{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T03:16:03Z","timestamp":1776914163697,"version":"3.51.2"},"reference-count":9,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Technische Universit\u00e4t Hamburg","award":["internal id: T-LP-E01-WTM-1801-02"],"award-info":[{"award-number":["internal id: T-LP-E01-WTM-1801-02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz. The resulting sequence of phase image volumes is fed into a 4D convolutional neural network which handles both spatial and temporal data processing. We evaluate the approach on a set of image data acquired for gelatin phantoms of known elasticity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Using the neural network, the gelatin concentration of unseen samples was predicted with a mean error of 0.65 \u00b1 0.81 percentage points from 90 subsequent volumes of phase data only. We achieve a data acquisition and data processing time of under 12 ms and 22 ms, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We demonstrate direct volumetric optical coherence elastography from phase image data. The approach does not rely on particular stimulation or sampling sequences and allows the estimation of elastic tissue properties of up to 40 Hz.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-020-02261-5","type":"journal-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T07:03:56Z","timestamp":1601449436000},"page":"23-27","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["4D deep learning for real-time volumetric optical coherence elastography"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-0864","authenticated-orcid":false,"given":"M.","family":"Neidhardt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Bengs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Latus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Schl\u00fcter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Saathoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Schlaefer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"issue":"06","key":"2261_CR1","first-page":"584","volume":"37","author":"D Chauvet","year":"2016","unstructured":"Chauvet D, Imbault M, Capelle L, Demene C, Mossad M, Karachi C, Boch AL, Gennisson JL, Tanter M (2016) In vivo measurement of brain tumor elasticity usingintraoperative shear wave elastography. Ultraschall in der Medizin-Eur J Ultrasound 37(06):584\u2013590","journal-title":"Ultraschall in der Medizin-Eur J Ultrasound"},{"issue":"1","key":"2261_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.4329\/wjr.v8.i1.59","volume":"8","author":"G Low","year":"2016","unstructured":"Low G, Kruse SA, Lomas DJ (2016) General review of magnetic resonance elastography. World J Radiol 8(1):59","journal-title":"World J Radiol"},{"key":"2261_CR3","first-page":"655","volume-title":"An approach for needle based optical coherence elastography measurements. MICCAI","author":"S Latus","year":"2017","unstructured":"Latus S, Otte C, Schl\u00fcter M, Rehra J, Schulz-Hildebrandt H, Saathoff T, H\u00fcuttmann G, Schlaefer A (2017) An approach for needle based optical coherence elastography measurements. MICCAI. Springer, Berlin, pp 655\u2013663"},{"issue":"1","key":"2261_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-12803-4","volume":"10","author":"F Zvietcovich","year":"2019","unstructured":"Zvietcovich F, Pongchalee P, Meemon P, Rolland JP, Parker KJ (2019) Reverberant 3d optical coherence elastography maps the elasticity of individual corneal layers. Nat Commun 10(1):1\u201313","journal-title":"Nat Commun"},{"issue":"6","key":"2261_CR5","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1515\/bmt-2014-0028","volume":"59","author":"A Karimi","year":"2014","unstructured":"Karimi A, Navidbakhsh M (2014) Material properties in unconfined compression of gelatin hydrogel for skin tissue engineering applications. Biomed Eng 59(6):479\u2013486","journal-title":"Biomed Eng"},{"issue":"5","key":"2261_CR6","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1007\/s00330-016-4534-9","volume":"27","author":"O Rouvi\u00e8re","year":"2017","unstructured":"Rouvi\u00e8re O, Melodelima C, Dinh AH, Bratan F, Pagnoux G, Sanzalone T, Crouzet S, Colombel M, M\u00e8ge-Lechevallier F, Souchon R (2017) Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study. Eur Radiol 27(5):1858\u20131866","journal-title":"Eur Radiol"},{"key":"2261_CR7","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE CVPR, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2261_CR8","doi-asserted-by":"crossref","unstructured":"Neidhardt M, Bengs M, Latus S, Schl\u00fcter M, Saathoff T, Schlaefer A (2020) Deep learning for high speed optical coherence elastography. In: IEEE 17th ISBI, pp 1583\u20131586","DOI":"10.1109\/ISBI45749.2020.9098422"},{"key":"2261_CR9","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02261-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02261-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02261-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T01:29:08Z","timestamp":1632965348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02261-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,30]]},"references-count":9,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["2261"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02261-5","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,30]]},"assertion":[{"value":"13 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 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":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This articles does not contain patient data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}