1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2016. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
2. MoDL: Model-based deep learning architecture for inverse problems;Aggarwal;IEEE Trans. Med. Imaging,2019
3. Advances in signal processing for relaxometry;Ben-Eliezer,2020
4. Model-based iterative reconstruction for radial fast spin-echo MRI;Block;IEEE Trans. Med. Imaging,2009
5. What are normal relaxation times of tissues at 3T?;Bojorquez;Magn. Reson. Imaging,2017