Fast magnetic resonance imaging (MRI) has been attracting more and more research interests in recent years. With the emergence of big data and development of advanced deep learning algorithms, neural network has become a common and effective tool for image reconstruction in fast MRI. One main challenge to the deep learning-based methods for fast MRI reconstruction is the trade-off between the network performance and the network capacity. Few previous studies have used the deep learning-based methods in parallel imaging. In this work, a deep recursive cascaded convolutional network (DRCCN) architecture was designed for parallel MRI, with reduced number of network parameters while maintaining a satisfactory performance. The experimental results demonstrated that, compared to the classical methods, image reconstruction with the well-trained DRCCN networks were more accurate and less time consuming.
CHENG Hui-tao
,
WANG Shan-shan
,
KE Zi-wen
,
JIA Sen
,
CHENG Jing
,
QIU Zhi-lang
,
ZHENG Hai-rong
,
LIANG Dong
. A Deep Recursive Cascaded Convolutional Network for Parallel MRI[J]. Chinese Journal of Magnetic Resonance, 2019
, 36(4)
: 437
-445
.
DOI: 10.11938/cjmr20192721
[1] LUSTIG M, DONOHO D L, SANTOS J M, et al. CompressedSensing MRI[J]. IEEE Signal Proc Mag, 2008, 25(2):72-82.
[2] 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.
[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] LUSTIG M, PAULY J M. SPIRiT:iterative self-consistent parallel imaging reconstruction from arbitrary k-space[J]. Magn Reson Med, 2010, 64(2):457-471.
[5] CHAI Q H, SU G Q, NIE S D. Compressive sensing low-field MRI reconstruction with dual-tree wavelet transform and wavelet tree sparsity[J]. Chinese J Magn Reson, 2018, 35(4):486-497. 柴青焕, 苏冠群, 聂生东. 双树小波变换与小波树稀疏联合的低场CS-MRI算法[J]. 波谱学杂志, 2018, 35(4):486-497.
[6] LIANG D, WANG H F, CHANG Y C, et al. Sensitivity encoding reconstruction with nonlocal total variation regularization[J]. Magn Reson Med, 2011, 65(5):1384-1392.
[7] LIU Q G, WANG S S, YING L, et al. Adaptive dictionary learning in sparse gradient domain for image recovery[J]. IEEE T Image Process, 2013, 22(12):4652-4663.
[8] SONG Y, XIE H B, YANG G. Dictionary learning with segmentation for compressed-sensing magnetic resonance imaging[J]. Chinese J Magn Reson, 2016, 33(4):559-569. 宋阳, 谢海滨, 杨光. 用于压缩感知磁共振成像的分割字典学习算法[J]. 波谱学杂志, 2016, 33(4):559-569.
[9] JASPAN O N, FLEYSHER R, LIPTON M L. Compressed sensing MRI:a review of the clinical literature[J]. Br J Radiol, 2015, 88(1056):20150487.
[10] HOLLINGSWORTH K G. Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction[J]. Phys Med Biol, 2015, 60(21):R297.
[11] WANG S S, SU Z H, YING L, et al. Accelerating magnetic resonance imaging via deep learning[C]//IEEE:2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016:514-517.
[12] YANG Y, SUN J, LI H B, et al. Deep ADMM-Net for compressive sensing MRI[C]//LEE D D, SUGIYAMA M, LUXBURG U V, et al. Advances in neural information processing systems 29, 2016:10-18.
[13] HAMMERNIK K, KLATZER T, KOBLER E, et al. Learning a variational network for reconstruction of accelerated MRI data[J]. Magn Reson Med, 2018, 79(6):3055-3071.
[14] KNOLL F, HAMMERNIK K, KOBLER E, et al. Assessment of the generalization of learned image reconstruction and the potential for transfer learning[J]. Magn Reson Med, 2019, 81(1):116-128.
[15] SCHLEMPER J, CABALLERO J, HAJNAL J V, et al. A deep cascade of convolutional neural networks for dynamic MR image reconstruction[J]. IEEE T Med Imaging, 2018, 37(2):491-503.
[16] ZHU B, LIU J Z, CAULEY S F, et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 2018, 555(7697):487-492.
[17] HAN Y, YOO J, KIM H H, et al. Deep learning with domain adaptation for accelerated projection-reconstruction MR[J]. Magn Reson Med, 2018, 80(3):1189-1205.
