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1. Lee S, Jung S, Jung KJ, Kim DH. Deep learning in MR motion correction: a brief review and a new motion simulation tool (view2Dmotion). Investigative Magnetic Resonance Imaging. 2020 Dec 1;24(4):196-206. doi:10.13104/imri.2020.24.4.196. [doi]
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6. Ding Y, Tian C, Ding H, Liu L. The CLIP Model is Secretly an Image-to-Prompt Converter. arXiv preprint arXiv:2305.12716. 2023 May 22. https://doi.org/10.48550/arXiv.2305.12716 [doi]
7. Kim K, Na Y, Ye SJ, Lee J, Ahn SS, Park JE, Kim H. Controllable text-to-image synthesis for multi-modality MR images. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2024 (pp. 7936-7945). doi: 10.1109/WACV57701.2024.00775. [doi]
8. Xing Z, Wan L, Fu H, Yang G, Zhu L. Diff-unet: A diffusion embedded network for volumetric segmentation. arXiv preprint arXiv:2303.10326. 2023 Mar 18. doi: 10.48550/arXiv.2303.10326. [doi]
9. Zhang C, Chen Y, Fan Z, Huang Y, Weng W, Ge R, Zeng D, Wang C. Tc-diffrecon: Texture coordination mri reconstruction method based on diffusion model and modified mf-unet method. In2024 IEEE International Symposium on Biomedical Imaging (ISBI) 2024 May 27 (pp. 1-5). IEEE. doi: 10.48550/arXiv.2402.11274. [doi]
10. Melanie Ganz and Hannah Eichhorn (2024). Datasets with and without deliberate head movements for evaluating the performance of markerless prospective motion correction and selective reacquisition in a general clinical protocol for brain MRI. OpenNeuro. [Dataset] doi: doi:10.18112/openneuro.ds004332.v1.3.0 [doi]
11. Eichhorn H, Frost R, Kongsgaard A, Kettless K, Slipsager J, Glimberg S, Shekhrajka N, Tisdall MD, Wighton P, Borgwardt L, Born AP. Evaluating the performance of markerless prospective motion correction and selective reacquisition in a general clinical protocol for brain MRI. doi: 10.31234/osf.io/vzh4g. [doi]