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Data acquisition time is a bottle neck for increasing imaging speed of magnetic resonance imaging. To solve the problem, a new fast magnetic resonance imaging method based on variable-density spiral acquisition and Bregman iterative reconstruction is proposed in this paper, under the framework of compressed sensing. The proposed method increases the acquisition speed by data undersampling. The resulting undersampling aliasing artifact is removed by utilizing the intrinsic property of variable-density spiral and Bregman iterative recosntruction. The proposed method is validated by both phantom experiemnt and in vivo experiment. The experimental results demonstrate that the proposed method can effectively remove aliasing artifact from data undersampling, and achieve an image with well-preserved image structure information. Therefore this method can be used for reducing data acquisition time.
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Keywords:
- magnetic resonance imaging /
- variable-density spiral /
- total variation /
- bregman iteration
[1] Candés E J, Romberg J, Tao T 2006 IEEE Tran. Inform. Theory 52 489
[2] Donoho D L 2006 IEEE Tran. Inform. Theory 52 1289
[3] Lustig M, Donoho D, Pauly J M 2007 Magn Reson. Med. 58 1182
[4] Lustig M, Donoho D L, Santos J M, Pauly J M 2008 IEEE Signal Proc. Mag. 25 72
[5] Kim D, Adalsteinsson E, Spielman D M 2003 Magnet. Reson. Med. 50 214
[6] Tsai C M, Nishimura D G 2000 Magnet. Reson. Med. 43 452
[7] Lee J H, Hargreaves B A, Hu B S, Nishimura D G 2003 Magnet. Reson. Med. 50 1276
[8] Lu G, Liu M L, Li L Y, Ye C H 2002 Chin. Phys. Lett. 19 1385
[9] Bensamoun S F, Glaser K J, Ringleb S I, Chen Q, Ehman R L, An K N 2008 J. Magn. Reson. Imaging. 27 1083
[10] Wang H Z, Xu L F, Yu J, Huang Q M, Wang X Y, Lu L 2010 Acta Phys. Sin. 59 7463 (in Chinese) [汪红志, 许凌峰, 俞捷, 黄清明, 王晓琰, 陆伦 2010 59 7463]
[11] Rudin L, Osher S, Fatemi E 1992 Physica D 60 259
[12] Chan T, Esedoglu S, Park F, Yip A 2006 Mathematical Models of Computer Vision: the Handbook (Boston, MA: Springer) p176
[13] Block K T, Uecker M, Frahm J 2007 Magnet. Reson. Med. 57 1086
[14] Osher S, Burger M, Goldfarb D, Xu J, Yin W 2005 Multiscale Model. Sim. 4 460
[15] Sha L, Guo H, Song A W 2003 J. Magnet. Reson. 162 250
[16] Jackson J I, Meyer C H, Nishimura D G, Macovski A 1991 IEEE Trans. Med. Imaging. 10 473
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[1] Candés E J, Romberg J, Tao T 2006 IEEE Tran. Inform. Theory 52 489
[2] Donoho D L 2006 IEEE Tran. Inform. Theory 52 1289
[3] Lustig M, Donoho D, Pauly J M 2007 Magn Reson. Med. 58 1182
[4] Lustig M, Donoho D L, Santos J M, Pauly J M 2008 IEEE Signal Proc. Mag. 25 72
[5] Kim D, Adalsteinsson E, Spielman D M 2003 Magnet. Reson. Med. 50 214
[6] Tsai C M, Nishimura D G 2000 Magnet. Reson. Med. 43 452
[7] Lee J H, Hargreaves B A, Hu B S, Nishimura D G 2003 Magnet. Reson. Med. 50 1276
[8] Lu G, Liu M L, Li L Y, Ye C H 2002 Chin. Phys. Lett. 19 1385
[9] Bensamoun S F, Glaser K J, Ringleb S I, Chen Q, Ehman R L, An K N 2008 J. Magn. Reson. Imaging. 27 1083
[10] Wang H Z, Xu L F, Yu J, Huang Q M, Wang X Y, Lu L 2010 Acta Phys. Sin. 59 7463 (in Chinese) [汪红志, 许凌峰, 俞捷, 黄清明, 王晓琰, 陆伦 2010 59 7463]
[11] Rudin L, Osher S, Fatemi E 1992 Physica D 60 259
[12] Chan T, Esedoglu S, Park F, Yip A 2006 Mathematical Models of Computer Vision: the Handbook (Boston, MA: Springer) p176
[13] Block K T, Uecker M, Frahm J 2007 Magnet. Reson. Med. 57 1086
[14] Osher S, Burger M, Goldfarb D, Xu J, Yin W 2005 Multiscale Model. Sim. 4 460
[15] Sha L, Guo H, Song A W 2003 J. Magnet. Reson. 162 250
[16] Jackson J I, Meyer C H, Nishimura D G, Macovski A 1991 IEEE Trans. Med. Imaging. 10 473
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