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Semi-PROPELLER Compressed Sensing Image Reconstruction with Enhanced Resolution in MRI

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EN
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EN
Magnetic Resonance Imaging (MRI) reconstruction algorithm using semi-PROPELLER compressed sensing is presented in this paper. It is exhibited that introduced algorithm for estimating data shifts is feasible when super- resolution is applied. The offered approach utilizes compressively sensed MRI PROPELLER sequences and improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. It is shown that the presented approach improves MR spatial resolution in cases when Compressed Sensing (CS) sequences are used. The application of CS in medical modalities has the potential for significant scan time reductions, with visible benefits for patients and health care economics. These methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. This diagnostic modality struggles with an inherently slow data acquisition process. The use of CS to MRI leads to substantial scan time reductions [7] and visible benefits for patients and economic factors. In this report the objective is to combine Super-Resolution image enhancement algorithm with both PROPELLER sequence and CS framework. The motion estimation algorithm being a part of super resolution reconstruction (SRR) estimates shifts for all blades jointly, utilizing blade-pair correlations that are both strong and more robust to noise.
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  • Department of Electronics and Telecommunications, Poznan University of Technology
Bibliografia
  • [1] R. Gribonval and M. Nielsen, Sparse representation in unions of bases. IEEE Trans. Inf. Theory, vol. 49, no. 12, pp. 3320-3325, 2003.
  • [2] D. L. Donoho, Compressed Sensing. IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
  • [3] M. J. Wainwright, Sharp thresholds for high-dimensional and noisy recovery of sparsity. Proceedings of 44th Allerton Conference on Communications, Control and Computing, Monticello, IL, 2006.
  • [4] K. Malczewski and R. Stasinski, Super resolution for multimedia, image, and video processing applications. ecent Advances in Multimedia Signal Processing and Communications, vol. 231, pp. 171–208, 2009.
  • [5] K. Malczewski, Breaking the Resolution Limit in Medical Image Modalities. Proceedings of The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, World- Comp 2012, USA, (2012).
  • [6] J. Pipe, et al., Revised motion estimation algorithm for PROPELLER MRI. N. R.; Magn Reson Med. 2013 Sep 4.
  • [7] M. Lustig, et al. Compressed Sensing MRI. Signal Processing Magazine, IEEE, 2008.
  • [8] K. Malczewski and R. Stasinski High resolution MRI image reconstruction from a PROPELLER data set of samples. International Journal of Functional Informatics and Personalized Medicine, Volume 1, Number 3/2008, (2008).
  • [9] M. Lustig, et al. Sparse MRI: The application of compressed sensing for rapid MR imaging. Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magnetic Resonance Medicine, 2007.
  • [10] M. I. Sezan and A. M. Tekalp, Iterative image restoration with ringing suppression using the method of POCS. Acoustics, Speech, and Signal Processing, 1988.
  • [11] F. Sebert, et al. Toeplitz block matrices in compressed sensing and their applications in imaging. Proceedings of the International Conference on Information Technology and Applications in Biomedicine (ITAB ’08), pp. 47–50, Shenzhen, China, 2008.
  • [12] R. G. Baraniuk, et al. A simple proof of the restricted isometry principle for random matrices. Constructive Approximation, 2007.
  • [13] E. Candes, et al. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489–509, 2006.
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