PURPOSE

To evaluate accelerated T- and T-mapping techniques for ultra-low-field MRI using low-rank reconstruction methods.

METHODS

Two low-rank-based algorithms, image-based locally low-rank (LLR) and k-space-based structured low-rank (SLR), were implemented to accelerate T and T mapping on a 46 mT Halbach MRI scanner. Data were acquired with 3D turbo spin-echo sequences using variable-density poisson-disk random sampling patterns. For validation, phantom and in vivo experiments were performed on six healthy volunteers to compare the obtained values with literature and to study reconstruction performance at different undersampling factors and spatial resolutions. In addition, the reconstruction performance of the LLR and SLR algorithms for T mapping was compared using retrospective undersampling datasets. Total scan times were reduced from 45/38 min (R = 1) to 23/19 min (R = 2) and 11/9 min (R = 4) for a 2.5 × 2.5 × 5 mm resolution, and to 18/16 min (R = 4) for a higher in-plane resolution 1.5 × 1.5 × 5 mm for T/T mapping, respectively.

RESULTS

Both LLR and SLR algorithms successfully reconstructed T and T maps from undersampled data, significantly reducing scan times and eliminating undersampling artifacts. Phantom validation showed that consistent T and T values were obtained at different undersampling factors up to R = 4. For in vivo experiments, comparable image quality and estimated T and T values were obtained for fully sampled and undersampled (R = 4) reconstructions, both of which were in line with the literature values.

CONCLUSIONS

The use of low-rank reconstruction allows significant acceleration of T and T mapping in low-field MRI while maintaining image quality.