1. Liao C, Cao X, Cho J, Zhang Z, Setsompop K, Bilgic B. Highly efficient MRI through multi-shot echo planar imaging. Wavelets and Sparsity XVIII, Proceedings of SPIE 11138.
2. Bhushan C, Joshi AA, Leahy RM, Haldar JP. Improved B0-distortion correction in diffusion MRI using Interlaced q-space sampling and constrained reconstruction. Magnetic Resonance in Medicine.
3. Haldar JP, Zhuo J. P-LORAKS: low-rank modeling of local k-space neighborhoods with parallel imaging data. Magnetic Resonance in Medicine.
4. Kim TH, Setsompop K, Haldar JP. LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration. Magnetic Resonance in Medicine.
5. Haldar JP, Setsompop K. Linear Predictability in Magnetic Resonance Imaging Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging. IEEE Signal Processing Magazine.
6. Shin PJ, Larson PEZ, Ohliger PA, Elad M, Pauly JM, Vigneron DB, Lustig M. Calibrationlessparallel imaging reconstruction based on structured low-rank matrix completion. Magnetic Resonance in Medicine.
7. Ongie G, Jacob M. Off-the-grid recovery of piecewise constant images from few Fourier samples SIAM Journal on Imaging Sciences.
8. Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
9. Heckel R, Hand P. Deep Decoder: Concise image representations from untrained non-convolutional networks. International Conference on Learning Representations (ICLR).
10. Arora S, Roeloffs V, Lustig M. Untrained Modified Deep Decoder for Joint Denoising and Parallel Imaging Reconstruction. International Society for Magnetic Resonance in Medicine Virtual Conference & Exhibition.
11. pp. 65–73.
12. pp. 1218–1232 5. Zahneisen B, Aksoy M, Maclaren J, Wuerslin C, Bammer R. Extended hybrid-space SENSE for EPI: Off-resonance and eddy current corrected joint interleaved blip-up/down reconstruction. NeuroImage.
13. pp. 668–681 9. Haldar JP. Autocalibrated loraks for fast constrained MRI reconstruction. IEEE International Symposium on Biomedical Imaging (ISBI).
14. pp. 959–970 14. Jin KH, Lee D, Ye JC. A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank hankel matrix. IEEE Transactions on Computational Imaging.
15. pp. 910–913.
16. pp. 1004–1041.
17. pp. 480–495.
18. pp. 1499–1514.
19. pp. 1021–1035.
20. pp. 494–507 7. Kim TH, Garg P, Haldar JP. LORAKI: Autocalibrated recurrent neural networks for autoregressive MRI reconstruction in k-Space. arXiv preprint.
21. pp. 97–108 6. Mani M, Jacob M, Kelley D, Magnotta V. Multi-shot sensitivityencoded diffusion data recovery using structured low-rank matrix completion (MUSSELS). Magnetic Resonance in Medicine.
22. pp. 9446–9454.
23. pp 394–405 21. Tamir JI, Yu SX, Lustig M. Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data. arXiv preprint.
24. arXiv:1909.12999 3. Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine.
25. p. 1113818 2. Cao X, Liao C, Zhang Z, Iyer SS, Wang K, He H, Liu H, Setsompop K, Zhong J, Bilgic B. T2-BUDA-gSlider: rapid high isotropic resolution T2 mapping with blip-up/down acquisition, generalized SLIce Dithered Enhanced Resolution and subspace reconstruction; arXiv preprint.
26. arXiv:1910.13110
27. arXiv:1904.09390 8. Haldar JP. Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI. IEEE Transactions on Medical Imaging.
28. arXiv:2007.02471 20. Aggarwal HK, Mani MP, and Jacob M. MoDL: model based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging.
29. p. 3585 19. Darestani MZ. Heckel R. Accelerated MRI with Un-trained Neural Networks. arXiv preprint.