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Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc.
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Desai, A.D., Schmidt, A.M., Rubin, E.B., Sandino, C.M., Black, M.S., Mazzoli, V., et al.: Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021)
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Liu, X., Wang, J., Liu, F., Zhou, S.K.: Universal undersampled mri reconstruction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 211–221. Springer (2021)
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2022
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Korkmaz, Y., Dar, S.U., Yurt, M., Özbey, M., Cukur, T.: Unsupervised mri reconstruction via zero-shot learned adversarial transformers. IEEE Transactions on Medical Imaging (2022)
2022
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