Presentation 2010-11-15
Compressive Sensing of MR Images Using FREBAS Transform
Koji MIYABAYASHI, Satoshi ITO, Yoshifumi YAMADA,
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Abstract(in English) Recently, there has been an emerging interest to accelerate MRI through iterative reconstruction of undersampled data based on the metrics that promote image sparsity. Compressed sensing theory predicts that such methods may accurately reconstruct images from data sampled much below the Nyquist limit through minimization of L1 norms. If the image is not sparse, a spasifying transformation should be used to allow compressed sensing. We proposed a new compressed sensing scheme in which FREBAS transform is used for the sparsifying transform. FREBAS transform is a kind of multi-resolution analysis, which decompose input image into many down-scaled images having different frequency bandwidth. In this work we compare the FREBAS compressed sensing with standard compressed sensing. Experiments show that our method can achieve a reduction factor higher than the standard compressed sensing method.
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Keyword(in English) k-space / sampling theorem / fast imaging
Paper # MI2010-75
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Committee MI
Conference Date 2010/11/8(1days)
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Registration To Medical Imaging (MI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Compressive Sensing of MR Images Using FREBAS Transform
Sub Title (in English)
Keyword(1) k-space
Keyword(2) sampling theorem
Keyword(3) fast imaging
1st Author's Name Koji MIYABAYASHI
1st Author's Affiliation Department of Information Systems Sciences, Utsunomiya University()
2nd Author's Name Satoshi ITO
2nd Author's Affiliation Department of Information Systems Sciences, Utsunomiya University
3rd Author's Name Yoshifumi YAMADA
3rd Author's Affiliation Utsunomiya University
Date 2010-11-15
Paper # MI2010-75
Volume (vol) vol.110
Number (no) 280
Page pp.pp.-
#Pages 5
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