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
| Autor: | Laurent Ruck, Lenka Minarikova, Siegfried Trattnig, Michael Uder, Armin M. Nagel, Matthias Utzschneider, Sebastian Lachner, Bernhard Hensel, Štefan Zbýň, Olgica Zaric |
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| Rok vydání: | 2021 |
| Předmět: |
Radiological and Ultrasound Technology
Channel (digital image) business.industry Computer science Image quality Biophysics Reconstruction algorithm Pattern recognition Iterative reconstruction 030218 nuclear medicine & medical imaging Weighting 03 medical and health sciences 0302 clinical medicine Compressed sensing Undersampling Sodium MRI Radiology Nuclear Medicine and imaging Artificial intelligence business |
| Zdroj: | Zeitschrift für Medizinische Physik. 31:48-57 |
| ISSN: | 0939-3889 |
| DOI: | 10.1016/j.zemedi.2020.10.003 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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