Abstract
In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction.
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References
Candes, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52, 489–509 (2006)
Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
Lustig, M., Donoho, D., Pauly, J.: Sparse mri: The application of compressed sensing for rapid mr imaging. Magnetic Resonance in Medicine 58, 1182–1195 (2007)
He, L., Chang, T.C., Osher, S., Fang, T., Speier, P.: Mr image reconstruction by using the iterative refinement method and nonlinear inverse scale space methods. Technical report, UCLA CAM 06-35 (2006)
Ye, J., Tak, S., Han, Y., Park, H.: Projection reconstruction mr imaging using focuss. Magnetic Resonance in Medicine 57, 764–775 (2007)
Chartrand, R.: Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal Processing Letters 14, 707–710 (2007)
Chartrand, R.: Fast algorithms for nonconvex compressive sensing: Mri reconstruction from very few data. In: Proceedings of ISBI (2009)
Trzasko, J., Manduca, A., Borisch, E.: Highly undersampled magnetic resonance image reconstruction via homotopic l0-minimization. IEEE Transactions on Medical Imaging 28, 106–121 (2009)
Ma, S., Yin, W., Zhang, Y., Chakraborty, A.: An efficient algorithm for compressed mr imaging using total variation and wavelets. In: Proceedings of CVPR (2008)
Yang, J., Zhang, Y., Yin, W.: A fast alternating direction method for tvl1-l2 signal reconstruction from partial fourier data. IEEE Journal of Selected Topics in Signal Processing, Special Issue on Compressive Sensing 4(2) (2010)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2(1), 183–202 (2009)
Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transaction on Image Processing 18(113), 2419–2434 (2009)
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Huang, J., Zhang, S., Metaxas, D. (2010). Efficient MR Image Reconstruction for Compressed MR Imaging. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_17
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DOI: https://doi.org/10.1007/978-3-642-15705-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
Online ISBN: 978-3-642-15705-9
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