Accelerated free-breathing liver fat and $R_2^{*}$ quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation

Purpose: To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and $R_2^{*}$ quantification accelerated by radial k-space undersampling.

Methods: A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, and $R_2^{*}$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and $R_2^{*}$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality.

Results: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s^-1 for $R_2^{*}$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s^-1 for $R_2^{*}$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0.

Conclusion: This proposed method may allow accelerated free-breathing liver PDFF and $R_2^{*}$ mapping with reduced biases and variations.