Data-driven motion-corrected brain MRI incorporating pose-dependent B0 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-0) fields. Theory and Methods Tissue susceptibility induces spatially varying B-0 distributions in the head, which change with pose. A physics-inspired B-0 model has been deployed to model the B-0 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-0 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-0 model was confirmed in vivo. Simulations show the need to include the pose-dependent B-0 fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B-0 evolution from the acquired data. The proposed motion and B-0 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-0 fields. The method improves current state-of-the-art data-driven motion-correction techniques when B-0 dependencies cannot be neglected. The use of a compact physics-inspired B-0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.

Automated summary

This study developed a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla), which includes modeling of pose-dependent changes in polarizing magnetic (B-0) fields. The proposed framework showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo.