Cite this abstract
http://echo.ismrm.org/p/ISMRM2024/3570
3570
ISMRM Abstract
DAES: Self-Supervised Parameter Estimation Model for MR Fingerprinting
Accepted
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here.
This content is freely available and no longer restricted to registered attendees. Log in or create a free account to view it.
Log inReferences
1. Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A. (2013). Magnetic resonance fingerprinting. Nature, 495(7440), 187–192.
2. Haldar JP, Anderson J, Sun SW. Maximum likelihood estimation of T1 relaxation parameters using VARPRO. In: Proceedings of the 15th Annual Meeting of ISMRM, Berlin, Germany. ; 2007. p.
3. Fang, C., Yang, Z., Wassermann, D., & Li, J.-R. (2023). A simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. Medical Image Analysis, 90, 102979.
4. Nykänen, O., Nevalainen, M., Casula, V., Isosalo, A., Inkinen, S. I., Nikki, M., Lattanzi, R., Cloos, M. A., Nissi, M. J., & Nieminen, M. T. (2023). Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint. Journal of Magnetic Resonance Imaging: JMRI, 58(2), 559–568.
5. Dar, S. U. H., Öztürk, Ş., Özbey, M., Oguz, K. K., & Çukur, T. (2023). Parallel-stream fusion of scan-specific and scan-general priors for learning deep MRI reconstruction in low-data regimes. Computers in Biology and Medicine, 167, 107610.
6. Li, Y., Joaquim, M. R., Pickup, S., Song, H. K., Zhou, R., & Fan, Y. (2023). Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.29833.
7. Zhao, Z., Nie, C., Zhao, L., Xiao, D., Zheng, J., Zhang, H., Yan, P., Jiang, X., & Zhao, H. (2023). Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas. European Radiology. https://doi.org/10.1007/s00330-023-10252-8.
8. Kang, B., Kim, B., Schär, M., Park, H., & Heo, H.-Y. (2021). Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 85(4), 2040–2054.
9. Gao, M., Ye, H., Kim, T. H., Zhang, Z., So, S., & Bilgic, B. (2022). Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting. arXiv:2112.03815. https://doi.org/10.48550/arXiv.2112.03815.
10. Jiang, Y., Ma, D., Seiberlich, N., Gulani, V., & Griswold, M. A. (2015). MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 74(6), 1621–1631.
11. Cao, X., Liao, C., Wang, Z., Chen, Y., Ye, H., He, H., & Zhong, J. (2017). Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 78(4), 1579–1588.
12. Hope, T., Resheff, Y. S., & Lieder, I. (2017). Learning TensorFlow: A Guide to Building Deep Learning Systems. “O’Reilly Media, Inc.” 13. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980.