Accelerating multi-echo water-fat MRI with a joint locally low-rank and spatial sparsity-promoting reconstruction.
OBJECTIVE
Our aim was to demonstrate the benefits of using locally low-rank (LLR) regularization for the compressed sensing reconstruction of highly-accelerated quantitative water-fat MRI, and to validate fat fraction (FF) and [Formula: see text] relaxation against reference parallel imaging in the abdomen.


METHODS
Reconstructions using spatial sparsity regularization (SSR) were compared to reconstructions with LLR and the combination of both (LLR+SSR) for up to seven fold accelerated 3-D bipolar multi-echo GRE imaging. For ten volunteers, the agreement with the reference was assessed in FF and [Formula: see text] maps.


RESULTS
LLR regularization showed superior noise and artifact suppression compared to reconstructions using SSR. Remaining residual artifacts were further reduced in combination with SSR. Correlation with the reference was excellent for FF with [Formula: see text] = 0.99 (all methods) and good for [Formula: see text] with [Formula: see text] = [0.93, 0.96, 0.95] for SSR, LLR and LLR+SSR. The linear regression gave slope and bias (%) of (0.99, 0.50), (1.01, 0.19) and (1.01, 0.10), and the hepatic FF/[Formula: see text] standard deviation was 3.5%/12.1 s[Formula: see text], 1.9%/6.4 s[Formula: see text] and 1.8%/6.3 s[Formula: see text] for SSR, LLR and LLR+SSR, indicating the least bias and highest SNR for LLR+SSR.


CONCLUSIONS
A novel reconstruction using both spatial and spectral regularization allows obtaining accurate FF and [Formula: see text] maps for prospectively highly accelerated acquisitions.
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. felix.lugauer@fau.de.
Felix
http://orcid.org/0000-0003-2166-7296
F
Lugauer
Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany.
Dominik
D
Nickel
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany.
Jens
J
Wetzl
Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany.
Berthold
B
Kiefer
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany.
Joachim
J
Hornegger
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany.
Andreas
A
Maier
D000273:Adipose Tissue / Q000000981:diagnostic imaging* / Q000378:metabolism; D000328:Adult; D000465:Algorithms; D016477:Artifacts; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011381:Programming Languages; D059629:Signal-To-Noise Ratio; D014867:Water
D014867:Water
Compressed sensing; Fat fraction; Locally low-rank (LLR); Multi-echo Dixon; Quantitative water-fat MRI