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Bias field correction for improved compressed sensing reconstruction in parallel magnetic resonance imaging

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Abstract

Parallel imaging and compressed sensing (PICS) may accelerate magnetic resonance imaging (MRI) acquisition with advanced reconstruction algorithms from under-sampled data set. However, bias field effects are often present in reconstructed MRI images due to hardware limitation and object property, which might lead to reconstruction imperfection with conventional PICS reconstruction due to altered image sparsity. In this paper, the MR signal bias field effects is modeled, and it was found that the bias field effects would induce reconstruction artifacts in the low-signal-intensity area. Then it was proposed to add a bias correction step into the PICS reconstruction framework to improve the reconstruction, and the proposed method was evaluated the proposed method via both simulation and in vivo study. It was then shown that the proposed method leads to improved image reconstruction in the low-signal-intensity area, with little extra computational effort needed compared to standard PICS reconstruction. This method could be readily extended to other signal processing area where the signal inhomogeneity problem is present and signal sparsity is exploited.

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References

  1. Feng, L., Benkert, T., Block, K.T., Sodickson, D.K., Otazo, R., Chandarana, H.: Compressed sensing for body MRI. J. Magn. Reson. Imaging 45, 966–987 (2017)

    Article  Google Scholar 

  2. Hong, S.N., Chan, R.H., Kissinger, K.V., Hauser, T.H., Josephson, M.E., Manning, W.J.: Accelerated late gadolinium enhancement cardiac MR imaging with isotropic spatial resolution using compressed sensing: initial experience. Radiology 264(3), 691–699 (2012)

    Article  Google Scholar 

  3. Sodickson, D.K., Manning, W.J.: Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn. Reson. Med. 38(4), 591–603 (1997)

    Article  Google Scholar 

  4. Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)

    Article  Google Scholar 

  5. Lustig, M., Donoho, D., Santos, J.: Compressed sensing MRI. IEEE Signal Process Magn. 25, 72–82 (2008)

    Article  Google Scholar 

  6. Moyher, S.E., Vigneron, D.B., Nelson, S.J.: Surface coil MR imaging of the human brain with an analytic reception profile correction. J. Magn. Reson. Imaging 5(2), 139–144 (1995)

    Article  Google Scholar 

  7. Dominguez-Viqueira, W., Geraghty, B.J., Lau, J.Y.C., Robb, F.J., Chen, A.P., Cunningham, C.H.: Intensity correction for multichannel hyperpolarized 13 C imaging of the heart. Magn. Reson. Med. 75(2), 859–865 (2016)

    Article  Google Scholar 

  8. Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002)

    Article  Google Scholar 

  9. Uecker, M., Lai, P., Murphy, M.J., Virtue, P., Elad, M., Pauly, J.M., Vasanawala, S.S., Lustig, M.: ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where sense meets GRAPPA. Magn. Reson. Med. 71(3), 990–1001 (2014)

    Article  Google Scholar 

  10. Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999)

    Article  Google Scholar 

  11. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)

    Article  Google Scholar 

  12. Ahmed, M.N., Yamany, S.M., Farag, A.A., Moriarty, T.: Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm. Proc.—IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1(c), 193–199 (1999)

    Google Scholar 

  13. Wiggins, G.C., Triantafyllou, C., Potthast, A., Reykowski, A., Nittka, M., Wald, L.L.: 32-Channel 3 tesla receive-only phased-array head coil with soccer-ball element geometry. Magn. Reson. Med. 56(1), 216–223 (2006)

    Article  Google Scholar 

  14. Vovk, U., Pernuš, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)

    Article  Google Scholar 

  15. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199 (2002)

    Article  Google Scholar 

  16. Blaimer, M., Breuer, F., Mueller, M., Heidemann, R.M., Griswold, M.A., Jakob, P.M.: Smash, Sense, Pils, Grappa. Top. Magn. Reson. Imaging 15(4), 223–236 (2004)

    Article  Google Scholar 

  17. Lustig, M., Pauly, J.M.: SPIRiT: iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64(2), 457–471 (2010)

    Article  Google Scholar 

  18. Murphy, M., Alley, M., Demmel, J., Keutzer, K., Vasanawala, S., Lustig, M.: Fast l-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime. IEEE Trans. Med. Imaging 31(6), 1250–1262 (2012)

    Article  Google Scholar 

  19. Cheng, J.Y., Zhang, T., Ruangwattanapaisarn, N., Alley, M.T., Uecker, M., Pauly, J.M., Lustig, M., Vasanawala, S.S.: Free-breathing pediatric MRI with nonrigid motion correction and acceleration. J. Magn. Reson. Imaging 42(2), 407–420 (2015)

    Article  Google Scholar 

  20. Block, K.T., Uecker, M., Frahm, J.: Suppression of MRI truncation artifacts using total variation constrained data extrapolation. Int. J. Biomed. Imaging 2008, 1–8 (2008)

    Article  Google Scholar 

  21. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm. Soc. Ind. Appl. Math. J. Imaging Sci. 2(1), 183–202 (2009)

    MATH  Google Scholar 

  22. Chen, C., Huang, J.: Exploiting the wavelet structure in compressed sensing MRI. Magn. Reson. Imaging 32(10), 1377–1389 (2014)

    Article  Google Scholar 

  23. Webb, A.G., Van de Moortele, P.F.: The technological future of 7 T MRI hardware. NMR Biomed. 29(9), 1305–1315 (2016)

    Article  Google Scholar 

  24. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  25. Zhao, S., Zhao, X., Ding, G., Keutzer, K: EmotionGAN: unsupervised domain adaptation for learning discrete probability distributions of image emotions. In: Proceedings of the 26th ACM international conference on Multimedia, vol. 2, pp. 1319–1327 (2018)

  26. Zhao, S., Gao, Y., Ding, G., Chua, T.S.: Real-time multimedia social event detection in microblog. IEEE Trans. Cybern. 48(11), 3218–3231 (2018)

    Article  Google Scholar 

  27. Zhao, S., Yao, H., Gao, Y., Ding, G., Chua, T.S.: Predicting personalized image emotion perceptions in social networks. IEEE Trans. Affect. Comput. 9(4), 526–540 (2018)

    Article  Google Scholar 

  28. Zhao, S., Yao, H., Gao, Y., Ji, R., Ding, G.: Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans. Multimed. 19(3), 632–645 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Medical Imaging Center of Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Fang Wang, Lei Fang, Xuehua Peng, Min Wu, Wenzhi Wang, Wenhan Zhang, Baiqu Zhu, Miao Wan, Xin Hu & Jianbo Shao

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  1. Fang Wang
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  2. Lei Fang
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  3. Xuehua Peng
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  4. Min Wu
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  5. Wenzhi Wang
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  6. Wenhan Zhang
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  7. Baiqu Zhu
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  8. Miao Wan
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  9. Xin Hu
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  10. Jianbo Shao
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Correspondence to Jianbo Shao.

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Wang, F., Fang, L., Peng, X. et al. Bias field correction for improved compressed sensing reconstruction in parallel magnetic resonance imaging. SIViP 15, 687–693 (2021). https://doi.org/10.1007/s11760-020-01721-4

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  • DOI: https://doi.org/10.1007/s11760-020-01721-4

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