{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T09:01:44Z","timestamp":1777194104142,"version":"3.51.4"},"reference-count":53,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T00:00:00Z","timestamp":1523318400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["P 29514-N32"],"award-info":[{"award-number":["P 29514-N32"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In photoacoustic tomography, one is interested to recover the initial pressure distribution inside a tissue from the corresponding\nmeasurements of the induced acoustic wave\non the boundary of a region enclosing the tissue.\nIn the limited view problem, the wave boundary measurements are given\non the part of the boundary,\nwhereas in the full view problem, the measurements are known on the whole boundary.\nFor the full view problem, there exist various fast and robust reconstruction methods. These methods give severe reconstruction\nartifacts when they are applied directly to the limited view data.\nOne approach for reducing such artefacts is trying to extend the limited view data to the\nwhole region boundary, and then use existing reconstruction methods for the full view data.\nIn this paper, we propose an operator learning approach for constructing an\noperator that gives an approximate extension of the limited view data. We consider the behavior of a reconstruction formula on the extended limited view data that is given\nby our proposed approach. Approximation errors of our approach are analyzed.\nWe also present numerical results with the proposed extension approach supporting our theoretical analysis.<\/jats:p>","DOI":"10.1515\/cmam-2018-0008","type":"journal-article","created":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T09:34:39Z","timestamp":1523439279000},"page":"749-764","source":"Crossref","is-referenced-by-count":9,"title":["Operator Learning Approach for the Limited View Problem in Photoacoustic Tomography"],"prefix":"10.1515","volume":"19","author":[{"given":"Florian","family":"Dreier","sequence":"first","affiliation":[{"name":"Department of Mathematics , University of Innsbruck , Technikerstra\u00dfe 13, 6020 Innsbruck , Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-3995","authenticated-orcid":false,"given":"Sergiy","family":"Pereverzyev Jr","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology , Medical University of Innsbruck , Anichstra\u00dfe 35, 6020 Innsbruck , Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5715-0331","authenticated-orcid":false,"given":"Markus","family":"Haltmeier","sequence":"additional","affiliation":[{"name":"Department of Mathematics , University of Innsbruck , Technikerstra\u00dfe 13, 6020 Innsbruck , Austria"}]}],"member":"374","published-online":{"date-parts":[[2018,4,10]]},"reference":[{"key":"2023033110105312608_j_cmam-2018-0008_ref_001_w2aab3b7d742b1b6b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"M.  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Nguyen,\nOn artifacts in limited data spherical Radon transform: Curved observation surface,\nInverse Problems 32 (2016), no. 1, Article ID 015012.","DOI":"10.1088\/0266-5611\/32\/1\/015012"},{"key":"2023033110105312608_j_cmam-2018-0008_ref_005_w2aab3b7d742b1b6b1ab2b1b5Aa","doi-asserted-by":"crossref","unstructured":"P.  Beard,\nBiomedical photoacoustic imaging,\nInterf. Focus 1 (2011), no. 4, 602\u2013631.","DOI":"10.1098\/rsfs.2011.0028"},{"key":"2023033110105312608_j_cmam-2018-0008_ref_006_w2aab3b7d742b1b6b1ab2b1b6Aa","doi-asserted-by":"crossref","unstructured":"P.  Burgholzer, J.  Bauer-Marschallinger, H.  Gr\u00fcn, M.  Haltmeier and G.  Paltauf,\nTemporal back-projection algorithms for photoacoustic tomography with integrating line detectors,\nInverse Problems 23 (2007), no. 6, S65\u2013S80.","DOI":"10.1088\/0266-5611\/23\/6\/S06"},{"key":"2023033110105312608_j_cmam-2018-0008_ref_007_w2aab3b7d742b1b6b1ab2b1b7Aa","doi-asserted-by":"crossref","unstructured":"P.  Burgholzer, G. J.  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