RESUMO
PURPOSE: To propose an acceleration method for 3D variable flip-angle (VFA) T1 mapping based on a technique called shift undersampling improves parametric mapping efficiency and resolution (SUPER). METHODS: The proposed method incorporates strategies of SUPER, controlled aliasing in volumetric parallel imaging (CAIPIRINHA), and total variation-based regularization to accelerate 3D VFA T1 mapping. The k-space sampling grid of CAIPIRINHA is internally undersampled with SUPER along the contrast dimension. A proximal algorithm was developed to preserve the computational efficiency of SUPER in the presence of regularization. The regularized SUPER-CAIPIRINHA (rSUPER-CAIPIRINHA) was compared with low rank plus sparsity (L + S), reconstruction of principal component coefficient maps (REPCOM), and other SUPER-based methods via simulations and in vivo brain T1 mapping. The results were assessed quantitatively with NRMSE and structural similarity index measure (SSIM), and qualitatively by two experienced reviewers. RESULTS: rSUPER-CAIPIRINHA achieved a lower NRMSE and higher SSIM than L + S (0.11 ± 0.01 vs. 0.19 ± 0.03, p < 0.001; 0.66 ± 0.05 vs. 0.37 ± 0.03, p < 0.001) and REPCOM (0.16 ± 0.02, p < 0.001; 0.46 ± 0.04, p < 0.001). The reconstruction time of rSUPER-CAIPIRINHA was 6% of L + S and 2% of REPCOM. For the qualitative comparison, rSUPER-CAIPIRINHA showed improvement of overall image quality and reductions of artifacts and blurring, although with a lower apparent SNR. Compared with 2D SUPER-SENSE, rSUPER-CAIPIRINHA significantly reduced NRMSE (0.11 ± 0.01 vs. 0.23 ± 0.04, p < 0.001) and generated less noisy reconstructions. CONCLUSION: By incorporating SUPER, CAIPIRINHA, and regularization, rSUPER-CAIPIRINHA mitigated noise amplification, reduced artifacts and blurring, and achieved faster reconstructions compared with L + S and REPCOM. These advantages render 3D rSUPER-CAIPIRINHA VFA T1 mapping potentially useful for clinical applications.