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.
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)