Computer Science ›› 2019, Vol. 46 ›› Issue (1): 36-44.doi: 10.11896/j.issn.1002-137X.2019.01.006

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Application Status and Development Trends of Cardiac Magnetic Resonance Fast Imaging Based on Compressed Sensing Theory

HENG Yang1, CHEN Feng2, XU Jian-feng3, TANG Min1,4,5   

  1. (School of Electronics and Information Engineering,Nantong University,Nantong,Jiangsu 226007,China)1
    (School of Electrical Engineering,Nantong University,Nantong,Jiangsu 226007,China)2
    (Department of Medical Imaging,Affiliated Hospital of Nantong University,Nantong,Jiangsu 226007,China)3
    (Tongke School of Microelectronics,Nantong,Jiangsu 226007,China)4
    (Nantong University-Nantong Joint Research Center for Intelligent Information Technology,Nantong,Jiangsu 226007,China)5
  • Received:2018-03-09 Online:2019-01-15 Published:2019-02-25

Abstract: Cardiac Magnetic Resonance (CMR) has several shortcomings in practical application,such as slow imaging speed and inevitable artifacts.Compressed Sensing (CS) is applied to CMR to make full use of the redundancy of K space information,and the images are reconstructed from partial K space data to reduce artifacts and ensure image accuracy.This paper summarized a review according to the domestic and foreign literatures published in recent three years.Firstly,this paper described the current situation of CMR,the commonly used sequences,sampling mask and the compressed sensing theory,respectively.Then,it provided the latest fruits and applications of CMR with an introduction to objective quantitative indices and research progress of the authors in the CS-CMR field.Finally,it concluded the shortcomings of current researches and analyzed the further research trends.

Key words: Cardiac magnetic resonance, Compressed sensing, Fast imaging techniques

CLC Number: 

  • TP391
[1]LI S T,WEI D.A Survey on Compressive Sensing [J].Acta Automatica Sinica,2009,35(11):1369-1377.(in Chinese)<br /> 李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11):1369-1377.<br /> [2]YIN H P,LIU Z D,CHAI Y,et al.Survey of Compressed Sensing [J].Control & Decision,2013,28(10):1441-1445.(in Chinese)<br /> 尹宏鹏,刘兆栋,柴毅,等.压缩感知综述[J].控制与决策,2013,28(10):1441-1445.<br /> [3]JIAO P F,LI L,ZHAO J.New Advances of Compressed Sensing in Medical Image Reconstruction [J].CT Theory and Applications,2012,21(1):133-147.(in Chinese)<br /> 焦鹏飞,李亮,赵骥.压缩感知在医学图像重建中的最新进展[J].CT理论与应用研究,2012,21(1):133-147.<br /> [4]GEETHANATH S,REDDY R,KONAR A S,et al.Compressed Sensing MRI:a review [J].Critical Reviews<sup>TM</sup> in Biomedical Engineering,2013,41(3):213-235.<br /> [5]DONOHO D.Compressed Sensing [J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.<br /> [6]LUSTIG M,DONOHO D,PAULY J M.Sparse MRI:the Application of Compressed Sensing for Rapid MR Imaging [J].Magnetic Resonance in Medicine,2007,58(6):l182-1195.<br /> [7]TANG Y,YAMASHITA Y,NAMIMOTO T,et al.Characterization of Focal Liver Lesions with Half-fourier Acquisition Single-Shot Turbo-Spin-Echo (HASTE) and Inversion Recovery(IR)-HASTE Sequences [J].Journal of Magnetic Resonance Imaging,1998,8(2):438-445.<br /> [8]BIGLAND J D,RADJENOVIC A,RIDGWAY J P.Cardiovascular Magnetic Resonance Physics for Clinicians:Part II [J].Journal of Cardiovascular Magnetic Resonance,2012,20(14):66.<br /> [9]COELHO-FILHO O R,RICKERS C,KWONG R Y,et al.Myocardial Perfusion Imaging [J].Radiology,2013,266(3):701-715.<br /> [10]GOMORI J M,HOLLAND G A,GROSSMAN R I,et al.Fat Suppression by Section-select Gradient Reversal on Spin-Echo MR Imaging,Work in progress [J].Radiology,1988,168(2):493-495.<br /> [11]WINKELMANN S,SCHAEFFTER T,KOEHLER T,et al.An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI [J].IEEE Transactions on Medical Imaging,2007,26(1):68-76.<br /> [12]WANG J.Coil Compression for Accelerated Imaging with Radial Acquisition [D].Hangzhou:Zhejiang University,2016.(in Chinese)<br /> 王晶.磁共振线圈通道压缩算法在径向快速采集中的应用[D].杭州:浙江大学,2016.<br /> [13]WUNDRAK S,PAUL J,ULRICI J,et al.A Small Surrogate for the Golden Angle in Time-Resolved Radial MRI Based on Ge-neralized Fibonacci Sequences [J].IEEE Transactions on Medical Imaging,2015,34(6):1262-1269.<br /> [14]WUNDRAK S,PAUL J,ULRICI J,et al.Golden Ratio Sparse MRI Using Tiny Golden Angles [J].Magnetic Resonance in Medicine,2016,75(6):2372-2378.<br /> [15]DONOHO D.For Most Large Underdetermined Systems of Linear Equations,the Minimal L1-norm Solution is Also the Sparsest Solution [J].Communications on Pure and Applied Mathematics,2006,59(6):797-829.<br /> [16]CANDES E,TAO T.Decoding by Linear Programming [J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.<br /> [17]BARANIUK R G.Compressive Sensing [J].IEEE Signal Processing Magazine,2007,24(4):118-121.<br /> [18]XU Z Q.Compressed Sensing:A Survey [J].Science China:Math,2012,42(9):865-877.(in Chinese)<br /> 许志强.压缩感知[J].中国科学:数学,2012,42(9):865-877.<br /> [19]SHAO W Z,WEI Z H.Advances and Perspectives on Compressed Sensing Theory [J].Journal of Image & Graphics,2012,17(1):4-15.(in Chinese)<br /> 邵文泽,韦志辉.压缩感知基本理论:回顾与展望[J].中国图像图形学报,2012,17(1):4-15.<br /> [20]FANG H,YANG H R.Greedy Algorithms and Compressed Sensing [J].Acta Automatica Sinica,2011,37(12):1413-1421.(in Chinese)<br /> 方红,杨海蓉.贪婪算法与压缩感知理论[J].自动化学报,201l,37(12):1413-l421.<br /> [21]LI S,MA C W,LI Y,et al.Survey on Reconstruction Algorithm Based on Compressive Sensing [J].Infrared & Laser Enginee-ring,2013,42(s1):225-232.(in Chinese)<br /> 李珅,马彩文,李艳,等.压缩感知重构算法综述[J].红外与激光工程,2013,42(s1):225-232.<br /> [22]SUN L H,YANG Z.Compressed Sensing of Noisy Speech Signal Based on Adaptive Basis Pursuit De-noising [J].Journal of Nanjing University of Posts & Telecommunications,2011,31(5):l-6.(in Chinese)<br /> 孙林慧,杨震.基于自适应基追踪去噪的含噪语音压缩感知[J].南京邮电大学学报(自然科学版),201l,31(5):1-6.<br /> [23]LV C L.Rapid Dynamic Magnetic Resonance Imaging Based on Compressed Sensing [D].Jinan:Shandong University,2012.(in Chinese)<br /> 吕成林.基于压缩感知的快速动态磁共振成像[D].济南:山东大学,2012.<br /> [24]ZAMANI J,MOGHADDAM A N,RAD H S.Compressed Sensing Cardiac MRI Exploiting Spatio-Temporal Sparsity [J].Journal of Cardiovascular Magnetic Resonance,2013,15(1):1-3.<br /> [25]USMAN M,ATKINSON D,ODILLE F,et al.Motion Corrected Compressed Sensing for Free-breathing Dynamic Cardiac MRI [J].Magnetic Resonance in Medicine,2013,70(2):504-516.<br /> [26]OTAZO R,LI F,CHANDARANA H,et al.Combination of Compressed Sensing and Parallel Imaging for Highly-Accele-rated Dynamic MRI[C]//International Symposium onBiomedi-cal Imaging.Barcelona:IEEE Press,2012:980-983.<br /> [27]OTAZO R,KIM D L,SODICKSON D K.Combination of Compressed Sensing and Parallel Imaging for Highly Accelerated First-pass Cardiac Perfusion MRI [J].Magnetic Resonance in Medicine,2010,64(3):767-776.<br /> [28]CHEN Z W.Real Time Cardiac Magnetic Resonance Imaging Based on Compressed Sensing [D].Shenyang:North Eastern University,2015.(in Chinese)<br /> 陈兆文.基于压缩感知的心脏实时磁共振成像[D].沈阳:东北大学,2015.<br /> [29]ZHANG H,HU L W,WANG Q,et al.Application of Radial K-pace Acquisition in Fetal Cardiac MRI [J].Chinese Journal of Medical Physics,2016,33(6):580-583.(in Chinese)<br /> 张弘,胡立伟,王谦,等.径向K空间采样技术在胎儿心脏磁共振成像中的应用[J].中国医学物理学杂志,2016,33(6):580-583.<br /> [30]KIDO T,NAKAMURA M,WATANABE K,et al.Compressed Sensing Real-time Cine Cardiovascular Magnetic Resonance:Accurate Assessment of Left Ventricular Function in A Single-Breath-Hold [J].Journal of Cardiovascular Magnetic Resonance,2016,18(1):50.<br /> [31]OTAZO R,CANDES E,SODICKSON D K.Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components [J].Magnetic Resonance in Medicine,2015,73(3):1125-1136.<br /> [32]ROOHI S F,ZONOOBI D,KASSIM A A,et al.Dynamic MRI Reconstruction Using Low Rank plus Sparse Tensor Decomposition[C]//International Conference on Image Processing.Phoenix:IEEE Press,2016:1769-1773.<br /> [33]ROOHI S F,ZONOOBI D,KASSIM A A,et al.Multi-dimensional Low Rank plus Sparse Decomposition for Reconstruction of Under-Sampled Dynamic MRI [J].Pattern Recognition,2017(63):667-679.<br /> [34]CHEN C W,ZHU J.Compressed Sensing Image Reconstruction Based on Low-rank and Sparse Prior [J].Applications Research of Computers,2017,34(3):949-952.(in Chinese)<br /> 陈长伟,朱俊.基于低秩和稀疏性先验知识的压缩感知图像重构[J].计算机应用研究,2017,34(3):949-952.<br /> [35]CHEN S J,YANG X M,LV X S.Fast Dynamic MRI Reconstruction Based on Separation via Sparse Pluse Low-rank Prior [J].Applications Research of Computers,2016,33(10):3196-3200.(in Chinese)<br /> 陈思吉,杨晓梅,吕雪霜.基于稀疏和低秩先验分离的快速动态MRI重建[J].计算机应用研究,2016,33(10):3196-3200.<br /> [36]FRANK O,LUSTIG M.Beyond Low Rank+Sparse:Multiscale Low Rank Matrix Decomposition [J].IEEE Journal of Selected Topics in Signal Processing,2016,10(4):672-687.<br /> [37]NAKARMI U,WANG Y H,LYU J Y,et al.A Kernel-based Low-rank (KLR) Model for Low-dimensional Manifold Reco-very in Highly Accelerated Dynamic MRI [J].IEEE Transactions on Medical Imaging,2017,36(11):2297-2307.<br /> [38]LEVINEE,STEVENSK,BEAULIEU C,et al.Accelerated Three-Dimensional Multispectral MRI with Robust Principal Component Analysis for Separation of On- and Off-Resonance Signals [J].Magnetic Resonance in Medicine,2018,79(3):1495-1505.<br /> [39]VALVANO G,MARTINI N,HUBER A,et al.Accelerating 4D Flow MRI by Exploiting Low-Rank Matrix Structure and Ha-damard Sparsity [J].Magnetic Resonance in Medicine,2017,78(4):1330-1341.<br /> [40]LUGAUER F,NICKEL D,WETZL J,et al.Accelerating Multi-Echo Water-Fat MRI with a Joint Locally Low-Rank and Spatial Sparsity-Promoting Reconstruction [J].Magnetic Resonance Materials in Physics,Biology and Medicine,2017,30(2):189-202.<br /> [41]RAVISHANKAR S,MOORE B,NADAKUDITI R,et al.Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging [J].IEEE Transactions on Medical Imaging,2017,36(5):1116-1128.<br /> [42]CHEN J B,LIU S Y,HUANG M.Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction [J].Journal of Healthcare Engineering,2017(12):1-9.<br /> [43]LIU S J,CAO J X,LIU H Q,et al.MRI Reconstruction Using a Joint Constraint in Patch-Based Total Variational Framework [J].Journal of Visual Communication and Image Representation,2017,46:150-164.<br /> [44]LIU R W,SHI L,YU S C,et al.Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints [J].Sensors,2017,17(3):509.<br /> [45]CAO P,ZHU X C,TANG S Y,et al.Shuffled Magnetization-Prepared Multicontrast Rapid Gradient-Echo Imaging [J].Magnetic Resonance in Medicine,2018,79(1):62-70.<br /> [46]LIU Q G,WANG S S,LIANG D.Sparse and Dense Hybrid Representation via Subspace Modeling for Dynamic MRI [J].Computerized Medical Imaging and Graphics,2017,56:24-37.<br /> [47]ZHANG X,XIE G,FENG X,et al.Accelerating PS Model-Based Dynamic Cardiac MRI using Compressed Sensing [J].Magnetic Resonance Imaging,2016,34(2):81-90.<br /> [48]TOLOUEE A,ALIREZAIE J,BABYN P.Motion Compensated Data Decomposition Algorithm to Accelerate Dynamic Cardiac MRI [J].Magnetic Resonance Materials in Physics,Biology and Medicine,2017,30(1):1-15.<br /> [49]TOLOUEE A,ALIREZAIE J,BABYN P.Nonrigid Motion Compensation in Compressed Sensing Reconstruction of Cardiac Cine MRI [J].Magnetic Resonance Imaging,2018,46:114-120.<br /> [50]SCHLEMPER J,CABALLERO J,HAJNAL J V,et al.A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction [C]//Information Processing in Medical Imaging.Honolulu,USA,2017:647-658.<br /> [51]WANG S,SU Z,YING L,et al.Accelerating Magnetic Resonance Imaging via Deep Learning[C]//International Symposium on Biomedical Imaging.Prague,Czech Republic:IEEE Press,2016:514-517.<br /> [52]DAR S,CUKUR T.A Transfer-Learning Approach for Acceler-ated MRI using Deep Neural Networks[J].arXiv:1710.02615.<br /> [53]LEE D,YOO J,YE J C.Deep Residual Learning for Compressed Sensing MRI[C]//International Symposium on Biomedical Imaging.Melbourne,Australia:IEEE Press,2017:15-18.<br /> [54]SCHLEMPER J,CABALLERO J,HAJNAL J V,et al.A Deep Cascade of Convolutional Neural Networks for Dynamic MR Ima-ge Reconstruction [J].IEEE Transactions on Medical Imaging,2017,37(2):491-503.<br /> [55]CHEN Q,SCHLEMPER J,CABALLERO J,et al.Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction[J].arXiv:1712.01751.<br /> [56]SHITRIT O,RAVIV T R.Accelerated Magnetic Resonance Imaging by Adversarial Neural Network[M]//Cardoso M,et al.,eds.Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2017:30-38.<br /> [57]QUAN T M,NGUYENDUC T,JEONG W K.Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks[J].arXiv:1709.00753v1.<br /> [58]YANG G,YU S,DONG H,et al.DAGAN:Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction [J].IEEE Transactions on Medical Imaging,2018,pp(99):1.<br /> [59]YU S M,DONG H,YANG G,et al.Deep De-Aliasing for Fast Compressive Sensing MRI[J].arXiv:1705.07137.<br /> [60]MARDANI M,GONG E,JOSEPH Y,et al.Deep Generative Adversarial Networks for Compressed Sensing (GANCS) Automates MRI[J].arXiv:1706.00051.<br /> [61]YANG Y,SUN Y,LI H,et al.Deep ADMM-net for Compressive Sensing MRI[C]//Advances in Neural Information Processing Systems.Barcelona,Spain:MIT Press,2016:10-18.<br /> [62]YANG Y,SUN Y,LI H,et al.A Deep Learning Approach for Compressive Sensing MRI[C]//Advances in Neural Information Processing Systems.Barcelona,Spain:MIT Press,2016:10-18.<br /> [63]CHANG M H,KIM H P,LEE S M,et al.Deep Learning for Undersampled MRI Reconstruction[J].arXiv:1709.02576.<br /> [64]HAMMERNIK K,KLATZER T,KOBLER E,et al.Learning a Variational Network for Reconstruction of Accelerated MRI Data[J].arXiv:1704.00447.
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