Acknowledgement
This work was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT)(No. 2022R1A2B5B01002517).
References
- Tomas X, Pomes J, Berenguer J, Quinto L, Nicolau C, Mercader JM, et al. MR imaging of temporomandibular joint dysfunction: a pictorial review. Radiographics 2006; 26: 765-81. https://doi.org/10.1148/rg.263055091
- Navallas M, Inarejos EJ, Iglesias E, Cho Lee GY, Rodriguez N, Anton J. MR imaging of the temporomandibular joint in juvenile idiopathic arthritis: technique and findings. Radiographics 2017; 37: 595-612. https://doi.org/10.1148/rg.2017160078
- Tokuda O, Harada Y, Shiraishi G, Motomura T, Fukuda K, Kimura M, et al. MRI of the anatomical structures of the knee: the proton density-weighted fast spin-echo sequence vs the proton density-weighted fast-recovery fast spin-echo sequence. Br J Radiol 2012; 85: e686-93. https://doi.org/10.1259/bjr/99570113
- Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182-95. https://doi.org/10.1002/mrm.21391
- Hollingsworth KG. Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Phys Med Biol 2015; 60: R297-322. https://doi.org/10.1088/0031-9155/60/21/R297
- Shin Y, Yang J, Lee YH. Deep generative adversarial networks: applications in musculoskeletal imaging. Radiol Artif Intell 2021; 3: e200157.
- Hwang JJ, Jung YH, Cho BH, Heo MS. Very deep super-resolution for efficient cone-beam computed tomographic image restoration. Imaging Sci Dent 2020; 50: 331-7. https://doi.org/10.5624/isd.2020.50.4.331
- Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, et al. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans Med Imaging 2018; 37: 1310-21.
- Mori M, Fujioka T, Katsuta L, Kikuchi Y, Oda G, Nakagawa T, et al. Feasibility of new fat suppression for breast MRI using pix2pix. Jpn J Radiol 2020; 38: 1075-81. https://doi.org/10.1007/s11604-020-01012-5
- Gao F, Xu X, Yu J, Shang M, Li X, Tao D. Complementary, heterogeneous and adversarial networks for image-to-image translation. IEEE Trans Image Process 2021; 30: 3487-98. https://doi.org/10.1109/TIP.2021.3061286
- Xia Y, Zhang L, Ravikumar N, Attar R, Piechnik SK, Neubauer S, et al. Recovering from missing data in population imaging - cardiac MR image imputation via conditional generative adversarial nets. Med Image Anal 2021; 67: 101812.
- Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004; 13: 600-12. https://doi.org/10.1109/TIP.2003.819861
- Mason A, Rioux J, Clarke SE, Costa A, Schmidt M, Keough V, et al. Comparison of objective image quality metrics to expert radiologists' scoring of diagnostic quality of MR images. IEEE Trans Med Imaging 2020; 39: 1064-72. https://doi.org/10.1109/TMI.2019.2930338
- Boulanger M, Nunes JC, Chourak H, Largent A, Tahri S, Acosta O, et al. Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med 2021; 89: 265-81. https://doi.org/10.1016/j.ejmp.2021.07.027
- Park JW, Song HH, Roh HS, Kim YK, Lee JY. Correlation between clinical diagnosis based on RDC/TMD and MRI findings of TMJ internal derangement. Int J Oral Maxillofac Surg 2012; 41: 103-8. https://doi.org/10.1016/j.ijom.2011.09.010
- Goncalves FG, Serai SD, Zuccoli G. Synthetic brain MRI: review of current concepts and future directions. Top Magn Reson Imaging 2018; 27: 387-93. https://doi.org/10.1097/RMR.0000000000000189
- Tanenbaum LN, Tsiouris AJ, Johnson AN, Naidich TP, DeLano MC, Melhem ER, et al. Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. AJNR Am J Neuroradiol 2017; 38: 1103-10. https://doi.org/10.3174/ajnr.A5227
- Lee SM, Choi YH, Cheon JE, Kim IO, Cho SH, Kim WH, et al. Image quality at synthetic brain magnetic resonance imaging in children. Pediatr Radiol 2017; 47: 1638-47. https://doi.org/10.1007/s00247-017-3913-y
- Lee C, Choi YJ, Jeon KJ, Han SS. Synthetic magnetic resonance imaging for quantitative parameter evaluation of temporomandibular joint disorders. Dentomaxillofac Radiol 2021; 50: 20200584. https://doi.org/10.1259/dmfr.20219002
- Benzakoun J, Deslys MA, Legrand L, Hmeydia G, Turc G, Hassen WB, et al. Synthetic FLAIR as a substitute for FLAIR sequence in acute ischemic stroke. Radiology 2022; 303: 153-9. https://doi.org/10.1148/radiol.211394
- Kim KH, Do WJ, Park SH. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Med Phys 2018; 45: 3120-31. https://doi.org/10.1002/mp.12945
- Yu B, Zhou L, Wang L, Shi Y, Fripp J, Bourgeat P. Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Trans Med Imaging 2019; 38: 1750-62. https://doi.org/10.1109/tmi.2019.2895894
- Kim S, Jang H, Hong S, Hong YS, Bae WC, Kim S, et al. Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization. Med Image Anal 2021; 73: 102198.