Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.

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Bibliographic Details
Title: Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.
Authors: Konovalova N; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Tolpadi A; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States.; Bioengineering Department, University of California, Berkeley, Berkeley, CA, United States., Liu F; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Akkaya Z; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States.; Faculty of Medicine, Radiology Department, Ankara University, Ankara, Turkey., Luitjens J; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Gassert F; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Giesler P; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States.; Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany., Bhattacharjee R; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Han M; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Bahroos E; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Majumdar S; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States., Pedoia V; Radiology and Biomedical Imaging Department, University of California, San Francisco, San Francisco, CA, United States.
Source: Radiology advances [Radiol Adv] 2025 Apr 04; Vol. 2 (2), pp. umaf015. Date of Electronic Publication: 2025 Apr 04 (Print Publication: 2025).
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9918840888906676 Publication Model: eCollection Cited Medium: Internet ISSN: 2976-9337 (Electronic) Linking ISSN: 29769337 NLM ISO Abbreviation: Radiol Adv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2976-9337
DOI:10.1093/radadv/umaf015