TY - JOUR
T1 - Compressed sensing and the use of phased array coils in 23Na MRI
T2 - a comparison of a SENSE-based and an individually combined multi-channel reconstruction
AU - Lachner, Sebastian
AU - Utzschneider, Matthias
AU - Zaric, Olgica
AU - Minarikova, Lenka
AU - Ruck, Laurent
AU - Zbýň, Štefan
AU - Hensel, Bernhard
AU - Trattnig, Siegfried
AU - Uder, Michael
AU - Nagel, Armin M.
N1 - Funding Information:
This work was supported by the Vienna Science and Technology Fund (WWTF, project LS14-096). The authors declare that they have no conflicts of interest.
Publisher Copyright:
© 2020
PY - 2021/2
Y1 - 2021/2
N2 - Purpose: To implement and to evaluate a compressed sensing (CS) reconstruction algorithm based on the sensitivity encoding (SENSE) combination scheme (CS-SENSE), used to reconstruct sodium magnetic resonance imaging (23Na MRI) multi-channel breast data sets. Methods: In a simulation study, the CS-SENSE algorithm was tested and optimized by evaluating the structural similarity (SSIM) and the normalized root-mean-square error (NRMSE) for different regularizations and different undersampling factors (USF = 1.8/3.6/7.2/14.4). Subsequently, the algorithm was applied to data from in vivo measurements of the healthy female breast (n = 3) acquired at 7 T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND). Results: The CS-SENSE reconstruction leads to an increased image quality for all undersampling factors and employed regularizations. Especially if a simple 2nd order total variation is chosen as sparsity transformation, the CS-SENSE reconstruction increases the image quality of highly undersampled data sets (CS-SENSE: SSIMUSF=7.2 = 0.234, NRMSEUSF=7.2 = 0.491 vs. CS-IND: SSIMUSF=7.2 = 0.201, NRMSEUSF=7.2 = 0.506). Conclusion: The CS-SENSE reconstruction supersedes the need of CS weighting factors for each channel as well as a method to combine single channel data. The CS-SENSE algorithm can be used to reconstruct undersampled data sets with increased image quality. This can be exploited to reduce total acquisition times in 23Na MRI.
AB - Purpose: To implement and to evaluate a compressed sensing (CS) reconstruction algorithm based on the sensitivity encoding (SENSE) combination scheme (CS-SENSE), used to reconstruct sodium magnetic resonance imaging (23Na MRI) multi-channel breast data sets. Methods: In a simulation study, the CS-SENSE algorithm was tested and optimized by evaluating the structural similarity (SSIM) and the normalized root-mean-square error (NRMSE) for different regularizations and different undersampling factors (USF = 1.8/3.6/7.2/14.4). Subsequently, the algorithm was applied to data from in vivo measurements of the healthy female breast (n = 3) acquired at 7 T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND). Results: The CS-SENSE reconstruction leads to an increased image quality for all undersampling factors and employed regularizations. Especially if a simple 2nd order total variation is chosen as sparsity transformation, the CS-SENSE reconstruction increases the image quality of highly undersampled data sets (CS-SENSE: SSIMUSF=7.2 = 0.234, NRMSEUSF=7.2 = 0.491 vs. CS-IND: SSIMUSF=7.2 = 0.201, NRMSEUSF=7.2 = 0.506). Conclusion: The CS-SENSE reconstruction supersedes the need of CS weighting factors for each channel as well as a method to combine single channel data. The CS-SENSE algorithm can be used to reconstruct undersampled data sets with increased image quality. This can be exploited to reduce total acquisition times in 23Na MRI.
KW - Compressed sensing
KW - Iterative reconstruction
KW - Multi-channel
KW - Prior knowledge
KW - Sensitivity encoding
KW - Sodium MRI
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U2 - 10.1016/j.zemedi.2020.10.003
DO - 10.1016/j.zemedi.2020.10.003
M3 - Article
C2 - 33183893
AN - SCOPUS:85095818325
SN - 0939-3889
VL - 31
SP - 48
EP - 57
JO - Zeitschrift fur Medizinische Physik
JF - Zeitschrift fur Medizinische Physik
IS - 1
ER -