{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T18:55:06Z","timestamp":1768071306479,"version":"3.49.0"},"reference-count":6,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003185","name":"Fraunhofer-Gesellschaft","doi-asserted-by":"publisher","award":["600 725"],"award-info":[{"award-number":["600 725"]}],"id":[{"id":"10.13039\/501100003185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Purpose<\/jats:title>\n<jats:p>We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic reconstruction to form, as compared to common reconstructions constrained by physical models using fixed speed of sound assumptions.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>We propose a U-Net-like architecture for the given task. Additional layers with strided convolutions downsample the raw data. Hyperparameter optimization was used to find a suitable learning rate. We train and test our deep learning approach on plane wave ultrasound images with a single insonification angle. The dataset includes phantom as well as in vivo data.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>The images produced by our method are visually comparable to ones reconstructed with the conventional delay and sum algorithm. Deviations between prediction and ground truth are likely to be related to speckle noise. For the test set, the mean absolute error is <jats:inline-formula><jats:alternatives><jats:tex-math>$$4.23 \\pm 1.52$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mrow>\n<mml:mn>4.23<\/mml:mn>\n<mml:mo>\u00b1<\/mml:mo>\n<mml:mn>1.52<\/mml:mn>\n<\/mml:mrow>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the phantom images and <jats:inline-formula><jats:alternatives><jats:tex-math>$$6.09 \\pm 0.72$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mrow>\n<mml:mn>6.09<\/mml:mn>\n<mml:mo>\u00b1<\/mml:mo>\n<mml:mn>0.72<\/mml:mn>\n<\/mml:mrow>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the in vivo data.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The result shows the feasibility of our approach and opens up new research directions regarding information retrieval from raw channel data. As the networks reconstruction performance is limited by the quality of the ground truth images, using other ultrasound reconstruction technique or image types as target data would be of interest.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s11548-020-02197-w","type":"journal-article","created":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T15:29:09Z","timestamp":1591198149000},"page":"1487-1490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep learning-based reconstruction of ultrasound images from raw channel data"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-484X","authenticated-orcid":false,"given":"Hannah","family":"Strohm","sequence":"first","affiliation":[]},{"given":"Sven","family":"Rothl\u00fcbbers","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"Eickel","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"G\u00fcnther","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"2197_CR1","unstructured":"Simson W, G\u00f6bl R, Paschali M, Kr\u00f6nke M, Scheidhauer K, Weber W, Navab N (2019) End-to-end learning-based ultrasound reconstruction. arXiv e-prints arXiv:1904.04696,"},{"issue":"10","key":"2197_CR2","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/TUFFC.2017.2736890","volume":"64","author":"M Gasse","year":"2017","unstructured":"Gasse M, Millioz F, Roux E, Garcia D, Liebgott H, Friboulet D (2017) High-quality plane wave compounding using convolutional neural networks. IEEE Trans Ultrasonics Ferroelectr Freq Control 64(10):1637\u20131639. https:\/\/doi.org\/10.1109\/TUFFC.2017.2736890","journal-title":"IEEE Trans Ultrasonics Ferroelectr Freq Control"},{"key":"2197_CR3","doi-asserted-by":"crossref","unstructured":"Nair AA, Tran TD, Reiter A, Bell MAL (2018) A deep learning based alternative to beamforming ultrasound images. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 3359\u20133363","DOI":"10.1109\/ICASSP.2018.8461575"},{"key":"2197_CR4","doi-asserted-by":"crossref","unstructured":"Nair AA, Tran TD, Reiter A, Bell MAL (2019) A generative adversarial neural network for beamforming ultrasound images: invited presentation. In: 2019 53rd Annual conference on information sciences and systems (CISS), pp 1\u20136. https:\/\/doi.org\/10.1109\/CISS.2019.8692835","DOI":"10.1109\/CISS.2019.8692835"},{"key":"2197_CR5","unstructured":"Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th international conference on machine learning, proceedings of machine learning research, vol\u00a080, pp 1437\u20131446"},{"key":"2197_CR6","doi-asserted-by":"crossref","unstructured":"Liebgott H, Rodriguez-Molares A, Cervenansky F, Jensen JA, Bernard O (2016) Plane-wave imaging challenge in medical ultrasound. In: 2016 IEEE International ultrasonics symposium (IUS), pp 1\u20134. https:\/\/doi.org\/10.1109\/ULTSYM.2016.7728908","DOI":"10.1109\/ULTSYM.2016.7728908"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02197-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02197-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02197-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T23:26:23Z","timestamp":1622676383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02197-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,3]]},"references-count":6,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["2197"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02197-w","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,3]]},"assertion":[{"value":"14 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}