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Fast and Accurate Abdominal PDFF and R2* Mapping With Model-Fitted Flip Angle Modulation and Simultaneous Multi-Slice
Chuanli Cheng1,2, Liwen Wan1,2, Hao Peng1,2
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Purpose:
To establish and validate an online model fitting-based flip angle calculation method for flip angle modulated (FAM) chemical shift-encoded (CSE) MRI integrated with simultaneous multi-slice (SMS) imaging for achieving fast and accurate abdominal proton-density fat fraction (PDFF) and R2* quantification.
Methods:
Gaussian functions were fitted to directly determine optimal FA profiles, thereby avoiding computationally intensive iterative optimization. Inter-slice SMS acceleration was incorporated to improve acquisition efficiency. Fat-water phantom and volunteer with varying liver fat deposition were scanned at 3.0 T to validate the accuracy of PDFF and R2* quantification across different PE steps with different acceleration methods by inter-slice SMS (FAM-SMS), in-plane GRAPPA (FAM-GRAPPA), and combination of SMS and GRAPPA (FAM-Hybrid), using low-FA 3D acquisitions as reference. Relative noise level (rNL) was defined to evaluate the acquisition efficiency.
Results:
In the phantom study (PDFF: 6.2%-35.7%; R2*: 29.2-154.3 s-1), the model-fitted FA profiles closely matched optimized profiles, showing unbiased PDFF and R2* (p > 0.05). FAM-SMS showed high correlation with 3D reference (PDFF: 0.994-0.995, R2*: 0.978-0.988). In vivo study, FAM-SMS achieved comparable noise levels to 3D acquisition (PDFF: 0.9%-28.7%; R2*: 32.7-85.4 s-1), demonstrating 18.7% lower PDFF SD and 18.5% lower R2* SD compared to FAM-GRAPPA (p < 0.05). Despite higher SD, FAM-Hybrid reduced rNL by 8.0% (PDFF) and 17.0% (R2*) versus FAM-GRAPPA.
Conclusion:
The proposed 2D FAM method with model-fitted FA calculation and SMS acceleration provides fast, accurate, and motion-robust liver PDFF and R2* imaging, with superior noise performance to GRAPPA, enabling simplified protocol design.
