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| Home > Publications database > Compressed sensing and the use of phased array coils in 23Na MRI: a comparison of a SENSE-based and an individually combined multi-channel reconstruction. > print |
| 001 | 165907 | ||
| 005 | 20240229133515.0 | ||
| 024 | 7 | _ | |a 10.1016/j.zemedi.2020.10.003 |2 doi |
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| 037 | _ | _ | |a DKFZ-2020-02464 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Lachner, Sebastian |b 0 |
| 245 | _ | _ | |a Compressed sensing and the use of phased array coils in 23Na MRI: a comparison of a SENSE-based and an individually combined multi-channel reconstruction. |
| 260 | _ | _ | |a Amsterdam |c 2021 |b Elsevier, Urban & Fischer45882 |
| 264 | _ | 1 | |3 print |2 Crossref |b Elsevier BV |c 2021-02-01 |
| 264 | _ | 1 | |3 print |2 Crossref |b Elsevier BV |c 2021-02-01 |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #LA:E020#2021 Feb;31(1):48-57 |
| 520 | _ | _ | |a 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.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 7T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND).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).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. |
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| 542 | _ | _ | |i 2021-02-01 |2 Crossref |u https://www.elsevier.com/tdm/userlicense/1.0/ |
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| 700 | 1 | _ | |a Utzschneider, Matthias |b 1 |
| 700 | 1 | _ | |a Zaric, Olgica |b 2 |
| 700 | 1 | _ | |a Minarikova, Lenka |b 3 |
| 700 | 1 | _ | |a Ruck, Laurent |b 4 |
| 700 | 1 | _ | |a Zbýň, Štefan |b 5 |
| 700 | 1 | _ | |a Hensel, Bernhard |b 6 |
| 700 | 1 | _ | |a Trattnig, Siegfried |b 7 |
| 700 | 1 | _ | |a Uder, Michael |b 8 |
| 700 | 1 | _ | |a Nagel, Armin |0 P:(DE-He78)054fd7a5195b75b11fbdc5c360276011 |b 9 |e Last author |u dkfz |
| 773 | 1 | 8 | |a 10.1016/j.zemedi.2020.10.003 |b Elsevier BV |d 2021-02-01 |n 1 |p 48-57 |3 journal-article |2 Crossref |t Zeitschrift für Medizinische Physik |v 31 |y 2021 |x 0939-3889 |
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