Abstract
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.
J. Duan and J. Schlemper—Contributed equally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schlemper, J., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imag. 37(2), 491–503 (2018)
Yan, Y., et al.: Deep ADMM-Net for compressive sensing MRI. In: NIPS, pp. 10–18 (2016)
Hammernik, K., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79(6), 3055–3071 (2018)
Aggarwal, H.K., et al.: MoDL: model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imag. 38(2), 394–405 (2019)
Han, Y., et al.: k-space deep learning for accelerated MRI. arXiv:1805.03779 (2018)
Akcakaya, M., et al.: Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: database-free deep learning for fast imaging. Magn. Reson. Med. 81(1), 439–453 (2018)
Jin, K., et al.: Self-supervised deep active accelerated MRI. arXiv:1901.04547 (2019)
Mardani, M., et al.: Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans. Med. Imag. 38(1), 167–179 (2019)
Tezcan, K., et al.: MR image reconstruction using deep density priors. IEEE Trans. Med. Imag. (2018)
Zhang, P., Wang, F., Xu, W., Li, Y.: Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in K-space. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 180–188. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_21
Uecker, M., et al.: ESPIRiT an eigenvalue approach to autocalibrating parallel MRI: where sense meets grappa. Magn. Reson. Med. 71(3), 990–1001 (2014)
Lu, W., et al.: Implementation of high-order variational models made easy for image processing. Math. Method Appl. Sci. 39(14), 4208–4233 (2016)
Lu, W., et al.: Graph-and finite element-based total variation models for the inverse problem in diffuse optical tomography. Biomed. Opt. Express 10(6), 2684–2707 (2019)
Duan, J., et al.: Denoising optical coherence tomography using second order total generalized variation decomposition. Biomed. Signal Process. Control 24, 120–127 (2016)
Liu, R.W., et al.: Undersampled CS image reconstruction using nonconvex nonsmooth mixed constraints. Multimed. Tools Appl. 78(10), 12749–12782 (2019)
Uecker, M., et al.: Software toolbox and programming library for compressed sensing and parallel imaging, Citeseer
Murphy, M., et al.: Fast l1-spirit compressed sensing parallel imaging MRI: Scalable parallel implementation and clinically feasible runtime. IEEE Trans. Med. Imag. 31(6), 1250–1262 (2012)
Acknowledgements
This work was supported by the EPSRC Programme Grant (EP/P001009/1) and the British Heart Foundation (NH/17/1/32725). The TITAN Xp GPU used for this research was kindly donated by the NVIDIA Corporation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Duan, J. et al. (2019). VS-Net: Variable Splitting Network for Accelerated Parallel MRI Reconstruction. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_78
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_78
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)
