Reconstruction of Simultaneous Multi-Slice MRI Data by Combining Virtual Conjugate Coil Technology and Convolutional Neural Network

doi:10.11938/cjmr20202800

Abstract

Abstract: This paper proposes an image reconstruction method for simultaneous multi-slice imaging (SMS) by combining the virtual conjugate coil (VCC) technology and robust artificial-neural-networks for k-space interpolation (RAKI). This method can effectively improve the reconstruction quality, and is named VIRGINIA (VIRtual conjuGate coIls Neural-networks InterpolAtion). VIRGINIA utilizes the complex conjugate symmetry property of the virtual coil concept to generate virtual coil data for training, and obtains better image quality by applying the trained network to the original aliased SMS data. With experimental data, the VIRGINIA method was compared to other reconstruction methods (i.e., RAKI only and slice-GRAPPA) in terms of quantitative indices such as structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The results demonstrated that, under some certain slice-acceleration factors, VIRGINIA produced better reconstruction quality than those obtainable by Slice-GRAPPA and RAKI.

Key words: magnetic resonance image reconstruction, simultaneous multi-slice imaging, robust artificial-neural-networks for k-space interpolation (RAKI), virtual conjugate coil, convolutional neural network (CNN)

CLC Number:

O482.53

WANG Wan-ting, SU Shi, JIA Sen, LIANG Dong, WANG Hai-feng. Reconstruction of Simultaneous Multi-Slice MRI Data by Combining Virtual Conjugate Coil Technology and Convolutional Neural Network[J]. Chinese Journal of Magnetic Resonance, 2020, 37(4): 407-421.

References

[1] ZHENG H R, WU Y, HE Q, et al. Fast and high-resolution magnetic resonance imaging on high field system[J]. Life Science Instruments, 2018, 16(Z1):29-44, 54.郑海荣, 吴垠, 贺强, 等. 基于高场磁共振的快速高分辨成像[J]. 生命科学仪器, 2018, 16(Z1):29-44, 54.
[2] MCGIBNEY G, SMITH M R, NICHOLS S T, et al. Quantitative-evaluation of several partial fourier reconstruction algorithms used in MRI[J]. Magn Reson Med, 1993, 30(1):51-59.
[3] PRUESSMANN K P, WEIGER M, SCHEIDEGGER M B, et al. SENSE:Sensitivity encoding for fast MRI[J]. Magn Reson Med, 1999, 42(5):952-962.
[4] GRISWOLD M A, JAKOB P M, HEIDEMANN R M, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA)[J]. Magn Reson Med, 2002, 47(6):1202-1210.
[5] SETSOMPOP K, GAGOSKI B A, POLIMENI J R, et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty[J]. Magn Reson Med. 2012, 67(5):1210-1224.
[6] LARKMAN D J, HAJNAL J V, HERLIHY A H, et al. Use of multicoil arrays for separation of signal from multiple slices simultaneously excited[J]. J Magn Reson Imaging, 2001, 13(2):313-317.
[7] BREUER F A, BLAIMER M, HEIDEMANN R M, et al. Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-slice imaging[J]. Magn Reson Med, 2005, 53(3):684-691.
[8] NUNES R G, HAJNAL J V, GOLAY X, et al. Simultaneous slice excitation and reconstruction for single shot EPI[C]//14th Annual Meeting of ISMRM, Seattle, Washington, USA. 2006:293.
[9] CHENG J, WANG H, YING L, et al. Model learning:Primal dual networks for fast MR imaging[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 13-17, 2019, Shenzhen, China. Springer, Cham, 2019:21-29.
[10] WANG H, CHENG J, JIA S, et al. Accelerating MR imaging via deep Chambolle-Pock network[C]. 201941st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 23-27, 2019, Berlin, Germany. IEEE, 2019:6818-6821.
[11] CHENG H T, WANG S S, KE Z W, et al. A deep recursive cascaded convolutional network for parallel MRI[J]. Chinese J Magn Reson, 2019, 36(4):437-445.程慧涛, 王珊珊, 柯子文, 等. 基于深度递归级联卷积神经网络的并行磁共振成像方法[J]. 波谱学杂志, 2019, 36(4):437-445.
[12] WANG H Z, ZHAO D, YANG L Q, et al. An approach for training data enrichment and batch labeling in AI+MRI aided diagnosis[J]. Chinese J Magn Reson, 2018, 35(4):447-456.汪红志, 赵地, 杨丽琴, 等. 基于AI+MRI的影像诊断的样本增广与批量标注方法[J]. 波谱学杂志, 2018, 35(4):447-456.
[13] AKCAKAYA M, MOELLER S, WEINGAERTNER S, et al. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction:Database-free deep learning for fast imaging[J]. Magn Reson Med, 2019, 81(1):439-453.
[14] EO T, JUN Y, KIM T, et al. KIKI-net:cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images[J]. Magn Reson Med, 2018, 80(5):2188-2201.
[15] BLAIMER M, BREUER F A, SEIBERLICH N, et al. Accelerated volumetric MRI with a SENSE/GRAPPA combination[J]. J Magn Reson Imaging, 2006, 24(2):444-450.
[16] ZHANG C, MOELLER S, WEINGAERTNER S, et al. Accelerated simultaneous multi-slice MRI using subject-specific convolutional neural networks[C]. Conference Record of the Asilomar Conference on Signals Systems and Computers, Pacific Grove, CA. 2018:1636-1640.
[17] BLAIMER M, GUTBERLET M, KELLMAN P, et al. Virtual coil concept for improved parallel MRI employing conjugate symmetric signals[J]. Magn Reson Med, 2009, 61(1):93-102.
[18] KINGMA D P, BA J. Adam:A method for stochastic optimization[C]. International Conference on Learning Representations (ICLR), May 7-9, 2015, San Diego, USA. arXiv preprint arXiv:1412.6980, 2014.
[19] SU S, LU N, JIA L, et al. High spatial resolution BOLD fMRI using simultaneous multislice excitation with echo-shifting gradient echo at 7 Tesla[J]. Magnc Reson Imaging, 2020, 66:86-92.
[20] MICKEVICIUS N J, PAULSON E S, TUGAN MUFTULER L, et al. Application of a k-space interpolating artificial neural network to in-plane accelerated simultaneous multislice imaging[OL].[2019-02-01]. https://ui.adsabs.harvard.edu/abs/2019arXiv190208589M