Presentation 2015-03-16
Study on spectral reconstruction using optimization with their sparsity in MR spectroscopy
Takenori OIDA, Tomokazu MURASE, Masahiro UMEDA,
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Abstract(in English) Magnetic resonance spectroscopy (MRS) is one of the chemical analyses with nuclear magnetic resonance (NMR), which is able to measure the NMR spectra non-invasively. In general, although spectra reconstructions with fast Fourier transformation have been utilized in MRS measurements, long acquisition time has been required to obtain high resolution in frequency domain. However, long acquisition time may cause artifacts and low signal-to-noise ratio. In this study, we proposed a spectra reconstruction method with compressed sensing using L1-norm optimization. MR signals obtained from 100 mM myo-Inositol phantom were compressed, and their spectra were reconstructed by FISTA, which is one of the L1-norm optimization algorithms, and zero-filled FFT. These results showed that the acquisition time was able to be reduced by FISTA with suppressing spread of the spectra.
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Keyword(in English) magnetic resonance spectroscopy (MRS) / compressed sensing (CS) / sparse model
Paper # MBE2014-117,NC2014-68
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Committee NC
Conference Date 2015/3/9(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Study on spectral reconstruction using optimization with their sparsity in MR spectroscopy
Sub Title (in English)
Keyword(1) magnetic resonance spectroscopy (MRS)
Keyword(2) compressed sensing (CS)
Keyword(3) sparse model
1st Author's Name Takenori OIDA
1st Author's Affiliation Graduate School of Engineering, Kyoto University()
2nd Author's Name Tomokazu MURASE
2nd Author's Affiliation Meiji University of Integrative Medicine
3rd Author's Name Masahiro UMEDA
3rd Author's Affiliation Meiji University of Integrative Medicine
Date 2015-03-16
Paper # MBE2014-117,NC2014-68
Volume (vol) vol.114
Number (no) 515
Page pp.pp.-
#Pages 6
Date of Issue