A computationally efficient method for reconstructing sequences of MR images from undersampled k-space data

被引:5
|
作者
Zonoobi, Dornoosh [1 ]
Kassim, Ashraf A. [1 ]
机构
[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore
关键词
Dynamic MRI reconstruction; Iterative thresholding method; Priori-knowledge;
D O I
10.1016/j.media.2014.04.010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a Compressive Sensing based approach to the problem of real-time reconstruction of MR image sequences. Our proposed method is able to extract useful priori information and incorporate it into a modified iterative thresholding algorithm for fast casual reconstruction of MR images from highly undersampled k-space data. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, while having a lower computational complexity and memory requirements compared to the other state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:857 / 865
页数:9
相关论文
共 50 条
  • [21] Correction of MR k-space data corrupted by spike noise
    Kao, YH
    MacFall, JR
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2000, 19 (07) : 671 - 680
  • [22] An efficient MR image reconstruction method for arbitrary K-space trajectories without density compensation
    Song, Jiayu
    Liu, Qing H.
    2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 2516 - +
  • [23] Reconstruction of MR images from data acquired on an arbitrary k-space trajectory using the same-image weight
    Qian, YX
    Lin, JR
    Jin, DQ
    MAGNETIC RESONANCE IN MEDICINE, 2002, 48 (02) : 306 - 311
  • [24] Image Reconstruction in K-Space from MR Data Encoded with Ambiguous Gradient Fields
    Schultz, Gerrit
    Gallichan, Daniel
    Weber, Hans
    Witschey, Walter R. T.
    Honal, Matthias
    Hennig, Juergen
    Zaitsev, Maxim
    MAGNETIC RESONANCE IN MEDICINE, 2015, 73 (02) : 857 - 864
  • [25] Signal-to-Noise Ratio Enhancement of MR Images With Variable Gains and K-Space Splicing Method
    Wu, Lin
    Zheng, Liqin
    Zhang, Shuang
    He, Chunqiao
    Liu, Hang
    Yang, Tianjiao
    Yu, Jie
    Zhang, Tao
    IEEE SENSORS JOURNAL, 2024, 24 (02) : 2099 - 2107
  • [26] Penalized-likelihood estimation of diffusion tensors from K-space MR data
    Yendiki, Anastasia
    2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3, 2007, : 924 - 927
  • [27] Compressive Manifold Learning: Estimating One-Dimensional Respiratory Motion Directly from Undersampled k-Space Data
    Usman, Muhammad
    Vaillant, Ghislain
    Atkinson, David
    Schaeffter, Tobias
    Prieto, Claudia
    MAGNETIC RESONANCE IN MEDICINE, 2014, 72 (04) : 1130 - 1140
  • [28] Increasing of the MR Imaging Spatial Resolution by Data Estimation in k-space
    Naguliak, O. O.
    Netreba, A., V
    Radchenko, S. P.
    Sudakov, O. O.
    2017 IEEE 37TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO), 2017, : 310 - 315
  • [29] Inductive measurement and encoding of k-space trajectories in MR raw data
    Jan Ole Pedersen
    Christian G. Hanson
    Rong Xue
    Lars G. Hanson
    Magnetic Resonance Materials in Physics, Biology and Medicine, 2019, 32 : 655 - 667
  • [30] Inductive measurement and encoding of k-space trajectories in MR raw data
    Pedersen, Jan Ole
    Hanson, Christian G.
    Xue, Rong
    Hanson, Lars G.
    MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 2019, 32 (06) : 655 - 667