Compressed Sensing Reconstruction Improves Sensitivity of Variable Density Spiral fMRI

被引:28
|
作者
Holland, D. J. [1 ]
Liu, C. [2 ]
Song, X. [2 ]
Mazerolle, E. L. [2 ,3 ]
Stevens, M. T. [2 ,4 ]
Sederman, A. J. [1 ]
Gladden, L. F. [1 ]
D'Arcy, R. C. N. [2 ,3 ,5 ]
Bowen, C. V. [2 ,4 ,5 ,6 ]
Beyea, S. D. [2 ,4 ,5 ,6 ]
机构
[1] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge, England
[2] Natl Res Council Canada, Inst Biodiagnost Atlantic, Halifax, NS, Canada
[3] Dalhousie Univ, Dept Psychol & Neurosci, Halifax, NS, Canada
[4] Dalhousie Univ, Dept Phys, Halifax, NS B3H 3J5, Canada
[5] Dalhousie Univ, Dept Radiol, Halifax, NS, Canada
[6] Dalhousie Univ, Sch Biomed Engn, Halifax, NS, Canada
基金
加拿大自然科学与工程研究理事会; 英国工程与自然科学研究理事会;
关键词
fMRI; compressed sensing; variable-density; spiral; IMAGE COMPRESSION; HIGH-SPEED; MRI; RESOLUTION; ROBUST; OPTIMIZATION; ACQUISITION; REGISTRATION; TRAJECTORIES; PRINCIPLES;
D O I
10.1002/mrm.24621
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeFunctional MRI (fMRI) techniques that can provide excellent blood oxygen level dependent contrast, rapid whole brain imaging, and minimal spatial distortion are in demand. This study explored whether fMRI sensitivity can be improved through the use of compressed sensing (CS) reconstruction of variable density spiral fMRI. MethodsThree different CS-reconstructed 1-shot variable density spirals were explored (corresponding to 28%, 35%, and 46% under-sampling), and compared with conventional 1-shot and 2-shot Archimedean spirals acquired using matched echo time and volume repetition time. fMRI maps were reconstructed with or without CS MRI and sensitivity was compared using identically matched voxels. ResultsThe results demonstrated that an l(1)-norm based CS reconstruction only led to an increase in functional contrast when applied to 28% under-sampled data. A whole brain t-contrast map revealed that 2-shot uniformly sampled spiral and 28% under-sampled spiral data reconstructed with CS yield equivalent sensitivity, even with matched echo time and volume repetition time ConclusionVD spiral exhibits a useful operating range, in the region of 25-30% under-sampling, for which CS reconstruction can be used to increase the sensitivity of fMRI to brain activity. Using CS, VD acquisitions achieve the same sensitivity as 2-shot Archimedean acquisitions, but require only a single shot. Magn Reson Med 70:1634-1643, 2013. (c) 2013 Wiley Periodicals, Inc.
引用
收藏
页码:1634 / 1643
页数:10
相关论文
共 50 条
  • [41] A Decentralized Reconstruction Algorithm for Distributed Compressed Sensing
    Wenbo Xu
    Yupeng Cui
    Zhilin Li
    Jiaru Lin
    Wireless Personal Communications, 2017, 96 : 6175 - 6182
  • [42] Iteratively Refined Nonlocal Total Variation Regularization for Parallel Variable Density Spiral Imaging Reconstruction
    Fang, Sheng
    Wu, Wenchuan
    Guo, Hua
    2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC), 2013, : 1382 - 1387
  • [43] A new signal reconstruction method in compressed sensing
    Chen, Xuan
    COMPUTERS & ELECTRICAL ENGINEERING, 2018, 69 : 865 - 880
  • [44] Compressed sensing by collaborative reconstruction on overcomplete dictionary
    Lin, Leping
    Liu, Fang
    Jiao, Licheng
    Signal Processing, 2014, 103 : 92 - 102
  • [45] Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging
    Zibetti, Marcelo Victor Wust
    Helou, Elias Salomao
    Regatte, Ravinder R.
    Herman, Gabor T.
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2019, 5 (01) : 109 - 119
  • [46] Signal Reconstruction Based on Block Compressed Sensing
    Sun, Liqing
    Wen, Xianbin
    Lei, Ming
    Xu, Haixia
    Zhu, Junxue
    Wei, Yali
    ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT II, 2011, 7003 : 312 - 319
  • [47] Compressed sensing reconstruction using expectation propagation
    Braunstein, Alfredo
    Muntoni, Anna Paola
    Pagnani, Andrea
    Pieropan, Mirko
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2020, 53 (18)
  • [48] Fixed point simulation of compressed sensing and reconstruction
    Gupta, Pravir Singh
    Choi, Gwan Seong
    COMPUTATIONAL IMAGING IV, 2019, 10990
  • [49] Sensing Matrix Sensitivity to Random Gaussian Perturbations in Compressed Sensing
    Lavrenko, Anastasia
    Roemer, Florian
    Del Galdo, Giovanni
    Thomae, Reiner S.
    2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2018, : 583 - 587
  • [50] Two-Part Reconstruction in Compressed Sensing
    Ma, Yanting
    Baron, Dror
    Needell, Deanna
    2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2013, : 1041 - 1044