UCLA

Purpose

To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping.

Methods

MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T₁ /T₂ /T_1ρ mapping in the brain was investigated.

Results

For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T₁ /T₂ /T1_ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T₁ /T₂ /T_1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance.

Conclusion

Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.