Fetching the paper…
Reading the bibliography…
Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue.
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Zico Kolter, “Differentiable convex optimization layers,” Advances in Neural Information Processing Systems , vol. 32, no. NeurIPS, 2019. 10.48550/arXiv.1910.12430
1910
Earlier work this paper cites.
P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization . Academic Press, 1981. ISBN 9780122839504
1981
Earlier work this paper cites.
D. C. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming , vol. 45, no. 1-3, pp. 503–528, 8 1989. 10.1007/BF01589116
1989
Earlier work this paper cites.
M. Crowder, J. R. Magnus, and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics. John Wiley & Sons, 1989. ISBN 9781119541202
1989
Earlier work this paper cites.
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “SENSE: Sensitivity encoding for fast MRI,” Magnetic Resonance in Medicine , vol. 42, no. 5, pp. 952–962, 1999. 10.1002/(SICI)1522-2594(199911)42:5¡952::AID-MRM16¿3.0.CO;2-S
1999
Earlier work this paper cites.
D. O. Walsh, A. F. Gmitro, and M. W. Marcellin, “Adaptive reconstruction of phased array MR imagery,” Magnetic Resonance in Medicine , vol. 43, no. 5, pp. 682–690, 2000. 10.1002/(SICI)1522-2594(200005)43:5¡682::AID-MRM10¿3.0.CO;2-G
2000
Earlier work this paper cites.
T. Bachlechner, B. P. Majumder, H. Mao, G. Cottrell, and J. McAuley, “ReZero is All You Need: Fast Convergence at Large Depth,” 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 , no. 1, pp. 1352–1361, 2021. 10.48550/arXiv./2003.04887
2003
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
J. Nocedal and S. Wright, Numerical Optimization , ser. Springer Series in Operations Research and Financial Engineering. Springer New York, 2006. ISBN 9780387303031
2006
Earlier work this paper cites.
B. Aubert-Broche, M. Griffin, G. B. Pike, A. C. Evans, and D. L. Collins, “Twenty new digital brain phantoms for creation of validation image data bases,” IEEE Transactions on Medical Imaging , vol. 25, no. 11, pp. 1410–1416, 2006. 10.1109/TMI.2006.883453
2006
Earlier work this paper cites.
M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed Sensing MRI,” IEEE Signal Processing Magazine , vol. 25, no. 2, pp. 72–82, 2008. 10.1109/MSP.2007.914728
2007
Earlier work this paper cites.
K. T. Block, M. Uecker, and J. Frahm, “Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint,” Magnetic Resonance in Medicine , vol. 57, no. 6, pp. 1086–1098, 2007. 10.1002/mrm.21236
2007
Earlier work this paper cites.
K. Kreutz-Delgado, “The complex gradient operator and the CR-calculus,” arXiv preprint , 2009. 10.48550/arXiv.0906.4835
2009
Earlier work this paper cites.
M. Doneva, P. Börnert, H. Eggers, C. Stehning, J. Sénégas, and A. Mertins, “Compressed sensing reconstruction for magnetic resonance parameter mapping,” Magnetic Resonance in Medicine , vol. 64, no. 4, pp. 1114–1120, 2010. 10.1002/mrm.22483
2010
Earlier work this paper cites.
M. V. Afonso, J. M. Bioucas-Dias, and M. A. Figueiredo, “Fast image recovery using variable splitting and constrained optimization,” IEEE Transactions on Image Processing , vol. 19, no. 9, pp. 2345–2356, 2010. 10.1109/TIP.2010.2047910
2010
Earlier work this paper cites.
J. Tran-Gia, D. Stäb, T. Wech, D. Hahn, and H. Köstler, “Model-based Acceleration of Parameter mapping (MAP) for saturation prepared radially acquired data,” Magnetic Resonance in Medicine , vol. 70, no. 6, pp. 1524–1534, 2013. 10.1002/mrm.24600
2013
Earlier work this paper cites.
D. Ma, V. Gulani, N. Seiberlich, K. Liu, J. L. Sunshine, J. L. Duerk, and M. A. Griswold, “Magnetic resonance fingerprinting,” Nature , vol. 495, no. 7440, pp. 187–192, 3 2013. 10.1038/nature11971
2013
Earlier work this paper cites.
O. de Oliveira, “The implicit and the inverse function theorems: Easy proofs,” Real Analysis Exchange , vol. 39, no. 1, pp. 207–218, 2013. 10.14321/realanalexch.39.1.0207
2013
Earlier work this paper cites.
B. Zhao, F. Lam, and Z. P. Liang, “Model-based MR parameter mapping with sparsity constraints: Parameter estimation and performance bounds,” IEEE Transactions on Medical Imaging , vol. 33, no. 9, pp. 1832–1844, 2014. 10.1109/TMI.2014.2322815
2014
Earlier work this paper cites.
K. Chow, J. A. Flewitt, J. D. Green, J. J. Pagano, M. G. Friedrich, and R. B. Thompson, “Saturation recovery single-shot acquisition (SASHA) for myocardial T 1 mapping,” Magnetic Resonance in Medicine , vol. 71, no. 6, pp. 2082–2095, 2014. 10.1002/mrm.24878
2014
Earlier work this paper cites.
S. J. Inati, M. S. Hansen, and P. Kellman, “A Fast Optimal Method for Coil Sensitivity Estimation and Adaptive Coil Combination for Complex Images,” Proceedings of the 22nd Annual Meeting of ISMRM , 2014
2014
Earlier work this paper cites.
M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “ESPIRiT–an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA,” Magnetic Resonance in Medicine , vol. 71, no. 3, pp. 990–1001, 2014. 10.1002/mrm.24751
2014
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 9351, pp. 234–241, 2015. 10.1007/978-3-319-24574-4_28
2015
Cited alongside, same era.
L. Wang, C.-Y. Lee, Z. Tu, and S. Lazebnik, “Training deeper convolutional networks with deep supervision,” 2015
2015
Cited alongside, same era.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings , pp. 1–15, 2015. 10.48550/arXiv.412.6980
2015
Z. Lv, F. Dellaert, J. M. Rehg, and A. Geiger, “Taking a deeper look at the inverse compositional algorithm,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2019-June, pp. 4576–4585, 2019. 10.1109/CVPR.2019.00471
2019
Later among the works it cites.
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li, “Bag of tricks for image classification with convolutional neural networks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2019-June, pp. 558–567, 2019. 10.48550/arXiv.1812.01187
2019
Later among the works it cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” 7th International Conference on Learning Representations, ICLR 2019 , 2019. 10.48550/arXiv.1711.05101
2019
Later among the works it cites.
A. Sriram, J. Zbontar, T. Murrell, A. Defazio, C. L. Zitnick, N. Yakubova, F. Knoll, and P. Johnson, “End-to-End Variational Networks for Accelerated MRI Reconstruction,” Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science , vol. 12262 LNCS, pp. 64–73, 2020. 10.1007/978-3-030-59713-9_7
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Y. Yang, J. Sun, H. Li, and Z. Xu, “Deep ADMM-Net for compressive sensing MRI,” Advances in Neural Information Processing Systems , vol. 29, 2016. 10.5555/3157096.3157098
2016
Cited alongside, same era.
J. Z. Bojorquez, S. Bricq, C. Acquitter, F. Brunotte, P. M. Walker, and A. Lalande, “What are normal relaxation times of tissues at 3 T?” Magnetic Resonance Imaging , vol. 35, pp. 69–80, 2017. 10.1016/j.mri.2016.08.021
2016
Cited alongside, same era.
S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo, “On differentiating parameterized argmin and argmax problems with application to bi-level optimization,” 2016
2016
Cited alongside, same era.
F. Pedregosa, “Hyperparameter optimization with approximate gradient,” 33rd International Conference on Machine Learning, ICML 2016 , vol. 2, pp. 1150–1159, 2016. 10.48550/arXiv.1602.02355
2016
Cited alongside, same era.
B. Amos and J. Z. Kolter, “OptNet: Differentiable optimization as a layer in neural networks,” 34th International Conference on Machine Learning, ICML 2017 , vol. 1, pp. 179–191, 2017. 10.48550/arXiv.1703.0044
2017
Cited alongside, same era.
K. J. Layton, S. Kroboth, F. Jia, S. Littin, H. Yu, J. Leupold, J.-F. Nielsen, T. Stöcker, and M. Zaitsev, “Pulseq: A rapid and hardware-independent pulse sequence prototyping framework,” Magnetic Resonance in Medicine , vol. 77, no. 4, pp. 1544–1552, 2017. 10.1002/mrm.26235
2017
Cited alongside, same era.
Z. Qiu, T. Yao, and T. Mei, “Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks,” Proceedings of the IEEE International Conference on Computer Vision , vol. 2017-Octob, pp. 5534–5542, 10 2017. 10.1109/ICCV.2017.590
2017
Cited alongside, same era.
X. Wang, V. Roeloffs, J. Klosowski, Z. Tan, D. Voit, M. Uecker, and J. Frahm, “Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH,” Magnetic Resonance in Medicine , vol. 79, no. 2, pp. 730–740, 2018. 10.1002/mrm.26726
2018
Cited alongside, same era.
2020
Later among the works it cites.
H. Jeelani, Y. Yang, R. Zhou, C. M. Kramer, M. Salerno, and D. S. Weller, “A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks,” Proceedings - International Symposium on Biomedical Imaging , vol. 2020-April, pp. 1941–1944, 2020. 10.1109/ISBI45749.2020.9098459
2020
Later among the works it cites.
Y. Wu and K. He, “Group Normalization,” International Journal of Computer Vision , vol. 128, no. 3, pp. 742–755, 2020. 10.1007/s11263-019-01198-w
2020
Later among the works it cites.
B. Zhou and S. Kevin Zhou, “Dudornet: Learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , pp. 4272–4281, 2020. 10.1109/CVPR42600.2020.00433
2020
Later among the works it cites.
Y. Jun, H. Shin, T. Eo, T. Kim, and D. Hwang, “Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method,” Medical Image Analysis , vol. 70, p. 102017, 2021. 10.1016/j.media.2021.102017
2021
Later among the works it cites.
A. Kofler, M. Haltmeier, T. Schaeffter, and C. Kolbitsch, “An end-to-end-trainable iterative network architecture for accelerated radial multi-coil 2d cine mr image reconstruction,” Medical Physics , vol. 48, no. 5, pp. 2412–2425, 2021
2021
Later among the works it cites.
D. Gilton, G. Ongie, and R. Willett, “Model Adaptation for Inverse Problems in Imaging,” IEEE Transactions on Computational Imaging , vol. 7, no. 2, pp. 661–674, 2021. 10.1109/TCI.2021.3094714
2021
Later among the works it cites.
K. Hammernik, J. Schlemper, C. Qin, J. Duan, R. M. Summers, and D. Rueckert, “Systematic evaluation of iterative deep neural networks for fast parallel mri reconstruction with sensitivity-weighted coil combination,” Magnetic Resonance in Medicine , vol. 86, no. 4, pp. 1859–1872, 2021. 10.1002/mrm.28827
2021
Later among the works it cites.
X. Xu, W. Gan, S. V. Kothapalli, D. A. Yablonskiy, and U. S. Kamilov, “CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping,” 2022. 10.48550/arXiv.2210.06330
2022
Later among the works it cites.
R. Guo, H. El-Rewaidy, S. Assana, X. Cai, A. Amyar, K. Chow, X. Bi, T. Yankama, J. Cirillo, P. Pierce, B. Goddu, L. Ngo, and R. Nezafat, “Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach,” Journal of Cardiovascular Magnetic Resonance , vol. 24, no. 1, pp. 1–15, 2022. 10.1186/s12968-021-00834-0
2022
Later among the works it cites.
K. Hammernik, T. Kustner, B. Yaman, Z. Huang, D. Rueckert, F. Knoll, and M. Akcakaya, “Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging,” IEEE Signal Processing Magazine , vol. 40, no. 1, pp. 98–114, 2023. 10.1109/msp.2022.3215288
2022
Later among the works it cites.
D. Chen, M. E. Davies, and M. Golbabaee, “Deep Unrolling for Magnetic Resonance Fingerprinting,” Proceedings - International Symposium on Biomedical Imaging , no. 2, 2022. 10.1109/ISBI52829.2022.9761475
2022
Later among the works it cites.
K. Hammernik, T. Küstner, and D. Rueckert, “Machine Learning for MRI Reconstruction,” in Magnetic Resonance Image Reconstruction , C. Prieto, M. I. Doneva, and M. Akcakaya, Eds. Elsevier, 2022, ch. 11, pp. 281–317
2022
Later among the works it cites.
X. Zhang, Q. Duchemin, K. Liu*, C. Gultekin, S. Flassbeck, C. Fernandez-Granda, and J. Assländer, “Cramér–Rao bound-informed training of neural networks for quantitative MRI,” Magnetic Resonance in Medicine , vol. 88, no. 1, pp. 436–448, 2022. 10.1002/mrm.29206
2022
Later among the works it cites.
H. Li, M. Yang, J. H. Kim, C. Zhang, R. Liu, P. Huang, D. Liang, X. Zhang, X. Li, and L. Ying, “SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling,” Magnetic Resonance in Medicine , vol. 89, no. 1, pp. 64–76, 2023. 10.1002/mrm.29411
2023
Closest in time.
F. F. Zimmermann and A. Kofler, “NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps,” International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM) , 2023. 10.48550/arXiv.2309.15608
2023
Closest in time.
F. F. Zimmermann, A. Kofler, C. Kolbitsch, and P. Schuenke, “Semi-Supervised Learning for Spatially Regularized Quantitative MRI Reconstruction - Application to Simultaneous T1, B0, B1 Mapping,” 2023, 1166, ISMRM Annual Meeting
2023
Closest in time.
Y. Jun, J. Cho, X. Wang, M. Gee, P. E. Grant, B. Bilgic, and B. Gagoski, “SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS,” Magnetic Resonance in Medicine , vol. 90, no. 5, pp. 2019–2032, 2023. 10.1002/mrm.29786
2023
Closest in time.