Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint
Autor: | Sebastian Lachner, Armin M. Nagel, Štefan Zbýň, Matthias Utzschneider, Siegfried Trattnig, Bernhard Hensel, Michael Uder, Lenka Minarikova, Olgica Zaric |
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Rok vydání: | 2019 |
Předmět: |
Computer science
Image quality business.industry Biomedical Engineering Biophysics Partial volume Pattern recognition Iterative reconstruction Regularization (mathematics) 030218 nuclear medicine & medical imaging Weighting Data set 03 medical and health sciences 0302 clinical medicine Compressed sensing Undersampling Radiology Nuclear Medicine and imaging Artificial intelligence business 030217 neurology & neurosurgery |
Zdroj: | Magnetic Resonance Imaging. 60:145-156 |
ISSN: | 0730-725X |
DOI: | 10.1016/j.mri.2019.03.024 |
Popis: | Purpose To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (23Na MRI) using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS) with anatomical 1H prior knowledge. Methods An iterative reconstruction for 23Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV(2)), adopted by anatomical weighting factors (AnaWeTV(2)) obtained from a high-resolution 1H image. A support region is included as additional regularization. Simulated and measured 23Na multi-channel data sets (n = 3) of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4) were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction. Results Compared with a conventional TV(2), the AnaWeTV(2) reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8) gridding reconstructed data set. Conclusion Especially for high USFs, the proposed algorithm allows improved image quality for multi-channel 23Na MRI data sets. |
Databáze: | OpenAIRE |
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