Free-breathing liver fat and R2∗ quantification using motion-corrected averaging based on a nonlocal means algorithm.

ABSTRACT

PURPOSE: To propose a motion-robust chemical shift-encoded (CSE) method with high signal-to-noise (SNR) for accurate quantification of liver proton density fat fraction (PDFF) and R2∗ . METHODS: A free-breathing multi-repetition 2D CSE acquisition with motion-corrected averaging using nonlocal means (NLM) was proposed. PDFF and R2∗ quantified with 2D CSE-NLM were compared to two alternative 2D techniques direct averaging and single acquisition (2D 1ave) in a digital phantom. Further, 2D NLM was compared in patients to 3D techniques (standard breath-hold, free-breathing and navigated), and the alternative 2D techniques. A reader study and quantitative analysis (Bland-Altman, correlation analysis, paired Student's t-test) were performed to evaluate the image quality and assess PDFF and R2∗ measurements in regions of interest. RESULTS: In simulations, 2D NLM resulted in lower standard deviations (STDs) of PDFF (2.7%) and R2∗ (8.2  s-1 ) compared to direct averaging (PDFF 3.1%, R2∗ 13.6  s-1 ) and 2D 1ave (PDFF 8.7%, R2∗ 33.2  s-1 ). In patients, 2D NLM resulted in fewer motion artifacts than 3D free-breathing and 3D navigated, less signal loss than 2D direct averaging, and higher SNR than 2D 1ave. Quantitatively, the STDs of PDFF and R2∗ of 2D NLM were comparable to those of 2D direct averaging (p>0.05). 2D NLM reduced bias, particularly in R2∗ (-5.73 to -0.36  s-1 ) that arises in direct averaging (-3.96 to 11.22  s-1 ) in the presence of motion. CONCLUSIONS: 2D CSE-NLM enables accurate mapping of PDFF and R2∗ in the liver during free-breathing.