Motion-robust, high-SNR liver fat quantification using a 2D sequential acquisition with a variable flip angle approach

Abstract

Purpose: Chemical shift encoded (CSE)-MRI enables quantification of proton-density fat fraction (PDFF) as a biomarker of liver fat content. However, conventional 3D Cartesian CSE-MRI methods require breath-holding. A motion-robust 2D Cartesian sequential method addresses this limitation but suffers from low SNR. In this work, a novel free breathing 2D Cartesian sequential CSE-MRI method using a variable flip angle approach with centric phase encoding (VFA-centric) is developed to achieve fat quantification with low $T_1$ bias, high SNR, and minimal blurring.

Methods: Numerical simulation was performed for variable flip angle schedule design and preliminary evaluation of VFA-centric method, along with several alternative flip angle designs. Phantom, adults (n = 8), and children (n = 27) were imaged at 3T. Multi-echo images were acquired and PDFF maps were estimated. PDFF standard deviation was used as a surrogate for SNR.

Results: In both simulation and phantom experiments, the VFA-centric method enabled higher SNR imaging with minimal $T_1$ bias and blurring artifacts. High correlation (slope = 1.00, intercept = 0.04, $R^2$ = 0.998) was observed in vivo between the proposed VFA-centric method obtained PDFF and reference PDFF (free breathing low-flip angle 2D sequential acquisition). Further, the proposed VFA-centric method (PDFF standard deviation = 1.5%) had a better SNR performance than the reference acquisition (PDFF standard deviation = 3.3%) with P < .001.

Conclusions: The proposed free breathing 2D Cartesian sequential CSE-MRI method with variable flip angle approach and centric-ordered phase encoding achieved motion robustness, low $T_1$ bias, high SNR compared to previous 2D sequential methods, and low blurring in liver fat quantification.

Keywords: 2D Cartesian; free breathing; high SNR; liver fat; low ${\mathrm{T}}_1$ bias; low blurring artifact; variable flip angle.

Figure 1.

The proposed formulation enables the generation of various fat and water signal profiles with different trade-offs between T₁ bias, signal level (i.e., SNR), and k-space fltering, based on the choice of several key formulation parameters (i.e., λ₁, λ₂, and k-space weighting profle). (a-c) Increasing values of λ₁, which controls the T₁ bias penalty, lead to reduced T₁ bias (i.e., similar signal profles for fat and water) at the cost of reduced signal levels. (d-f) Increasing values of λ₂, which controls the signal level, promotes high signal level (i.e., SNR) at the cost of slight differences between fat and water profles. (g-i) Effects of the desired k-space weighting profle. A flat profle (g) leads to slightly reduced signal at the center of k-space compared to Gaussian-shaped profles (h-i), which in turn lead to some k-space fltering. The parameter choice in the third column was applied in subsequent experiments in this study.

Figure 2.

Simulation-based comparisons between five different 2D CSE-MRI acquisitions (LFA-linear, HFA-linear, LFA-centric, HFA-centric, and VFA-centric), including the corresponding flip angle design (frst row), relative signal intensity profle of fat and water across the phase encoding direction (second row), PDFF map (third row), and PDFF difference map (fourth row). As demonstrated by these simulations, the proposed VFA-centric method provides relatively high signal intensity (high SNR) with little divergence between fat and water (low T₁ bias), and moderate k-space fltering (low blurring).

Figure 3.

Results from a PDFF/ T₁ phantom experiment, showing PDFF bias analysis among five different 2D CSE-MRI acquisitions (LFA-linear, HFA-linear, LFA-centric, HFA-centric, and VFA-centric). The vertical range of each acquisition represents the ROI-based pixel-wise standard deviation of the ten repeated experiment measurements. Using standard deviation of PDFF measurement as a surrogate measure of SNR, LFA-linear and LFA-centric acquisitions have higher standard deviation (i.e., lower SNR) for PDFF measurements than HFA-linear, HFA-centric, and the proposed VFA-centric acquisitions. However, HFA-linear acquisition leads to T₁ bias in PDFF measurements. Further, HFA-centric acquisition leads to severe blurring (not shown). The VFA-centric acquisition provides low bias across a wide range of T_1,water and PDFF values, and about twice the SNR of the reference acquisition (i.e., LFA-linear).

Figure 4.

Representative PDFF maps from two children, obtained with three different CSE-MRI acquisitions (3D, LFA-linear, and VFA-centric). One child was able to sustain a 20-second breath-hold (‘Good BH’, top row), whereas another child was unable to sustain such breath-hold (‘Poor BH’, bottom row). In the Good BH case, negligible motion artifacts are observed with the three acquisitions. In the Poor BH case, substantial motion artifact is observed in the BH 3D acquisition. In contrast, the free-breathing 2D acquisitions (LFA-linear and VFA-centric) effectively freeze breathing motion. In both subjects, LFA-linear acquisition leads to high noise levels in PDFF maps. The proposed VFA-centric method is able to freeze breathing motion while maintaining high SNR. Yellow circles represent sample ROIs used for further analysis.

Figure 5.

Representative PDFF maps from three adults with increasing fat fraction level using three different CSE-MRI acquisitions (3D, LFA-linear, and VFA-centric). All three acquisitions show good agreement in liver fat quantifcation. BH 3D acquisition requires breath-hold during scanning, whereas the 2D acquisitions (LFA-linear and VFA-centric) are acquired during free-breathing. The proposed VFA-centric acquisition leads to visually apparent improved noise performance compared to the standard LFA-linear acquisition. Yellow or black circles represent sample ROIs used for further analysis.

Figure 6.

Linear regression (top row) and Bland-Altman analysis (bottom row) between measured PDFF from five different CSE-MRI acquisitions (3D, HFA-linear, LFA-centric, HFA-centric, and VFA-centric) and reference PDFF (LFA-linear) across all subjects in this study. High correlation in PDFF measurements is observed between the reference LFA-linear acquisition and each of the five additional acquisitions. Based on Bland-Altman plots, the proposed VFA-centric method provides close agreement with low bias and narrow limits of agreement in PDFF measurement (Bias = 0.0%, 95 % LoA = −0.5% to 0.6%), compared to the reference LFA-linear acquisition.

Figure 7.

The proposed VFA-centric method enables liver PDFF mapping with high SNR performance (i.e., low standard deviation in PDFF measurement). Plot shows ROI-based PDFF standard deviation measurements across all subjects between six different CSE-MRI acquisitions (3D, LFA-linear, HFA-linear, LFA-centric, HFA-centric, and VFA-centric). The proposed VFA-centric method has relatively low mean standard deviation of 1.5%, comparable to HFA acquisitions and lower than LFA acquisitions. From the Bonferroni corrected t-test results, the proposed VFA-centric method shows highly significant difference (lower standard deviation) with LFA-linear acquisition (p <0.001) and LFA-centric acquisition (p <0.001). Further, the VFA-centric acquisition shows significant difference (higher standard deviation) with HFA-centric acquisition (p = 0.006) and no statistically significant difference with HFA-linear acquisition (p = 0.2). (* p<0.05, • ** p<0.01, *** p<0.001)