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Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint.
Magn Reson Imaging. 2019 07; 60:145-156.MR

Abstract

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.

Authors+Show Affiliations

Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. Electronic address: sebastian.lachner@uk-erlangen.de.High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.Center for Medical Physics and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Division of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany; Institute of Medical Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

30943437

Citation

Lachner, Sebastian, et al. "Compressed Sensing Reconstruction of 7 Tesla 23Na Multi-channel Breast Data Using 1H MRI Constraint." Magnetic Resonance Imaging, vol. 60, 2019, pp. 145-156.
Lachner S, Zaric O, Utzschneider M, et al. Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint. Magn Reson Imaging. 2019;60:145-156.
Lachner, S., Zaric, O., Utzschneider, M., Minarikova, L., Zbýň, Š., Hensel, B., Trattnig, S., Uder, M., & Nagel, A. M. (2019). Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint. Magnetic Resonance Imaging, 60, 145-156. https://doi.org/10.1016/j.mri.2019.03.024
Lachner S, et al. Compressed Sensing Reconstruction of 7 Tesla 23Na Multi-channel Breast Data Using 1H MRI Constraint. Magn Reson Imaging. 2019;60:145-156. PubMed PMID: 30943437.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint. AU - Lachner,Sebastian, AU - Zaric,Olgica, AU - Utzschneider,Matthias, AU - Minarikova,Lenka, AU - Zbýň,Štefan, AU - Hensel,Bernhard, AU - Trattnig,Siegfried, AU - Uder,Michael, AU - Nagel,Armin M, Y1 - 2019/03/31/ PY - 2018/12/10/received PY - 2019/03/01/revised PY - 2019/03/29/accepted PY - 2019/4/4/pubmed PY - 2019/9/5/medline PY - 2019/4/4/entrez KW - 7 Tesla KW - Compressed sensing (CS) KW - Iterative reconstruction KW - Multi-channel KW - Prior knowledge KW - Sodium ((23)Na) breast MRI SP - 145 EP - 156 JF - Magnetic resonance imaging JO - Magn Reson Imaging VL - 60 N2 - 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. SN - 1873-5894 UR - https://www.unboundmedicine.com/medline/citation/30943437/Compressed_sensing_reconstruction_of_7_Tesla_23Na_multi_channel_breast_data_using_1H_MRI_constraint_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0730-725X(18)30616-7 DB - PRIME DP - Unbound Medicine ER -