Abstract
Magnetic resonance (MR) imaging is widely used in clinical scenarios, while the long acquisition time is still one of its major limitations. An efficient way to accelerate the imaging process is to subsample the k-space, where MR signal is physically acquired, and then estimate the fully-sampled MR image from subsampled signal with a learned reconstruction model. In this work, we are inspired from the idea of neural network pruning and propose a novel strategy to learn the k-space subsampling pattern and the reconstruction model alternately in a data-driven fashion. More specifically, in each iteration of learning, we first greedily eliminate a few phases that are considered less important in the k-space according to their assigned weights, and then fine-tune the reconstruction model. In our pilot study, experiments demonstrated the robustness and superiority of our proposed method in both single- and multi-modal MRI settings.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bahadir, C.D., Dalca, A.V., Sabuncu, M.R.: Learning-based optimization of the under-sampling pattern in MRI. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 780–792. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_61
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Feinberg, D.A., Hale, J.D., Watts, J.C., Kaufman, L., Mark, A.: Halving MR imaging time by conjugation: demonstration at 3.5 kG. Radiology 161(2), 527–531 (1986)
Haldar, J.P., Kim, D.: OEDIPUS: an experiment design framework for sparsity-constrained MRI. IEEE Trans. Med. Imaging 38(7), 1545–1558 (2019)
Hu, H., Peng, R., Tai, Y.W., Tang, C.K.: Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv:1607.03250 [cs], July 2016
Jackson, J., Meyer, C., Nishimura, D., Macovski, A.: Selection of a convolution function for Fourier inversion using gridding (computerised tomography application). IEEE Trans. Med. Imaging 10(3), 473–478 (1991)
Jin, K.H., Unser, M., Yi, K.M.: Self-supervised deep active accelerated MRI. arXiv:1901.04547 [cs], January 2019
Levine, E., Hargreaves, B.: On-the-fly adaptive k-space sampling for linear MRI reconstruction using moment-based spectral analysis. IEEE Trans. Med. Imaging 37(2), 557–567 (2018)
Liu, D.D., Liang, D., Liu, X., Zhang, Y.T.: Under-sampling trajectory design for compressed sensing MRI. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 73–76, August 2012
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2755–2763, October 2017
Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. IEEE Signal Process. Mag. 25(2), 72–82 (2008)
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)
Marseille, G.J., de Beer, R., Fuderer, M., Mehlkopf, A.F., van Ormondt, D.: Nonuniform phase-encode distributions for MRI scan time reduction. J. Magn. Reson., Ser. B 111(1), 70–75 (1996)
McGibney, G., Smith, M.R., Nichols, S.T., Crawley, A.: Quantitative evaluation of several partial fourier reconstruction algorithms used in MRI. Magn. Reson. Med. 30(1), 51–59 (1993)
Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11256–11264, June 2019
Paszke, A., et al.: PyTorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019)
Ravishankar, S., Bresler, Y.: MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans. Med. Imaging 30(5), 1028–1041 (2011)
Tsai, C.M., Nishimura, D.G.: Reduced aliasing artifacts using variable-density k-space sampling trajectories. Magn. Reson. Med. 43(3), 452–458 (2000)
Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020)
Wang, S., et al.: Accelerating magnetic resonance imaging via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 514–517, April 2016
Xiang, L., et al.: Ultra-fast T2-weighted MR reconstruction using complementary T1-weighted information. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 215–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_25
Zbontar, J., et al.: fastMRI: an open dataset and benchmarks for accelerated MRI. arXiv:1811.08839 [physics, stat], December 2019
Zhang, Z., Romero, A., Muckley, M.J., Vincent, P., Yang, L., Drozdzal, M.: Reducing uncertainty in undersampled MRI reconstruction with active acquisition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2049–2058 (2019)
Zijlstra, F., Viergever, M.A., Seevinck, P.R.: Evaluation of variable density and data-driven K-space undersampling for compressed sensing magnetic resonance imaging. Invest. Radiol. 51(6), 410–419 (2016)
Acknowledgment
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400), Science and Technology Commission of Shanghai Municipality (19QC1400600), and China Scholarship Council.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xuan, K., Sun, S., Xue, Z., Wang, Q., Liao, S. (2020). Learning MRI k-Space Subsampling Pattern Using Progressive Weight Pruning. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-59713-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59712-2
Online ISBN: 978-3-030-59713-9
eBook Packages: Computer ScienceComputer Science (R0)