Compressed sensing reconstruction of 7 Tesla23Na multi-channel breast data using 1H MRI constraint

Lachner S, Zaric O, Utzschneider M, Minarikova L, Zbýň Š, Hensel B, Trattnig S, Uder M, Nagel AM (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Magnetic Resonance Imaging Elsevier

Book Volume: 60

Pages Range: 145-156

DOI: 10.1016/j.mri.2019.03.024

Abstract

Purpose: To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging ( 23 Na MRI)using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS)with anatomical 1 H prior knowledge. Methods: An iterative reconstruction for 23 Na 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 1 H image. A support region is included as additional regularization. Simulated and measured 23 Na 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 23 Na MRI data sets.

Authors with CRIS profile

Sebastian Lachner Institute of Radiology Matthias Utzschneider Institute of Radiology Bernhard Hensel Max Schaldach-Stiftungsprofessur für Biomedizinische Technik Michael Uder Lehrstuhl für Diagnostische Radiologie Armin Michael Nagel Professur für metabolische und funktionelle MR-Bildgebung

Additional Organisation(s)

Zentrum für Medizinische Physik und Technik

Involved external institutions

Medizinische Universität Wien AT Austria (AT)

How to cite

APA:

Lachner, S., Zaric, O., Utzschneider, M., Minarikova, L., Zbýň, Š., Hensel, B.,... Nagel, A.M. (2019). Compressed sensing reconstruction of 7 Tesla23Na multi-channel breast data using 1H MRI constraint. Magnetic Resonance Imaging, 60, 145-156. https://dx.doi.org/10.1016/j.mri.2019.03.024

MLA:

Lachner, Sebastian, et al. "Compressed sensing reconstruction of 7 Tesla23Na multi-channel breast data using 1H MRI constraint." Magnetic Resonance Imaging 60 (2019): 145-156.

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