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Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method |
| Lin Chen(陈琳)1, Zhi-Wei Zheng(郑志伟)1, Li-Jun Bao(包立君)1, Jin-Sheng Fang(方金生)1, Tian-He Yang(杨天和)2, Shu-Hui Cai(蔡淑惠)1, Cong-Bo Cai(蔡聪波)3 |
1 Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China;
2 Magnetic Resonance Center, Zhongshan Hospital, Medical College of Xiamen University, Xiamen 361004, China;
3 Department of Communication Engineering, Xiamen University, Xiamen 361005, China |
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Abstract An ill-posed inverse problem in quantitative susceptibility mapping (QSM) is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman (WTVSB) is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the L1-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division (TKD), morphology enabled dipole inversion (MEDI), total variation using the split Bregman (TVSB) method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.
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Received: 23 November 2017
Revised: 27 April 2018
Accepted manuscript online:
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PACS:
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87.19.lf
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(MRI: anatomic, functional, spectral, diffusion)
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33.15.Kr
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(Electric and magnetic moments (and derivatives), polarizability, and magnetic susceptibility)
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02.30.Zz
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(Inverse problems)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11474236, 81671674, and 11775184) and the Science and Technology Project of Fujian Province, China (Grant No. 2016Y0078). |
Corresponding Authors:
Shu-Hui Cai, Cong-Bo Cai
E-mail: shcai@xmu.edu.cn;cbcai@xmu.edu.cn
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Cite this article:
Lin Chen(陈琳), Zhi-Wei Zheng(郑志伟), Li-Jun Bao(包立君), Jin-Sheng Fang(方金生), Tian-He Yang(杨天和), Shu-Hui Cai(蔡淑惠), Cong-Bo Cai(蔡聪波) Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method 2018 Chin. Phys. B 27 088701
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| [1] |
Jiang B, Luo F, Ding Y, Sun P, Zhang X, Ling J, Li C, Mao X, Yang D, Tang C and Liu M 2013 Anal. Chem. 85 2523
|
| [2] |
Zhong K, Ernst T, Buchthal S, Speck O, Anderson L and Chang L 2011 NeuroImage 55 1068
|
| [3] |
Ni H, Zhou P, Zeng P, Huang X, Liu H and Ning X 2015 Chin. Phys. B 24 070502
|
| [4] |
Li J, Chang S, Liu T, Wang Q, Cui D, Chen X, Jin M, Wang B, Pei M, Wisnieff C, Spincecmaille P, Zhang M and Wang Y 2012 Magn. Reson. Med. 68 1563
|
| [5] |
Wang Y and Liu T 2015 Magn. Reson. Med. 73 82
|
| [6] |
Langkammer C, Schweser F, Shmueli K, Kames C, Li X, Li G, Milovic C, Kim J, Wei H, Bredies K, Buch S, Guo Y, Liu Z, Meineke J, Rauscher A, Marques J and Bilgic B 2017 Magn. Reson. Med. doi:10.1002/mrm
|
| [7] |
Liu T, Surapaneni K, Lou M, Cheng L, Spincemaille P and Wang Y 2012 Radiology 262 269
|
| [8] |
Langkammer C, Schweser F, Krebs N, Deistung A, Goessler W, Scheurer E, Sommer K, Reishofer G, Yen K and Fazekas F 2012 NeuroImage 62 1593
|
| [9] |
Murakami Y, Kakeda S, Watanabe K, Ueda I, Ogasawara A, Moriya J, Ide S, Futatsuya K, Sato T and Okada K 2015 Am. J. Neuroradiol. 36 1102
|
| [10] |
van Bergen J, Hua J, Unschuld P, Lim I, Jones C, Margolis R, Ross C, van Zijl P and Li X 2016 Am. J. Neuroradiol. 37 789
|
| [11] |
Chen W, Gauthier S A, Gupta A, Comunale J, Liu T, Wang S, Pei M, Pitt D and Wang Y 2014 Radiology 271 183
|
| [12] |
Marques J and Bowtell R 2005 Concepts Magn. Reson. Part. B 25 65
|
| [13] |
Liu T, Spincemaille P, de Rochefort L, Kressler B and Wang Y 2009 Magn. Reson. Med. 61 196
|
| [14] |
Shmueli K, de Zwart J A, van Gelderen P, Li T Q, Dodd S J and Duyn J H 2009 Magn. Reson. Med. 62 1510
|
| [15] |
Bao L, Li X, Cai C, Chen Z and van Zijl P C 2016 IEEE Trans. Med. Imaging 35 2040
|
| [16] |
Bilgic B, Pfefferbaum A, Rohlfing T, Sullivan E V and Adalsteinsson E 2012 NeuroImage 59 2625
|
| [17] |
de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Wu J and Wang Y 2010 Magn. Reson. Med. 63 194
|
| [18] |
Liu J, Liu T, de Rochefort L, Ledoux J, Khalidov I, Chen W, Tsiouris A J, Wisnieff C, Spincemaille P and Prince M R 2012 NeuroImage 59 2560
|
| [19] |
Liu T, Liu J, de Rochefort L, Spincemaille P, Khalidov I, Ledoux J R and Wang Y 2011 Magn. Reson. Med. 66 777
|
| [20] |
Schweser F, Sommer K, Deistung A and Reichenbach J R 2012 NeuroImage 62 2083
|
| [21] |
Wu B, Li W, Guidon A and Liu C 2012 Magn. Reson. Med. 67 137
|
| [22] |
Liu T, Xu W, Spincemaille P, Avestimehr A S and Wang Y 2012 IEEE Trans. Med. Imaging 31 816
|
| [23] |
Bilgic B, Fan A P, Polimeni J R, Cauley S F, Bianciardi M, Adalsteinsson E, Wald L L and Setsompop K 2014 Magn. Reson. Med. 72 1444
|
| [24] |
Goldstein T and Osher S 2009 SIAM J. Imaging Sci. 2 323
|
| [25] |
Wang S, Liu T, Chen W, Spincemaille P, Wisnieff C, Tsiouris A J, Zhu W, Pan C, Zhao L and Wang Y 2013 IEEE Trans. Biomed. Eng. 60 3441
|
| [26] |
Vogel C R and Oman M E 1996 SIAM J. Sci. Comput. 17 227
|
| [27] |
Li W, Wang N, Yu F, Han H, Cao W, Romero R, Tantiwongkosi B, Duong T Q and Liu C 2015 NeuroImage 108 111
|
| [28] |
Liu T, Wisnieff C, Lou M, Chen W, Spincemaille P and Wang Y 2013 Magn. Reson. Med. 69 467
|
| [29] |
Cusack R and Papadakis N 2002 NeuroImage 16 754
|
| [30] |
Liu T, Khalidov I, de Rochefort L, Spincemaille P, Liu J, Tsiouris A J and Wang Y 2011 NMR Biomed. 24 1129
|
| [31] |
Li W, Wu B and Liu C 2011 NeuroImage 55 1645
|
| [32] |
Schweser F, Deistung A, Lehr B W and Reichenbach J R 2011 NeuroImage 54 2789
|
| [33] |
Fang S, Wu W, Ying K and Guo H 2013 Acta Phys. Sin. 62 048702 (in Chinese)
|
| [34] |
Mao B, Chen X, Xiao D, Fan S, Teng Y and Kang Y 2014 Acta Phys. Sin. 63 138701 (in Chinese)
|
| [35] |
Liu C 2010 Magn. Reson. Med. 63 1471
|
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