{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T07:59:49Z","timestamp":1792569589256,"version":"4.5.0"},"reference-count":32,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["BE6526\/1-1"],"award-info":[{"award-number":["BE6526\/1-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"crossref","award":["101021526-BREATHE"],"award-info":[{"award-number":["101021526-BREATHE"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,2,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Time-difference electrical impedance tomography (EIT) is gaining widespread use for bedside lung monitoring in intensive care patients suffering from lung-related diseases. It involves collecting voltage measurements from electrodes placed on the patient\u2019s thorax, which are then used to reconstruct impedance images. This study investigates how incorporating anatomical information from CT data into the widely used Graz consensus reconstruction algorithm affects EIT (GREIT) images and improves their interpretability.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Based on clinically motivated lung state scenarios, we simulated EIT measurements to assess how the GREIT parameters influence the result of EIT image reconstruction, particularly with respect to noise performance and image accuracy. We introduce quality measures that allow us to perform a quantitative assessment of reconstruction quality. We incorporate the anatomical features of a patient from CT data by customizing the background conductivity and the distribution of GREIT training targets.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Our analysis confirmed that unphysiological background conductivity assumptions can lead to misleading EIT images, whereas physiological values, although more accurate, come with higher noise sensitivity. By increasing the number of GREIT training targets inside the lung and adapting the respective weighting radius, we significantly improved the anatomical accuracy of the EIT images. When applied to clinical EIT data from a representative acute respiratory distress syndrome patient, these adjustments in the reconstruction setup substantially enhanced the interpretability of the resulting EIT images.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Incorporating CT-based anatomical data in the GREIT reconstruction significantly enhances the clinical applicability of EIT in lung monitoring. The improved interpretability of EIT images facilitates better-informed clinical decisions and the individualized adjustment of ventilation strategies for critically ill patients.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae4289","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:54:38Z","timestamp":1770332078000},"page":"025009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Anatomically informed GREIT reconstruction: improving EIT imaging for lung monitoring"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2660-464X","authenticated-orcid":true,"given":"Maximilian","family":"Ludwig","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-2265","authenticated-orcid":true,"given":"Carolin M","family":"Eichinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3132-589X","authenticated-orcid":false,"given":"Armin","family":"Sablewski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7712-6539","authenticated-orcid":true,"given":"In\u00e9z","family":"Frerichs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2026-7629","authenticated-orcid":true,"given":"Tobias","family":"Becher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7419-3384","authenticated-orcid":false,"given":"Wolfgang A","family":"Wall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"pmeaae4289bib1","doi-asserted-by":"publisher","first-page":"S35","DOI":"10.1088\/0967-3334\/30\/6\/S03","type":"journal-article","article-title":"GREIT: a unified approach to 2D linear EIT reconstruction of lung images","volume":"30","author":"Adler","year":"2009","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib2","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1109\/42.491418","type":"journal-article","article-title":"Electrical impedance tomography: Regularized imaging and contrast detection","volume":"15","author":"Adler","year":"1996","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmeaae4289bib3","doi-asserted-by":"publisher","first-page":"S25","DOI":"10.1088\/0967-3334\/27\/5\/S03","type":"journal-article","article-title":"Uses and abuses of EIDORS. an extensible software base for EIT","volume":"27","author":"Adler","year":"2006","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib4","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1088\/0967-3334\/36\/6\/1119","type":"journal-article","article-title":"Tracking boundary movement and exterior shape modelling in lung EIT imaging","volume":"36","author":"Biguri","year":"2015","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib5","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1109\/TBME.2017.2659540","type":"journal-article","article-title":"A versatile noise performance metric for electrical impedance tomography algorithms","volume":"64","author":"Braun","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmeaae4289bib6","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1109\/10.35300","type":"journal-article","article-title":"Electrode models for electric current computed tomography","volume":"36","author":"Cheng","year":"1989","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmeaae4289bib7","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1007\/s00134-009-1447-y","type":"journal-article","article-title":"Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography","volume":"35","author":"Costa","year":"2009","journal-title":"Intensive Care Med."},{"key":"pmeaae4289bib8","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1002\/mp.13458","type":"journal-article","article-title":"Automatic multiorgan segmentation in thorax CT images using U-net-GAN","volume":"46","author":"Dong","year":"2019","journal-title":"Med. Phys."},{"key":"pmeaae4289bib9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1136\/thoraxjnl-2016-208357","type":"journal-article","article-title":"Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations. Consensus statement of the translational EIT development study group","volume":"72","author":"TREND study group","year":"2017","journal-title":"Thorax"},{"key":"pmeaae4289bib10","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1088\/0967-3334\/34\/6\/579","type":"journal-article","article-title":"Uniform background assumption produces misleading lung EIT images","volume":"34","author":"Grychtol","year":"2013","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0103045","type":"journal-article","article-title":"Functional validation and comparison framework for EIT lung imaging","volume":"9","author":"Grychtol","year":"2014","journal-title":"PLoS One"},{"key":"pmeaae4289bib12","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1109\/TMI.2012.2200904","type":"journal-article","article-title":"Impact of model shape mismatch on reconstruction quality in electrical impedance tomography","volume":"31","author":"Grychtol","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmeaae4289bib13","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1088\/0967-3334\/37\/6\/785","type":"journal-article","article-title":"3D EIT image reconstruction with GREIT","volume":"37","author":"Grychtol","year":"2016","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib14","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2021.676118","type":"journal-article","article-title":"Using artificial intelligence for automatic segmentation of CT lung images in acute respiratory distress syndrome","volume":"12","author":"Herrmann","year":"2021","journal-title":"Front. Physiol."},{"key":"pmeaae4289bib15","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0186-2022","type":"journal-article","article-title":"The oesophageal balloon for respiratory monitoring in ventilated patients: updated clinical review and practical aspects","volume":"32","author":"Jonkman","year":"2023","journal-title":"Eur. Respir. Rev."},{"key":"pmeaae4289bib16","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1186\/s13054-024-05130-8","type":"journal-article","article-title":"Regional ventilation dynamics of electrical impedance tomography validated with four-dimensional computed tomography: single-center, prospective, observational study","volume":"28","author":"Katayama","year":"2024","journal-title":"Crit. Care"},{"key":"pmeaae4289bib17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jcrc.2016.04.028","type":"journal-article","article-title":"Electrical impedance tomography in adult patients undergoing mechanical ventilation: a systematic review","volume":"35","author":"Kobylianskii","year":"2016","journal-title":"J. Crit. Care"},{"key":"pmeaae4289bib18","article-title":"Part 1: The reconstruction problem","author":"Lionheart","year":"2005","type":"book"},{"key":"pmeaae4289bib19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/bja\/aeg085","type":"journal-article","article-title":"New concepts of atelectasis during general anaesthesia","volume":"91","author":"Magnusson","year":"2003","journal-title":"Br. J. Anaesth."},{"key":"pmeaae4289bib20","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1088\/0957-0233\/13\/12\/310","type":"journal-article","article-title":"A Matlab toolkit for three-dimensional electrical impedance tomography. a contribution to the electrical impedance and diffuse optical reconstruction software project","volume":"13","author":"Polydorides","year":"2002","journal-title":"Meas. Sci. Technol."},{"key":"pmeaae4289bib21","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1152\/japplphysiol.00313.2025","type":"journal-article","article-title":"Patient-specific prediction of regional lung mechanics in patients with ARDS with physics-based models: a validation study","volume":"139","author":"Rixner","year":"2025","journal-title":"J. Appl. Physiol."},{"key":"pmeaae4289bib22","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1152\/japplphysiol.00236.2016","type":"journal-article","article-title":"Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses","volume":"122","author":"Roth","year":"2017","journal-title":"J. Appl. Physiol."},{"key":"pmeaae4289bib23","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1186\/s13054-024-05173-x","type":"journal-article","article-title":"Electrical impedance tomography monitoring in adult ICU patients: state-of-the-art, recommendations for standardized acquisition, processing, and clinical use, and future directions","volume":"28","author":"Scaramuzzo","year":"2024","journal-title":"Crit. Care"},{"key":"pmeaae4289bib24","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1088\/0967-3334\/37\/9\/1605","type":"journal-article","article-title":"Compensation for large thorax excursions in EIT imaging","volume":"37","author":"Schullcke","year":"2016","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib25","author":"Simini","year":"2018","type":"book"},{"key":"pmeaae4289bib26","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1137\/0152060","type":"journal-article","article-title":"Existence and uniqueness for electrode models for electric current computed tomography","volume":"52","author":"Somersalo","year":"1992","journal-title":"SIAM J. Appl. Math."},{"key":"pmeaae4289bib27","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aab513","type":"journal-article","article-title":"Clinical performance of a novel textile interface for neonatal chest electrical impedance tomography","volume":"39","author":"Sophocleous","year":"2018","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0182215","type":"journal-article","article-title":"Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution. Comparison to 4-dimensional computed tomography in a porcine model","volume":"12","author":"Th\u00fcrk","year":"2017","journal-title":"PLoS One"},{"key":"pmeaae4289bib29","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab248e","type":"journal-article","article-title":"Influence of reconstruction settings in electrical impedance tomography on figures of merit and physiological parameters","volume":"40","author":"Th\u00fcrk","year":"2019","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib30","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024","type":"journal-article","article-title":"TotalSegmentator: robust segmentation of 104 anatomic structures in CT images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol. Artif. Intell."},{"key":"pmeaae4289bib31","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ada8f1","type":"journal-article","article-title":"Ventilation-perfusion matching in early-stage of prone position ventilation: a prospective cohort study between COVID-19 ARDS and ARDS from other etiologies","volume":"13","author":"Yang","year":"2025","journal-title":"Physiol. Meas."},{"key":"pmeaae4289bib32","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1088\/0967-3334\/35\/6\/1083","type":"journal-article","article-title":"The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation","volume":"35","author":"Zhao","year":"2014","journal-title":"Physiol. Meas."}],"container-title":["Physiological Measurement"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T11:03:23Z","timestamp":1771239803000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,16]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,16]]},"published-print":{"date-parts":[[2026,2,27]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6579\/ae4289","relation":{},"ISSN":["0967-3334","1361-6579"],"issn-type":[{"value":"0967-3334","type":"print"},{"value":"1361-6579","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,16]]},"assertion":[{"value":"Anatomically informed GREIT reconstruction: improving EIT imaging for lung monitoring","name":"article_title","label":"Article Title"},{"value":"Physiological Measurement","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-08-04","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-16","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}