Data-driven motion-corrected brain MRI incorporating pose-dependent B fields.

Purpose: To develop a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose-dependent changes in polarizing magnetic (B ) fields.

Theory And Methods: Tissue susceptibility induces spatially varying B distributions in the head, which change with pose. A physics-inspired B model has been deployed to model the B variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B variations from appropriately acquired data without requiring navigators. The effect on data-driven motion correction is validated in simulations and in vivo.

Results: The applicability of the physics-inspired B model was confirmed in vivo. Simulations show the need to include the pose-dependent B fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B evolution from the acquired data. The proposed motion and B correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo.

Conclusion: We have developed a motion-correction framework that accounts for and estimates pose-dependent B fields. The method improves current state-of-the-art data-driven motion-correction techniques when B dependencies cannot be neglected. The use of a compact physics-inspired B model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.