References
1. Kofler A, Zimmermann FF, Papafitsoros K. Machine Learning for Quantitative MR Image Reconstruction. arXiv preprint arXiv:240219396 .
2. Byanju R, Klein S, Cristobal-Huerta A, Hernandez-Tamames JA, Poot DHJ. Time efficiency analysis for undersampled quantitative MRI acquisitions. Med Image Anal . 2022;78:102390.
3. Jun Y, Shin H, Eo T, Kim T, Hwang D. Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T 1 mapping using variable flip angle method. Med Image Anal . 2021;70:102017.
4. Wang L, Schweitzer ME, Padua A, Regatte RR. Rapid 3D ‐ T 1 mapping of cartilage with variable flip angle and parallel imaging at 3.0 T. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine . 2008;27(1):154-161.
5. Doneva M, Börnert P, Eggers H, Stehning C, Sénégas J, Mertins A. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magn Reson Med . 2010;64(4):1114-1120.
6. Huang C, Graff CG, Clarkson EW, Bilgin A, Altbach MI. T 2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magn Reson Med . 2012;67(5):1355-1366.
7. Velikina J V, Alexander AL, Samsonov A. Accelerating MR parameter mapping using sparsity ‐ promoting regularization in parametric dimension. Magn Reson Med . 2013;70(5):1263-1273.
8. Zhang T, Pauly JM, Levesque IR. Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med . 2015;73(2):655-661.
9. Zhao B, Lu W, Hitchens TK, Lam F, Ho C, Liang Z. Accelerated MR parameter mapping with low ‐ rank and sparsity constraints. Magn Reson Med . 2015;74(2):489-498.
10. Sumpf TJ, Uecker M, Boretius S, Frahm J. Model ‐ based nonlinear inverse reconstruction for T 2 mapping using highly undersampled spin ‐ echo MRI. Journal of Magnetic Resonance Imaging . 2011;34(2):420-428.
11. Sumpf TJ, Petrovic A, Uecker M, Knoll F, Frahm J. Fast T 2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function. IEEE Trans Med Imaging . 2014;33(12):2213-2222.
12. Zhao B, Lam F, Liang ZP. Model-based MR parameter mapping with sparsity constraints: parameter estimation and performance bounds. IEEE Trans Med Imaging . 2014;33(9):1832-1844.
13. Roeloffs V, Wang X, Sumpf TJ, Untenberger M, Voit D, Frahm J. Model ‐ based reconstruction for T 1 mapping using single ‐ shot inversion ‐ recovery radial FLASH. Int J Imaging Syst Technol . 2016;26(4):254-263.
14. Wang X, Roeloffs V, Klosowski J, et al. Model ‐ based T 1 mapping with sparsity constraints using single ‐ shot inversion ‐ recovery radial FLASH. Magn Reson Med . 2018;79(2):730-740.
15. Hilbert T, Sumpf TJ, Weiland E, et al. Accelerated T 2 mapping combining parallel MRI and model ‐ based reconstruction: GRAPPATINI. Journal of Magnetic Resonance Imaging . 2018;48(2):359-368.
16. Maier O, Schoormans J, Schloegl M, et al. Rapid T 1 quantification from high resolution 3D data with model ‐ based reconstruction. Magn Reson Med . 2019;81(3):2072-2089.
17. Wang X, Kohler F, Unterberg-Buchwald C, Lotz J, Frahm J, Uecker M. Model-based myocardial T 1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance. Journal of cardiovascular magnetic resonance . 2019;21(1):60.
18. Cohen O, Zhu B, Rosen MS. MR fingerprinting deep reconstruction network (DRONE). Magn Reson Med . 2018;80(3):885-894.
19. Liu F, Feng L, Kijowski R. MANTIS: Model ‐ Augmented Neural neTwork with Incoherent k ‐ space Sampling for efficient MR parameter mapping. Magn Reson Med . 2019;82(1):174-188.
20. Liu F, Kijowski R, El Fakhri G, Feng L. Magnetic resonance parameter mapping using model ‐ guided self ‐ supervised deep learning. Magn Reson Med . 2021;85(6):3211-3226.
21. Kim TH, Cho J, Zhao B, Bilgic B. Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks.
22. Heydari A, Ahmadi A, Kim TH, Bilgic B. Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks. arXiv preprint arXiv:240802988 .
23. Heydari A, Ahmadi A, Kim TH, Bilgic B. Joint MAPLE: Accelerated joint T 1 and T * 2 mapping with scan ‐ specific self ‐ supervised networks. Magn Reson Med .
24. Yaman B, Hosseini SAH, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv preprint arXiv:210207737 .
25. Uecker M, Lai P, Murphy MJ, et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med . 2014;71(3):990-1001.
26. Haldar JP. Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI. IEEE Trans Med Imaging . 2013;33(3):668-681.
27. Kim TH, Setsompop K, Haldar JP. LORAKS makes better SENSE: phase ‐ constrained partial fourier SENSE reconstruction without phase calibration. Magn Reson Med . 2017;77(3):1021-1035.
28. Bilgic B, Kim TH, Liao C, et al. Improving parallel imaging by jointly reconstructing multi ‐ contrast data. Magn Reson Med . 2018;80(2):619-632.
29. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine . 1999;42(5):952-962.