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Any discrepancies between the available model and the patient in terms of body shape and electrode position lead to incorrectly displayed impedance distributions. This work addresses that problem by presenting and evaluating a method for 3D model generation of the thorax and any affixed electrodes based on handheld video-footage.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Therefore, a process was developed, providing users with the ability to capture a patient's chest and the attached electrodes via smartphone. Once data is collected, extracted images are used to generate a 3D model with a structure from motion approach and locate electrodes with ArUco markers. For the evaluation of the developed method, multiple tests were performed in laboratory environments, which were compared with manually created reference models and differences quantified based on mean distance, standard deviation, and maximum distance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The implemented workflow allows for automated model reconstruction based on videos or selected images captured with a handheld device. It generates sparse point clouds from which a surface mesh is reconstructed and returns relative coordinates of any identified ArUco marker. The average value for the mean distance error of two model generations was 5.4\u00a0mm while the mean standard deviation was 6.0\u00a0mm. The average runtime of twelve reconstructions was 5:17\u00a0min, with a minimal runtime of 3:22\u00a0min and a maximal runtime of 7:29\u00a0min.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The presented methods and results show that model reconstruction of a patient\u2019s thorax and applied electrodes at an emergency site is feasible with already available devices. This is a first step toward the automated generation of patient-specific reconstruction models for Electrical Impedance Tomography based on images recorded with handheld devices.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-022-02593-4","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T10:03:51Z","timestamp":1648721031000},"page":"1707-1716","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automated 3D thorax model generation using handheld video-footage"],"prefix":"10.1007","volume":"17","author":[{"given":"Nadine","family":"Dussel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5431-5206","authenticated-orcid":false,"given":"Reinhard","family":"Fuchs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2759-0245","authenticated-orcid":false,"given":"Andreas W.","family":"Reske","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6999-5024","authenticated-orcid":false,"given":"Thomas","family":"Neumuth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"2593_CR1","unstructured":"H\u00f6fer C, Lefering R (2020) Annual Report 2020 (in german). 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