TY - GEN
T1 - Hybrid sparse regularization for magnetic resonance spectroscopy
AU - Laruelo, Andrea
AU - Chaari, Lotfi
AU - Batatia, Hadj
AU - Ken, Soleakhena
AU - Rowland, Ben
AU - Laprie, Anne
AU - Tourneret, Jean-Yves
PY - 2013/9/26
Y1 - 2013/9/26
N2 - Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from poor signal-to-noise ratio limiting its clinical use for treatment purposes. This is due to the combination of a weak MR signal and low metabolite concentrations, in addition to the acquisition noise. We propose a new method that handles this challenge by efficiently denoising MRSI signals without constraining the spectral or spatial profiles. The proposed denoising approach is based on wavelet transforms and exploits the sparsity of the MRSI signals both in the spatial and frequency domains. A fast proximal optimization algorithm is then used to recover the optimal solution. Experiments on synthetic and real MRSI data showed that the proposed scheme achieves superior noise suppression (SNR increase up to 60%). In addition, this method is computationally efficient and preserves data features better than existing methods.
AB - Magnetic resonance spectroscopy imaging (MRSI) is a powerful non-invasive tool for characterising markers of biological processes. This technique extends conventional MRI by providing an additional dimension of spectral information describing the abnormal presence or concentration of metabolites of interest. Unfortunately, in vivo MRSI suffers from poor signal-to-noise ratio limiting its clinical use for treatment purposes. This is due to the combination of a weak MR signal and low metabolite concentrations, in addition to the acquisition noise. We propose a new method that handles this challenge by efficiently denoising MRSI signals without constraining the spectral or spatial profiles. The proposed denoising approach is based on wavelet transforms and exploits the sparsity of the MRSI signals both in the spatial and frequency domains. A fast proximal optimization algorithm is then used to recover the optimal solution. Experiments on synthetic and real MRSI data showed that the proposed scheme achieves superior noise suppression (SNR increase up to 60%). In addition, this method is computationally efficient and preserves data features better than existing methods.
UR - http://www.scopus.com/inward/record.url?scp=84886455331&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2013.6611110
DO - 10.1109/EMBC.2013.6611110
M3 - Conference contribution
C2 - 24111297
AN - SCOPUS:84886455331
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society
SP - 6768
EP - 6771
BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - IEEE
T2 - 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2013
Y2 - 3 July 2013 through 7 July 2013
ER -