{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:41:13Z","timestamp":1742996473074,"version":"3.40.3"},"publisher-location":"Wiesbaden","reference-count":10,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"type":"print","value":"9783658416560"},{"type":"electronic","value":"9783658416577"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-658-41657-7_66","type":"book-chapter","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T07:02:48Z","timestamp":1685602968000},"page":"306-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-modality Training Approach for CT Super-resolution Network"],"prefix":"10.1007","author":[{"given":"Wai Yan Ryana","family":"Fok","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Fieselmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magdalena","family":"Herbst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ludwig","family":"Ritschl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Beister","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Kappler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvia","family":"Saalfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"66_CR1","doi-asserted-by":"crossref","unstructured":"Barbosa Jr EJM, Gefter WB, Ghesu FC, Liu S, Mailhe B, Mansoor A et al. Automated detection and quantification of COVID-19 airspace disease on chest radiographs: a novel approach achieving expert radiologist-level performance using a deep convolutional neural network trained on digital reconstructed radiographs from computed tomography-derived ground truth. Invest Radiol. 2021;56(8):471\u20139.","DOI":"10.1097\/RLI.0000000000000763"},{"key":"66_CR2","doi-asserted-by":"crossref","unstructured":"Umehara K, Ota J, Ishida T. Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging. 2018;31(4):441\u201350.","DOI":"10.1007\/s10278-017-0033-z"},{"key":"66_CR3","doi-asserted-by":"crossref","unstructured":"Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography superresolution using deep convolutional neural network. Phys Med Biol. 2018;63(14):145011.","DOI":"10.1088\/1361-6560\/aacdd4"},{"key":"66_CR4","doi-asserted-by":"crossref","unstructured":"Yu H, Liu D, Shi H, Yu H, Wang Z, Wang X et al. Computed tomography super-resolution using convolutional neural networks. Conf Proc IEEE Int Conf Signal Image Process Appl. IEEE. 2017:3944\u20138.","DOI":"10.1109\/ICIP.2017.8297022"},{"key":"66_CR5","doi-asserted-by":"crossref","unstructured":"Parker JA, Kenyon RV, Troxel DE. Comparison of interpolating methods for image resampling. IEEE Trans Med Imaging. 1983;2(1):31\u20139.","DOI":"10.1109\/TMI.1983.4307610"},{"key":"66_CR6","doi-asserted-by":"crossref","unstructured":"Hirahara D, Takaya E, Kadowaki M, Kobayashi Y, Ueda T. Effect of the pixel interpolation method for downsampling medical images on deep learning accuracy. J. comput. commun. 2021;9(11):150\u20136.","DOI":"10.4236\/jcc.2021.911010"},{"key":"66_CR7","doi-asserted-by":"crossref","unstructured":"Grunz JP, Weng AM, Gietzen CH, Veyhl-Wichmann M, Pennig L, Kunz A et al. Evaluation of ultra-high-resolution cone-beam CT prototype of twin robotic radiography system for cadaveric wrist imaging. Acad Radiol. 2021;28(10):e314\u2013e322.","DOI":"10.1016\/j.acra.2020.06.018"},{"key":"66_CR8","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Med Image Comput Comput Assist Interv. Springer. 2015:234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"66_CR9","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G et al. PyTorch: an imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32. Ed. byWallach H, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9-Buc F, Fox E, Garnett R. Curran Associates, Inc., 2019:8024\u201335."},{"key":"66_CR10","doi-asserted-by":"crossref","unstructured":"Peng C, Zhou SK, Chellappa R. DA-VSR: domain adaptable volumetric super-resolution for medical images. Med Image Comput Comput Assist Interv. Springer. 2021:75\u201385.","DOI":"10.1007\/978-3-030-87231-1_8"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2023"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-41657-7_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T10:07:39Z","timestamp":1707127659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-41657-7_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783658416560","9783658416577"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-41657-7_66","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"type":"print","value":"1431-472X"},{"type":"electronic","value":"2628-8958"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"2 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Braunschweig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}