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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Ultrasound (US) imaging is valued for its safety, affordability, and accessibility, but its low spatial resolution and operator dependence limit its diagnostic capabilities. Tomographic imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) offer high-resolution 3D visualization but are cost-prohibitive and complex. Ultrasound-based tomographic imaging aims to combine the advantages of both modalities, potentially democratizing access to advanced imaging. A scoping review was conducted following PRISMA-SR guidelines. Articles were identified through searches in PubMed MEDLINE, Embase, Scopus, and arXiv from inception to July 2025. Eligibility criteria included full-text original studies focused on ultrasound-based tomographic imaging generation or reconstruction methods. Out of 8256 identified articles, 86 met the inclusion criteria. Studies examined four imaging modalities: photoacoustic tomography (36%), ultrasound computed tomography (36%), 3D reconstruction (20%), and synthetic imaging (7%). Deep learning algorithms (67%) were the most common, followed by iterative reconstruction algorithms (9%), and other methods. The breast (17%), brain (16%), and blood vessels (14%) were the most studied anatomical regions. This review highlights advancements in ultrasound-based tomographic imaging, driven by deep learning innovations. Despite progress, the field is still in its infancy, and challenges remain in clinical adoption, particularly in standardization and validating performance. Future research should focus on improving algorithm efficiency, generalizability, and validation.<\/jats:p>","DOI":"10.1007\/s10278-025-01674-5","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T15:46:49Z","timestamp":1760456809000},"page":"3452-3485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review"],"prefix":"10.1007","volume":"39","author":[{"given":"Daniel","family":"de Wilde","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aron","family":"Alakmeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Zanier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaele","family":"Da Mutten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anatol","family":"Aicher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustav","family":"Burstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Edstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Elmi-Terander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanos","family":"Voglis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Regli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Serra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1039-2098","authenticated-orcid":false,"given":"Victor E.","family":"Staartjes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"1674_CR1","doi-asserted-by":"publisher","unstructured":"A. Carovac, F. Smajlovic, and D. Junuzovic, \"Application of ultrasound in medicine,\" (in eng), Acta Inform Med, vol. 19, no. 3, pp. 168-71, Sep 2011, https:\/\/doi.org\/10.5455\/aim.2011.19.168-171.","DOI":"10.5455\/aim.2011.19.168-171"},{"key":"1674_CR2","doi-asserted-by":"publisher","unstructured":"A. E. J. Powles, D. J. Martin, I. T. P. Wells, and C. R. Goodwin, \"Physics of ultrasound,\" Anaesthesia and Intensive Care Medicine, vol. 19, no. 4, pp. 202\u2013205, 2018\/04\/01\/ 2018. https:\/\/doi.org\/10.1016\/j.mpaic.2018.01.005.","DOI":"10.1016\/j.mpaic.2018.01.005"},{"key":"1674_CR3","doi-asserted-by":"publisher","unstructured":"G. Stasi and E. Ruoti, \"A critical evaluation in the delivery of the ultrasound practice: The point of view of the radiologist,\" Italian Journal of Medicine, vol. 9, 03\/20 2015. https:\/\/doi.org\/10.4081\/itjm.2015.502.","DOI":"10.4081\/itjm.2015.502"},{"key":"1674_CR4","doi-asserted-by":"publisher","unstructured":"D. J. Brenner and E. J. Hall, \"Computed tomography\u2014an increasing source of radiation exposure,\" New England Journal of Medicine, vol. 357, no. 22, pp. 2277\u20132284, 2007\/11\/29 2007. https:\/\/doi.org\/10.1056\/NEJMra072149.","DOI":"10.1056\/NEJMra072149"},{"key":"1674_CR5","doi-asserted-by":"publisher","unstructured":"A. Sodickson et al., \"Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults,\" (in eng), Radiology, vol. 251, no. 1, pp. 175-84, Apr 2009, https:\/\/doi.org\/10.1148\/radiol.2511081296.","DOI":"10.1148\/radiol.2511081296"},{"key":"1674_CR6","doi-asserted-by":"publisher","unstructured":"V. P. Grover, J. M. Tognarelli, M. M. Crossey, I. J. Cox, S. D. Taylor-Robinson, and M. J. 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Gao, \"Learned parameters and increment for iterative photoacoustic image reconstruction via deep learning,\" (in eng), Annu Int Conf IEEE Eng Med Biol Soc, vol. 2021, pp. 2989-2992, Nov 2021, https:\/\/doi.org\/10.1109\/embc46164.2021.9630545.","DOI":"10.1109\/embc46164.2021.9630545"},{"key":"1674_CR35","doi-asserted-by":"publisher","unstructured":"Y. Zhang, Y. Wang, and C. Zhang, \"Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,\" Ultrasonics, vol. 52, no. 8, pp. 1046\u20131055, 2012\/12\/01\/ 2012, .https:\/\/doi.org\/10.1016\/j.ultras.2012.08.012","DOI":"10.1016\/j.ultras.2012.08.012"},{"key":"1674_CR36","doi-asserted-by":"publisher","unstructured":"H. Lan, D. Jiang, F. Gao, and F. Gao, \"Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel,\" Photoacoustics, vol. 22, p. 100270, 2021\/06\/01\/ 2021, https:\/\/doi.org\/10.1016\/j.pacs.2021.100270.","DOI":"10.1016\/j.pacs.2021.100270"},{"key":"1674_CR37","doi-asserted-by":"publisher","unstructured":"S. Antholzer, M. Haltmeier, and J. Schwab, \"Deep learning for photoacoustic tomography from sparse data,\" (in eng), Inverse Probl Sci Eng, vol. 27, no. 7, pp. 987-1005, 2019, https:\/\/doi.org\/10.1080\/17415977.2018.1518444.","DOI":"10.1080\/17415977.2018.1518444"},{"key":"1674_CR38","doi-asserted-by":"crossref","unstructured":"S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, \"Photoacoustic image reconstruction via deep learning,\" 2018, vol. 10494: SPIE, pp. 433\u2013442.","DOI":"10.1117\/12.2290676"},{"key":"1674_CR39","doi-asserted-by":"publisher","unstructured":"P. Bardsley, K. Ren, and R. 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