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Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.
Yasaka K, Uehara S, Kato S, Watanabe Y, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S. Yasaka K, et al. Among authors: ohtomo k. J Imaging Inform Med. 2024 Apr 26. doi: 10.1007/s10278-024-01112-y. Online ahead of print. J Imaging Inform Med. 2024. PMID: 38671337
Clinical Impact of Deep Learning Reconstruction in MRI.
Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, Akahane M, Abe O, Ohtomo K. Kiryu S, et al. Among authors: ohtomo k. Radiographics. 2023 Jun;43(6):e220133. doi: 10.1148/rg.220133. Radiographics. 2023. PMID: 37200221
Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.
Akai H, Yasaka K, Sugawara H, Tajima T, Akahane M, Yoshioka N, Ohtomo K, Abe O, Kiryu S. Akai H, et al. Among authors: ohtomo k. Magn Reson Med Sci. 2023 Jul 1;22(3):353-360. doi: 10.2463/mrms.mp.2022-0020. Epub 2022 Jul 9. Magn Reson Med Sci. 2023. PMID: 35811127 Free PMC article.
798 results