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Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI).
C. Shannon, “Communities in the presence of noise,” Proc. IRE , vol. 37, no. 1, pp. 10–21, 1949
1949
Earlier work this paper cites.
B. Moraal et al. , “Multi-contrast, isotropic, single-slab 3d mr imaging in multiple sclerosis,” Neuroradiology J. , vol. 22, pp. 33–42, 2009
2009
Earlier work this paper cites.
S. Roy, A. Carass, and J. Prince, “A compressed sensing approach for mr tissue contrast synthesis,” in Inf. Process. Med. Imag. , 2011, pp. 371–383
2011
Earlier work this paper cites.
B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ants similarity metric performance in brain image registration,” Neuroimage , vol. 54, no. 3, pp. 2033–2044, 2011
2011
Earlier work this paper cites.
S. Roy, A. Carass, and J. L. Prince, “Magnetic resonance image example-based contrast synthesis,” IEEE Trans. Med. Imag. , vol. 32, no. 12, pp. 2348–2363, 2013
2013
Earlier work this paper cites.
R. Li et al. , “Deep learning based imaging data completion for improved brain disease diagnosis,” in Med. Image Comput. Comput. Assist. Interv. , 2014, pp. 305–312
2014
Earlier work this paper cites.
B. H. Menze et al. , “The multimodal brain tumor image segmentation benchmark (brats),” IEEE Trans. Med. Imag. , vol. 34, no. 10, pp. 1993–2024, 2015
2015
Earlier work this paper cites.
S. Tourbier, X. Bresson, P. Hagmann, J.-P. Thiran, R. Meuli, and M. B. Cuadra, “An efficient total variation algorithm for super-resolution in fetal brain mri with adaptive regularization,” NeuroImage , vol. 118, pp. 584–597, 2015
2015
Earlier work this paper cites.
F. Shi, J. Cheng, L. Wang, P.-T. Yap, and D. Shen, “Lrtv: Mr image super-resolution with low-rank and total variation regularizations,” IEEE Trans. Med. Imag. , vol. 34, no. 12, pp. 2459–2466, 2015
2015
Earlier work this paper cites.
Y. Huang, L. Shao, and A. F. Frangi, “Simultaneous super-resolution and cross-modality synthesis of 3d medical images using weakly-supervised joint convolutional sparse coding,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 6070–6079
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 5967–5976
2017
Earlier work this paper cites.
A. S. Chaudhari et al. , “Super-resolution musculoskeletal mri using deep learning,” Magn. Reson. Med. , vol. 80, no. 5, pp. 2139–2154, 2018
2018
Earlier work this paper cites.
A. Chartsias, T. Joyce, M. V. Giuffrida, and S. A. Tsaftaris, “Multimodal mr synthesis via modality-invariant latent representation,” IEEE Trans. Med. Imag. , vol. 37, no. 3, pp. 803–814, 2018
2018
Earlier work this paper cites.
B. Yu, L. Zhou, L. Wang, J. Fripp, and P. Bourgeat, “3d cgan based cross-modality mr image synthesis for brain tumor segmentation,” in Proc. IEEE 15th Int. Symp. Biomed. Imag. , 2018, pp. 626–630
2018
Earlier work this paper cites.
Y. Chen, Y. Xie, Z. Zhou, F. Shi, A. G. Christodoulou, and D. Li, “Brain mri super resolution using 3d deep densely connected neural networks,” in Proc. IEEE 15th Int. Symp. Biomed. Imag. , 2018, pp. 739–742
2018
Earlier work this paper cites.
Y. Chen, F. Shi, A. G. Christodoulou, Y. Xie, Z. Zhou, and D. Li, “Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi-level densely connected network,” in Med. Image Comput. Comput. Assist. Interv , 2018, pp. 91–99
2018
Earlier work this paper cites.
T. Miyato and M. Koyama, “cGANs with projection discriminator,” in Int. Conf. Learn. Represent. , 2018
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of GANs for improved quality, stability, and variation,” in Int. Conf. Learn. Represent. , 2018
2018
Cited alongside, same era.
M. K. Abd-Ellah, A. I. Awad, A. A. Khalaf, and H. F. Hamed, “A review on brain tumor diagnosis from mri images: Practical implications, key achievements, and lessons learned,” Magn. Reson. Imag. , vol. 61, pp. 300–318, 2019
2019
Cited alongside, same era.
S. U. Dar, M. Yurt, L. Karacan, A. Erdem, E. Erdem, and T. Cukur, “Image synthesis in multi-contrast mri with conditional generative adversarial networks,” IEEE Trans. Med. Imag. , vol. 38, no. 10, pp. 2375–2388, 2019
2019
Cited alongside, same era.
X. Hu, H. Mu, X. Zhang, Z. Wang, T. Tan, and J. Sun, “Meta-sr: A magnification-arbitrary network for super-resolution,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 1575–1584
2019
Cited alongside, same era.
Y. Pang, J. Lin, T. Qin, and Z. Chen, “Image-to-image translation: Methods and applications,” IEEE Trans. on Multimedia , vol. 24, pp. 3859–3881, 2022
2022
Later among the works it cites.
A. Rogozhnikov, “Einops: Clear and reliable tensor manipulations with einstein-like notation,” in Int. Conf. Learn. Represent. , 2022
2022
Later among the works it cites.
“Adni dataset,” https://adni.loni.usc.edu/ . Accessed: 2022-11-21
2022
Later among the works it cites.
“Ixi dataset,” https://brain-development.org/ixi-dataset/ . Accessed: 2022-11-21
2022
Later among the works it cites.
M. Özbey et al. , “Unsupervised medical image translation with adversarial diffusion models,” IEEE Trans. Med. Imag. , pp. 1–1, 2023
2023
Closest in time.
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T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 8110–8119
2020
Cited alongside, same era.
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, “Training generative adversarial networks with limited data,” Adv. Neural Inf. Process. Syst. , vol. 33, pp. 12 104–12 114, 2020
2020
Cited alongside, same era.
B. Yan, X. Niu, B. Bare, and W. Tan, “Semantic segmentation guided pixel fusion for image retargeting,” IEEE Trans. Multimedia , vol. 22, no. 3, pp. 676–687, 2020
2020
Cited alongside, same era.
J. E. Iglesias et al. , “Joint super-resolution and synthesis of 1 mm isotropic mp-rage volumes from clinical mri exams with scans of different orientation, resolution and contrast,” Neuroimage , vol. 237, p. 118206, 2021
2021
Cited alongside, same era.
H. Lan, A. D. N. Initiative, A. W. Toga, and F. Sepehrband, “Three-dimensional self-attention conditional gan with spectral normalization for multimodal neuroimaging synthesis,” Magn. Reson. Med. , vol. 86, no. 3, pp. 1718–1733, 2021
2021
Cited alongside, same era.
C. Zhao et al. , “Smore: A self-supervised anti-aliasing and super-resolution algorithm for mri using deep learning,” IEEE Trans. Med. Imag. , vol. 40, no. 3, pp. 805–817, 2021
2021
Cited alongside, same era.
T. Karras et al. , “Alias-free generative adversarial networks,” Adv. Neural Inf. Process. Syst. , vol. 34, pp. 852–863, 2021
2021
Cited alongside, same era.
S. Zhao et al. , “Large scale image completion via co-modulated generative adversarial networks,” in Int. Conf. Learn. Represent. , 2021
2021
Cited alongside, same era.
Q. Wu et al. , “An arbitrary scale super-resolution approach for 3d mr images via implicit neural representation,” IEEE J. Biomed. Health Informat. , vol. 27, no. 2, pp. 1004–1015, 2023
2023
Closest in time.
2023
Closest in time.
S. Zhong et al. , “Understanding aliasing effects and their removal in spen mri: A k-space perspective,” Magn. Reson. Med. , vol. 90, no. 1, pp. 166–176, 2023
2023
Closest in time.
A. Kirillov et al. , “Segment anything,” arXiv preprint arXiv:2304.02643 , 2023
2023
Closest in time.
B. Cao, H. Cao, J. Liu, P. Zhu, C. Zhang, and Q. Hu, “Autoencoder-based collaborative attention gan for multi-modal image synthesis,” IEEE Trans. Multimedia , pp. 1–16, 2023
2023
Closest in time.
Z. Song et al. , “Nucleus-aware self-supervised pretraining using unpaired image-to-image translation for histopathology images,” IEEE Trans. Med. Imag. , pp. 1–1, 2023
2023
Closest in time.
Y. Zhang, Y. Liu, R. Hu, Q. Wu, and J. Zhang, “Mutual dual-task generator with adaptive attention fusion for image inpainting,” IEEE Trans Multimedia , pp. 1–13, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
T. Kalluri, D. Pathak, M. Chandraker, and D. Tran, “Flavr: Flow-agnostic video representations for fast frame interpolation,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. , 2023, pp. 2071–2082
2082
Closest in time.