Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography

A Javaherian, Manuchehr Soleimani, K Moeller

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
146 Downloads (Pure)

Abstract

This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT.
Original languageEnglish
Pages (from-to)43–66
JournalPhysiological Measurement
Volume36
Issue number43
Early online date12 Dec 2014
DOIs
Publication statusPublished - Jan 2015

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