Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images_Journal of Biomedical Engineering

Authors：

LI Xinling , HUANG Yunzhi , HAN Luyi ,  LIU Qi , HE Ling , ZHANG Jin , ZHANG Junpeng , ZHANG Jiang

School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China;

Corresponding author：

LIU Qi, Email: liuqi@scu.edu.com

Keywords：

susceptibility weighted imaging; magnitude image; phase unwrapping; phase error

DOI：

10.7507/1001-5515.201611011

Video：

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Abstract Full text Figures/Tables Video References Cited by

To better use the phase information to compensate the influence of blood flow, the phase unwrapping problem in susceptibility weighted imaging (SWI) is studied in this paper. In order to improve the accuracy of unwrapping, this paper proposes a magnitude image-guided phase unwrapping algorithm of SWI. The basic idea is as follows: (1) reduce the influence of noise by improving the rotational invariant non-local principal component analysis method (PRI-NL-PCA); (2) extract the corresponding solid region in the phase image to avoid the influence of the background noise on the phase unwrapping method; (3) use the phase compensation method to constrain the phase image reconstructed by the K-space. Finally, the reliability of the unwrapping method is evaluated by using four kinds of statistics as quantification index: the number, mean (M), variance (Var), and positive percentage (Pos) and negative percentage (Neg) of phasic error points. By comparing the simulated data with 226 sets of true head SWI data, the results show that the proposed algorithm has high accuracy compared with the classical branch cut method and the least squares method.

Citation： LI Xinling, HUANG Yunzhi, HAN Luyi, LIU Qi, HE Ling, ZHANG Jin, ZHANG Junpeng, ZHANG Jiang. Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images. Journal of Biomedical Engineering, 2017, 34(5): 721-729. doi: 10.7507/1001-5515.201611011 Copy

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2.	Gilbert G, Savard G, Bard C, et al. Quantitative comparison between a multiecho sequence and a single-echo sequence for susceptibility-weighted phase imaging. Magn Reson Imaging, 2012, 30(5): 722-730.
3.	Wharton S, Schafer A R. Susceptibility mapping in the human brain using threshold-based k-space division. Magnetic Resonance in Medicine, 2010, 63(5): 1292-1304.
4.	Schweser F, Deistung A, Lehr B W, et al. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain Iron metabolism? Neuroimage, 2011, 54(4): 2789-2807.
5.	Rydell J, Knutsson H, Pettersson J, et al. Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient//Ayache N, Ourselin S, Maeder A. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Berlin, Heidelberg: Springer, Berlin, Heidelberg, 2007: 210-218.
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7.	Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Science, 1988, 23(4): 713-720.
8.	Cusack R, Huntley J M, Goldrein H T. Improved noise-immune phase-unwrapping algorithm. Appl Opt, 1989, 28(16): 781-789.
9.	Zhao Ming, Huang Lei, Zhang Qican, et al. Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies. Appl Opt, 2011, 50(33): 6214-6224.
10.	Witoszynskyj S, Rauscher A, Reichenbach J R, et al. Phase unwrapping of MR images using Phi UN--a fast and robust region growing algorithm. Med Image Anal, 2009, 13(2): 257-268.
11.	Jenkinson M. Fast, automated, N-dimensional phase-unwrapping algorithm. Magnetic Resonance in Medicine, 2003, 49(1): 193-197.
12.	Ghiglia D C, Romero L A. Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods. Journal of the Optical Society of America A, 1994, 11(1): 107-117.
13.	Chan T F, Vese L A. Active contours without edges. IEEE Trans Image Process, 2001, 10(2): 266-277.
14.	Ye Xiufen, Zhang Zhehui, Liu P X, et al. Sonar image segmentation based on GMRF and level-set models. Ocean Engineering, 2010, 37(10): 891-901.
15.	Henkelman R M. Measurement of signal Intensities in the presence of noise in MR images. Med Phys, 1985, 12(2): 232-233.
16.	Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine, 1996, 34(2): 910-914.
17.	Manjón J V, Coupé P, Buades A. MRI noise estimation and denoising using non-local PCA. Med Image Anal, 2015, 22(1): 35-47.
18.	Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a new one. Multiscale Model Simul, 2005, 4(2): 490-530.
19.	Zhang Lei, Dong Weisheng, Zhang D, et al. Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognit, 2010, 43(4): 1531-1549.
20.	Manjón J V, Coupé P, Buades A, et al. New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal, 2012, 16(1): 18-27.
21.	Coupe P, Yger P, Prima S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging, 2008, 27(4): 425-441.
22.	Bouthemy P. Adaptive spatio-temporal restoration for 4D fluorescence microscopic imaging. Lect Notes Comput Sci, 2005, 8(Pt 1): 893-901.
23.	Buades A, Coll B, Morel J M. Nonlocal image and movie denoising. Int J Comput Vis, 2008, 76(2): 123-139.

1. Haacke E M, Xu Y, Cheng Y C N, et al. Susceptibility weighted imaging (SWI). Magnetic Resonance in Medicine, 2004, 52(3): 612-618.
2. Gilbert G, Savard G, Bard C, et al. Quantitative comparison between a multiecho sequence and a single-echo sequence for susceptibility-weighted phase imaging. Magn Reson Imaging, 2012, 30(5): 722-730.
3. Wharton S, Schafer A R. Susceptibility mapping in the human brain using threshold-based k-space division. Magnetic Resonance in Medicine, 2010, 63(5): 1292-1304.
4. Schweser F, Deistung A, Lehr B W, et al. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain Iron metabolism? Neuroimage, 2011, 54(4): 2789-2807.
5. Rydell J, Knutsson H, Pettersson J, et al. Phase sensitive reconstruction for water/fat separation in MR imaging using inverse gradient//Ayache N, Ourselin S, Maeder A. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Berlin, Heidelberg: Springer, Berlin, Heidelberg, 2007: 210-218.
6. Bagher-Ebadian H, Jiang Quan, Ewing J R. A modified Fourier-based phase unwrapping algorithm with an application to MRI venography. Journal of Magnetic Resonance Imaging, 2008, 27(3): 649-652.
7. Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Science, 1988, 23(4): 713-720.
8. Cusack R, Huntley J M, Goldrein H T. Improved noise-immune phase-unwrapping algorithm. Appl Opt, 1989, 28(16): 781-789.
9. Zhao Ming, Huang Lei, Zhang Qican, et al. Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies. Appl Opt, 2011, 50(33): 6214-6224.
10. Witoszynskyj S, Rauscher A, Reichenbach J R, et al. Phase unwrapping of MR images using Phi UN--a fast and robust region growing algorithm. Med Image Anal, 2009, 13(2): 257-268.
11. Jenkinson M. Fast, automated, N-dimensional phase-unwrapping algorithm. Magnetic Resonance in Medicine, 2003, 49(1): 193-197.
12. Ghiglia D C, Romero L A. Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods. Journal of the Optical Society of America A, 1994, 11(1): 107-117.
13. Chan T F, Vese L A. Active contours without edges. IEEE Trans Image Process, 2001, 10(2): 266-277.
14. Ye Xiufen, Zhang Zhehui, Liu P X, et al. Sonar image segmentation based on GMRF and level-set models. Ocean Engineering, 2010, 37(10): 891-901.
15. Henkelman R M. Measurement of signal Intensities in the presence of noise in MR images. Med Phys, 1985, 12(2): 232-233.
16. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine, 1996, 34(2): 910-914.
17. Manjón J V, Coupé P, Buades A. MRI noise estimation and denoising using non-local PCA. Med Image Anal, 2015, 22(1): 35-47.
18. Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a new one. Multiscale Model Simul, 2005, 4(2): 490-530.
19. Zhang Lei, Dong Weisheng, Zhang D, et al. Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognit, 2010, 43(4): 1531-1549.
20. Manjón J V, Coupé P, Buades A, et al. New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal, 2012, 16(1): 18-27.
21. Coupe P, Yger P, Prima S, et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging, 2008, 27(4): 425-441.
22. Bouthemy P. Adaptive spatio-temporal restoration for 4D fluorescence microscopic imaging. Lect Notes Comput Sci, 2005, 8(Pt 1): 893-901.
23. Buades A, Coll B, Morel J M. Nonlocal image and movie denoising. Int J Comput Vis, 2008, 76(2): 123-139.

Journal of Biomedical Engineering

Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images

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