Abstract
This work addresses the well known problem of reconstructing magnetic resonance images from their partially samples K-space. Compressed sensing (CS) based techniques have been used rampantly for the said problem. Later studies, instead of employing a fixed basis (like DCT, wavelet etc. as used in CS), learnt the basis adaptively from the image itself. Such studies, loosely dubbed as dictionary learning (DL) showed marked improvement over CS. This work proposes deep dictionary learning based inversion. Instead of learning a single level of basis, we learn multiple levels adaptively from the image, while reconstructing it. The results show marked improvement over all previously known techniques.
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References
Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)
Ravishankar, S., Bresler, Y.: MR image reconstruction from highly under sampled k-space data by dictionary learning. IEEE Trans. Med. Imaging 30(5), 1028–1041 (2011)
Ravishankar, S., Bresler, Y.: Sparsifying transform learning for compressed sensing MRI. In: IEEE ISBI, pp. 17–20 (2013)
Majumdar, A.: Compressed Sensing for Magnetic Resonance Image Reconstruction. Cambridge University Press, Cambridge (2015)
Mehta, J., Majumdar, A.: RODEO: robust DE-aliasing auto encoder for real-time medical image reconstruction. Pattern Recogn. 63, 499–510 (2017)
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., Ashok, A.: ReconNet: non-iterative reconstruction of images from compressively sensed measurements. In: IEEE CVPR, pp. 449–458 (2016)
Tariyal, S., Majumdar, A., Singh, R., Vatsa, M.: Deep dictionary learning. IEEE Access 4, 10096–10109 (2016)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Fixed-point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212 (2011)
Afonso, M.V., Bioucas-Dias, J.M., Figueiredo, M.A.: An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process. 20(3), 681–695 (2011)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)
Maggu, J., Singh, P., Majumdar, A.: Multi-echo reconstruction from partial K-space scans via adaptively learnt basis. Magn. Reson. Imaging 45, 105–112 (2018)
Majumdar, A., Ward, R.K.: Joint reconstruction of multiecho MR images using correlated sparsity. Magn. Reson. Imaging 29(7), 899–906 (2011)
Majumdar, A., Ward, R.K.: Accelerating multi-echo T2 weighted MR imaging: analysis prior group-sparse optimization. J. Magn. Reson. 210(1), 90–97 (2011)
Majumdar, A., Ward, R.K.: Calibration-less multi-coil MR image reconstruction. Magn. Reson. Imaging 30(7), 1032–1045 (2012)
Majumdar, A., Ward, R.K.: Nuclear norm-regularized SENSE reconstruction. Magn. Reson. Imaging 30(2), 213–221 (2012)
Acknowledgements
We are thankful in part to the Infosys Center for Artificial Intelligence @ IIITD for partial support and in part to 5IOA036 FA23861610004 grant by Air Force Office of Scientific Research (AFOSR), AOARD.
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John Lewis, D., Singhal, V., Majumdar, A. (2018). Adaptive Deep Dictionary Learning for MRI Reconstruction. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_1
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