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

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autorinnen und Autoren: Lachner S, Zaric O, Utzschneider M, Minarikova L, Zbýň Š, Hensel B, Trattnig S, Uder M, Nagel AM
Zeitschrift: Magnetic Resonance Imaging
Jahr der Veröffentlichung: 2019
Band: 60
Seitenbereich: 145-156
ISSN: 0730-725X


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.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Hensel, Bernhard Prof. Dr.
Max Schaldach-Stiftungsprofessur für Biomedizinische Technik
Lachner, Sebastian
Radiologisches Institut
Nagel, Armin Michael Prof. Dr.
Professur für metabolische und funktionelle MR-Bildgebung
Uder, Michael Prof. Dr.
Lehrstuhl für Diagnostische Radiologie
Utzschneider, Matthias
Radiologisches Institut


Zusätzliche Organisationseinheit(en)
Zentrum für Medizinische Physik und Technik


Einrichtungen weiterer Autorinnen und Autoren

Medizinische Universität Wien


Zitierweisen

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.

BibTeX: 

Zuletzt aktualisiert 2019-24-05 um 15:38