References
1. Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR Biomed . 2022;35(4):e4416.
2. Deoni SCL. Quantitative relaxometry of the brain. Top Magn Reson imaging . 2010;21(2):101-113.
3. Berg RC, Leutritz T, Weiskopf N, Preibisch C. Multi ‐ parameter quantitative mapping of R1, R2*, PD, and MTsat is reproducible when accelerated with Compressed SENSE. Neuroimage . 2022;253:119092.
4. Jansen JFA, Kooi ME, Kessels AGH, Nicolay K, Backes WH. Reproducibility of quantitative cerebral T2 relaxometry, diffusion tensor imaging, and 1H magnetic resonance spectroscopy at 3.0 Tesla. Invest Radiol . 2007;42(6):327-337.
5. Feng L, Otazo R, Jung H, et al. Accelerated cardiac T2 mapping using breath ‐ hold multiecho fast spin ‐ echo pulse sequence with k ‐ t FOCUSS. Magn Reson Med . 2011;65(6):1661-1669.
6. 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.
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. Wang L, Schweitzer ME, Padua A, Regatte RR. Rapid 3D ‐ T1 mapping of cartilage with variable flip angle and parallel imaging at 3.0 T. J Magn Reson Imaging An Off J Int Soc Magn Reson Med . 2008;27(1):154-161.
9. 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.
10. Huang C, Graff CG, Clarkson EW, Bilgin A, Altbach MI. T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magn Reson Med . 2012;67(5):1355-1366.
11. Zhang T, Pauly JM, Levesque IR. Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med . 2015;73(2):655-661.
12. Sumpf TJ, Uecker M, Boretius S, Frahm J. Model ‐ based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin ‐ echo MRI. J Magn Reson Imaging . 2011;34(2):420-428.
13. Sumpf TJ, Petrovic A, Uecker M, Knoll F, Frahm J. Fast T2 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.
14. Zhao B, Lam F, Liang Z-P. Model-based MR parameter mapping with sparsity constraints: parameter estimation and performance bounds. IEEE Trans Med Imaging . 2014;33(9):1832-1844.
15. Roeloffs V, Wang X, Sumpf TJ, Untenberger M, Voit D, Frahm J. Model ‐ based reconstruction for T1 mapping using single ‐ shot inversion ‐ recovery radial FLASH. Int J Imaging Syst Technol . 2016;26(4):254-263.
16. Wang X, Kohler F, Unterberg-Buchwald C, Lotz J, Frahm J, Uecker M. Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance. J Cardiovasc Magn Reson . 2019;21:1-11.
17. Hilbert T, Sumpf TJ, Weiland E, et al. Accelerated T2 mapping combining parallel MRI and model ‐ based reconstruction: GRAPPATINI. J Magn Reson Imaging . 2018;48(2):359-368.
18. Maier O, Schoormans J, Schloegl M, et al. Rapid T1 quantification from high resolution 3D data with model ‐ based reconstruction. Magn Reson Med . 2019;81(3):2072-2089.
19. 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.
20. Wang X, Cho J, Jun Y, Gagoski B, Bilgic B. Model-Based Phase-Difference Reconstruction for Accelerated Phase-Based T2 Mapping.
21. Cai C, Wang C, Zeng Y, et al. Single ‐ shot T2 mapping using overlapping ‐ echo detachment planar imaging and a deep convolutional neural network. Magn Reson Med . 2018;80(5):2202-2214.
22. Li H, Yang M, Kim J, et al. Ultra-fast simultaneous T1rho and T2 mapping using deep learning In Proceedings of the 28th Annual Meeting of ISMRM , Virtual Conference and Exhibition , 2020, vol.
23. Hamilton JI, Seiberlich N. Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE . 2019;108(1):69-85.
24. Virtue P, Stella XY, Lustig M. Better than real: Complex-valued neural nets for MRI fingerprinting. In: 2017 IEEE International Conference on Image Processing (ICIP) . IEEE; 2017:3953-3957.
25. Kim TH, Cho J, Zhao B, Bilgic B. Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks. In Proceedings of the Annual Meeting of ISMRM, London, 2022. p.
26. Heydari A, Kim TH, Ahmadi A, Bilgic B. Joint MR T1 and T2* Parameter Mapping with Scan Specific Unsupervised Networks. In Proceedings of the Annual Meeting of ISMRM, Toronto, 2023. p.
27. Yaman B, Hosseini SAH, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv Prepr arXiv210207737 . Published online 2021.
28. 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.
29. Zhang T, Pauly JM, Vasanawala SS, Lustig M. Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med . 2013;69(2):571-582.
30. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med An Off J Int Soc Magn Reson Med . 1999;42(5):952-962.
31. Haldar JP. Low-rank modeling of local $ k $-space neighborhoods (LORAKS) for constrained MRI. IEEE Trans Med Imaging . 2013;33(3):668-681.
32. 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.
33. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage . 2002;17(2):825-841.