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Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation (bibtex)
Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation (bibtex)
by V. Golkov, T. Sprenger, A. Menini, M.I. Menzel, D. Cremers and J.I. Sperl
Reference:
Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation (V. Golkov, T. Sprenger, A. Menini, M.I. Menzel, D. Cremers and J.I. Sperl), In European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting, 2013. 
Bibtex Entry:
@string{esmrmb="European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting"}
@inproceedings{Golkov-et-al-esmrmb13-comparison,
 author = {V. Golkov and T. Sprenger and A. Menini and M.I. Menzel and D. Cremers and J.I. Sperl},
 title = {Effects of Low-Rank Constraints, Line-Process Denoising, and {q}-Space Compressed Sensing on Diffusion {MR} Image Reconstruction and Kurtosis Tensor Estimation},
 booktitle = {European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) Annual Meeting},
 year = {2013},
 keywords = {magnetic resonance imaging, diffusion MRI, medical imaging, compressed sensing},
 award = {Oral Presentation},
}
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