction performance to the existing methods, where the scores evaluated by the quantitative metrics are improved by at least 17%. The presented algorithm is envisioned to offer broader applicability, since it yields improved image quality and recovery efficiency.
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - 6 Feb 2020|
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- 1 Finished
Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography
1/02/17 → 31/05/18