These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction. Hossbach J; Splitthoff DN; Cauley S; Clifford B; Polak D; Lo WC; Meyer H; Maier A Med Phys; 2023 Apr; 50(4):2148-2161. PubMed ID: 36433748 [TBL] [Abstract][Full Text] [Related]
3. AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models. Beljaards L; Pezzotti N; Rao C; Doneva M; van Osch MJP; Staring M Med Phys; 2024 May; 51(5):3555-3565. PubMed ID: 38167996 [TBL] [Abstract][Full Text] [Related]
4. Unsupervised motion artifact correction of turbo spin-echo MRI using deep image prior. Lee J; Seo H; Lee W; Park H Magn Reson Med; 2024 Jul; 92(1):28-42. PubMed ID: 38282279 [TBL] [Abstract][Full Text] [Related]
5. Correction of out-of-FOV motion artifacts using convolutional neural network. Wang C; Liang Y; Wu Y; Zhao S; Du YP Magn Reson Imaging; 2020 Sep; 71():93-102. PubMed ID: 32464243 [TBL] [Abstract][Full Text] [Related]
6. Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network. Sommer K; Saalbach A; Brosch T; Hall C; Cross NM; Andre JB AJNR Am J Neuroradiol; 2020 Mar; 41(3):416-423. PubMed ID: 32054615 [TBL] [Abstract][Full Text] [Related]
7. Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions. Duffy BA; Zhao L; Sepehrband F; Min J; Wang DJ; Shi Y; Toga AW; Kim H; Neuroimage; 2021 Apr; 230():117756. PubMed ID: 33460797 [TBL] [Abstract][Full Text] [Related]
8. Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Johnson PM; Drangova M Magn Reson Med; 2019 Sep; 82(3):901-910. PubMed ID: 31006909 [TBL] [Abstract][Full Text] [Related]
9. Learning-based motion artifact removal networks for quantitative Xu X; Kothapalli SVVN; Liu J; Kahali S; Gan W; Yablonskiy DA; Kamilov US Magn Reson Med; 2022 Jul; 88(1):106-119. PubMed ID: 35257400 [TBL] [Abstract][Full Text] [Related]
10. Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning. Ghodrati V; Bydder M; Ali F; Gao C; Prosper A; Nguyen KL; Hu P NMR Biomed; 2021 Feb; 34(2):e4433. PubMed ID: 33258197 [TBL] [Abstract][Full Text] [Related]
11. Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging. Fantini I; Yasuda C; Bento M; Rittner L; Cendes F; Lotufo R Comput Med Imaging Graph; 2021 Jun; 90():101897. PubMed ID: 33770561 [TBL] [Abstract][Full Text] [Related]
12. MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks. Zhang Q; Ruan G; Yang W; Liu Y; Zhao K; Feng Q; Chen W; Wu EX; Feng Y Magn Reson Med; 2019 Dec; 82(6):2133-2145. PubMed ID: 31373061 [TBL] [Abstract][Full Text] [Related]
13. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. Nomura Y; Xu Q; Shirato H; Shimizu S; Xing L Med Phys; 2019 Jul; 46(7):3142-3155. PubMed ID: 31077390 [TBL] [Abstract][Full Text] [Related]
14. Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks. Amirian M; Montoya-Zegarra JA; Herzig I; Eggenberger Hotz P; Lichtensteiger L; Morf M; Züst A; Paysan P; Peterlik I; Scheib S; Füchslin RM; Stadelmann T; Schilling FP Med Phys; 2023 Oct; 50(10):6228-6242. PubMed ID: 36995003 [TBL] [Abstract][Full Text] [Related]
15. Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics. Vakli P; Weiss B; Szalma J; Barsi P; Gyuricza I; Kemenczky P; Somogyi E; Nárai Á; Gál V; Hermann P; Vidnyánszky Z Med Image Anal; 2023 Aug; 88():102850. PubMed ID: 37263108 [TBL] [Abstract][Full Text] [Related]
16. TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization. Haskell MW; Cauley SF; Wald LL IEEE Trans Med Imaging; 2018 May; 37(5):1253-1265. PubMed ID: 29727288 [TBL] [Abstract][Full Text] [Related]
17. Stop moving: MR motion correction as an opportunity for artificial intelligence. Zhou Z; Hu P; Qi H MAGMA; 2024 Jul; 37(3):397-409. PubMed ID: 38386151 [TBL] [Abstract][Full Text] [Related]
18. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis. Cui L; Song Y; Wang Y; Wang R; Wu D; Xie H; Li J; Yang G PLoS One; 2023; 18(1):e0278668. PubMed ID: 36603007 [TBL] [Abstract][Full Text] [Related]
19. Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network. Kromrey ML; Tamada D; Johno H; Funayama S; Nagata N; Ichikawa S; Kühn JP; Onishi H; Motosugi U Eur Radiol; 2020 Nov; 30(11):5923-5932. PubMed ID: 32556463 [TBL] [Abstract][Full Text] [Related]
20. Reconstruction of Compressed-sensing MR Imaging Using Deep Residual Learning in the Image Domain. Ouchi S; Ito S Magn Reson Med Sci; 2021 Jun; 20(2):190-203. PubMed ID: 32611937 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]