{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T06:23:54Z","timestamp":1769235834554,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031830105","type":"print"},{"value":"9783031830082","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The Swoop system of Hyperfine Inc. is an affordable, ultra-low-field MRI developed for use in a clinical setting. However, despite its advantages, the relatively low resolution of 64mT MRI data poses additional challenges, especially in examining small structures such as the hippocampus or vessels. As a part of our attempt at the Low field pediatric brain magnetic resonance Image Segmentation and Quality Assurance (LISA) Challenge 2024, we developed two deep learning-based models. First, to evaluate the image quality of 64mT T2 brain MRI data, we implemented an axis classifier module to improve the model performance. Second, for segmentation of the hippocampus in the MRI, a multi-label learning method was used for more accurate segmentation. With these models, we expect to alleviate the accessibility barrier to brain MRI.<\/jats:p>","DOI":"10.1007\/978-3-031-83008-2_5","type":"book-chapter","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T21:06:24Z","timestamp":1740344784000},"page":"53-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Axis-Guided Quality Assessment and Multi-label Hippocampal and Ventricular Segmentation in Low-Resolution Pediatric Brain MRI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9905-6388","authenticated-orcid":false,"given":"Hyunwook","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0731-3826","authenticated-orcid":false,"given":"Jinew","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6285-7303","authenticated-orcid":false,"given":"Seiyoung","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4366-1688","authenticated-orcid":false,"given":"Joon hyung","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9122-1753","authenticated-orcid":false,"given":"Sungchul","family":"On","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3615-926X","authenticated-orcid":false,"given":"Jinwha","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"van Beek, E.J.R., et al.: Value of MRI in medicine: more than just another test?\u201d J. Magn. Reson. Imaging 49(7), e14\u201325 (2019). Wiley Online Library https:\/\/doi.org\/10.1002\/jmri.26211","DOI":"10.1002\/jmri.26211"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Mazurek, M.H., et al.: Portable, bedside, low-field magnetic resonance imaging for evaluation of intracerebral hemorrhage. Nat. Commun. 12(1), 5119 (2021). www.nature.com, https:\/\/doi.org\/10.1038\/s41467-021-25441-6","DOI":"10.1038\/s41467-021-25441-6"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Arnold, T.C., et al.: Simulated diagnostic performance of low-field MRI: harnessing open-access datasets to evaluate novel devices. Magn. Reson. Imaging 87, 67\u201376 (2022). PubMed Central, https:\/\/doi.org\/10.1016\/j.mri.2021.12.007","DOI":"10.1016\/j.mri.2021.12.007"},{"key":"5_CR4","unstructured":"Price, R., et al.: MR Quality Control Manual. American College of Radiology (2015). www.acr.org\/-\/media\/ACR\/Files\/Clinical-Resources\/QC-Manuals\/MR_QCManual.pdf. 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