eprintid: 10059459 rev_number: 24 eprint_status: archive userid: 608 dir: disk0/10/05/94/59 datestamp: 2018-10-30 13:57:47 lastmod: 2021-10-24 23:27:47 status_changed: 2018-10-30 13:57:47 type: article metadata_visibility: show creators_name: Adesokan, B creators_name: Jensen, B creators_name: Jin, B creators_name: Knudsen, K title: Acousto-Electric Tomography with Total Variation Regularization ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 keywords: acousto-electric tomography, reconstruction, total variation note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: We study the numerical reconstruction problem in acousto-electric tomography of recovering the conductivity distribution in a bounded domain from interior power density data. We propose a numerical method for recovering discontinuous conductivity distributions, by reformulating it as an optimization problem with L 1 fitting and total variation penalty subject to PDE constraints. We establish continuity and differentiability results for the forward map, the well-posedness of the optimization problem, and present an easy-to-implement and robust numerical method based on successive linearization, smoothing and iterative reweighing. Extensive numerical experiments are presented to illustrate the feasibility of the proposed approach. date: 2019-03 date_type: published publisher: Institute of Physics official_url: https://doi.org/10.1088/1361-6420/aaece5 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green article_type_text: Article verified: verified_manual elements_id: 1596725 doi: 10.1088/1361-6420/aaece5 lyricists_name: Jin, Bangti lyricists_id: BJINX59 actors_name: Jin, Bangti actors_id: BJINX59 actors_role: owner full_text_status: public publication: Inverse Problems volume: 35 number: 3 article_number: 035008 issn: 0266-5611 citation: Adesokan, B; Jensen, B; Jin, B; Knudsen, K; (2019) Acousto-Electric Tomography with Total Variation Regularization. Inverse Problems , 35 (3) , Article 035008. 10.1088/1361-6420/aaece5 <https://doi.org/10.1088/1361-6420%2Faaece5>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10059459/1/main.pdf