Motion guidance lines for robust data consistency–based retrospective motion correction in 2D and 3D MRI. Issue 5 (6th February 2023)

• Record Type: Journal Article
• Authors: Polak, Daniel; Hossbach, Julian; Splitthoff, Daniel Nicolas; Clifford, Bryan; Lo, Wei‐Ching; Tabari, Azadeh; Lang, Min; Huang, Susie Y.; Conklin, John; Wald, Lawrence L.; Cauley, Stephen
• Is Part Of: Magnetic resonance in medicine. Volume 89:Issue 5(2023)
• Page Start: 1777
• Page End: 1790
• Publication Date: 2023-02-06
• Subjects: motion correction -- parallel imaging; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29534
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To develop a robust retrospective motion‐correction technique based on repeating k‐space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI. Methods: The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency–based motion estimation and reconstruction, which can be guided by a low‐resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k‐space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence. Results: Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on‐the‐fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware. Conclusion: The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.