title: Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography.
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.
authors.affiliation: Institute of Technical Medicine, Faculty of Medical and Life Sciences, Furtwangen University of Applied Sciences, VS-Schwenningen, Germany.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Javaherian
authors.forename: Manuchehr
authors.initials: M
authors.lastname: Soleimani
authors.forename: Knut
authors.initials: K
authors.lastname: Moeller
mesh_terms: D000465:Algorithms; D001940:Breast / Q000503:physiopathology; D001943:Breast Neoplasms / Q000503:physiopathology; D003198:Computer Simulation; D017097:Electric Impedance; D004566:Electrodes; D020342:Finite Element Analysis; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D013997:Time Factors; D014054:Tomography / Q000295:instrumentation / Q000379:methods*