Purpose Investigating the feasibility of variable flip angle (VFA) T 1 mapping for quantitative liver imaging at 0.55 T and evaluating the impact of RF spoiling correction and deep learning-based (DL) reconstruction on accuracy. Methods A VFA protocol was developed and combined with DL reconstruction to improve quantitative robustness at 0.55 T. Phantom experiments validated T 1 estimates against multi-TI inversion-recovery spin-echo reference measurements. Bloch simulations characterized RF spoiling-induced bias and derived 0.55 T-specific calibration coefficients. Twelve healthy subjects were scanned to assess field-strength-dependent effects of B 1 + inhomogeneity. Results In vivo, DL reconstruction reduced noise-related variability of voxel-wise hepatic T 1 estimates compared to conventional reconstructions in the same subject. At 0.55 T, B 1 + maps showed the highest transmit homogeneity, with liver flip angle variation of 98.2 ± 2.9%, compared with 94.6 ± 7.4% at 1.5 T and 100.7 ± 9.8% at 3 T. Accordingly, B 1 + correction had minimal impact on hepatic T 1 distributions at 0.55 T, whereas more pronounced effects were observed at higher field strengths. Phantom experiments demonstrated improved agreement of VFA-derived T 1 values with the IR-SE reference after 0.55 T-specific RF spoiling correction, supporting the accuracy of the corrected VFA approach. Bland–Altman analysis confirmed that RF spoiling correction had a greater impact on phantom T 1 accuracy than B 1 + correction. Conclusion At 0.55 T, RF spoiling and reconstruction strategies are the main determinants of quantitative accuracy, while transmit field inhomogeneity plays a minor role. Combined RF spoiling correction and DL reconstruction enables robust VFA T 1 mapping without B 1 + correction. Reduced low-field T 1 values improve VFA model conditioning and reduce sensitivity to flip angle deviations, supporting VFA-based T 1 mapping for low-field MRI.
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Helo et al. (2026) studied this question.
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