Fetching the paper…
Reading the bibliography…
Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation.
Mezrich, R.: A perspective on k-space. Radiology 195
1995
Earlier work this paper cites.
Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: Sense: sensitivity encoding for fast mri. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 42
1999
Earlier work this paper cites.
Lewicki, M.S., Sejnowski, T.J.: Learning overcomplete representations. Neural computation 12
2000
Earlier work this paper cites.
Fisher, R.B.: Cvonline: The evolving, distributed, non-proprietary, on-line compendium of computer vision. Retrieved January 28, 2006 from http://homepages. inf. ed. ac. uk/rbf/CVonline (2008)
2008
Earlier work this paper cites.
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning. pp. 1096–1103 (2008)
2008
Earlier work this paper cites.
Liang, D., Liu, B., Wang, J., Ying, L.: Accelerating sense using compressed sensing. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 62
2009
Earlier work this paper cites.
Haldar, J.P., Hernando, D., Liang, Z.P.: Compressed-sensing mri with random encoding. IEEE transactions on Medical Imaging 30
2010
Earlier work this paper cites.
Ravishankar, S., Bresler, Y.: Mr image reconstruction from highly undersampled k-space data by dictionary learning. IEEE transactions on medical imaging 30
2010
Earlier work this paper cites.
Patel, V.M., Chellappa, R.: Sparse representations, compressive sensing and dictionaries for pattern recognition. In: The first Asian conference on pattern recognition. pp. 325–329. IEEE (2011)
2011
Earlier work this paper cites.
Patel, V.M., Maleh, R., Gilbert, A.C., Chellappa, R.: Gradient-based image recovery methods from incomplete fourier measurements. IEEE Transactions on Image Processing 21
2011
Earlier work this paper cites.
Majumdar, A.: Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic mri reconstruction. Magnetic resonance imaging 33
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
2015
Cited alongside, same era.
Tamir, J.I., Ong, F., Cheng, J.Y., Uecker, M., Lustig, M.: Generalized magnetic resonance image reconstruction using the berkeley advanced reconstruction toolbox. In: ISMRM Workshop on Data Sampling & Image Reconstruction, Sedona, AZ (2016)
2016
Cited alongside, same era.
Edmund, J.M., Nyholm, T.: A review of substitute ct generation for mri-only radiation therapy. Radiation Oncology 12
2017
Cited alongside, same era.
Schlemper, J., et al.: A deep cascade of convolutional neural networks for dynamic mr image reconstruction. IEEE transactions on Medical Imaging 37
2017
Cited alongside, same era.
Chen, E.Z., Chen, T., Sun, S.: Mri image reconstruction via learning optimization using neural odes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 83–93. Springer (2020)
2020
Later among the works it cites.
Guo, P., Wang, P., Yasarla, R., Zhou, J., Patel, V.M., Jiang, S.: Anatomic and molecular mr image synthesis using confidence guided cnns. IEEE Transactions on Medical Imaging pp. 1–1 (2020). https://doi.org/10.1109/TMI.2020.3046460
2020
Later among the works it cites.
Guo, P., Wang, P., Zhou, J., Patel, V.M., Jiang, S.: Lesion mask-based simultaneous synthesis of anatomic and molecular mr images using a gan. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 104–113. Springer (2020)
2020
Later among the works it cites.
Knoll, F., et al.: fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning. Radiology: Artificial Intelligence 2
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H.J., Hwang, D.: Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magnetic resonance in medicine 80
2018
Cited alongside, same era.
Lee, D., Yoo, J., Tak, S., Ye, J.C.: Deep residual learning for accelerated mri using magnitude and phase networks. IEEE Transactions on Biomedical Engineering 65
2018
Cited alongside, same era.
Qin, C., et al.: Convolutional recurrent neural networks for dynamic mr image reconstruction. IEEE Transactions on Medical Imaging 38
2018
Cited alongside, same era.
Akçakaya, M., Moeller, S., Weingärtner, S., Uğurbil, K.: Scan-specific robust artificial-neural-networks for k-space interpolation (raki) reconstruction: Database-free deep learning for fast imaging. Magnetic resonance in medicine 81
2019
Cited alongside, same era.
Jiang, S., et al.: Identifying recurrent malignant glioma after treatment using amide proton transfer-weighted mr imaging: a validation study with image-guided stereotactic biopsy. Clinical Cancer Research 25
2019
Cited alongside, same era.
Putzky, P., Welling, M.: Invert to learn to invert. arXiv preprint arXiv:1911.10914 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
Liang, D., Cheng, J., Ke, Z., Ying, L.: Deep magnetic resonance image reconstruction: Inverse problems meet neural networks. IEEE Signal Processing Magazine 37
2020
Later among the works it cites.
2020
Later among the works it cites.
Valanarasu, J.M.J., Sindagi, V.A., Hacihaliloglu, I., Patel, V.M.: Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 363–373. Springer (2020)
2020
Later among the works it cites.
Yasarla, R., Valanarasu, J.M.J., Patel, V.M.: Exploring overcomplete representations for single image deraining using cnns. IEEE Journal of Selected Topics in Signal Processing (2020)
2020
Later among the works it cites.
Guo, P., Wang, P., Zhou, J., Jiang, S., Patel, V.M.: Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2423–2432 (2021)
2021
Closest in time.
Valanarasu, J.M.J., Patel, V.M.: Overcomplete deep subspace clustering networks. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 746–755 (2021)
2021
Closest in time.