Motion-Robust Dental MRI for Imaging of Paediatric Dental Trauma

OBJECTIVES: Dental MRI provides soft-tissue contrast without ionising-radiation, but is susceptible to motion, particularly in paediatric imaging. Independent mandibular motion limits conventional single-rigid-model correction, while tailored strategies remain unexplored for dental MRI. We developed an automated, region-adaptive motion-corrected reconstruction method tailored to dental MRI. METHODS: The proposed method automatically defines the maxillary- and mandibular landmarks, performs region-specific joint motion-corrected reconstruction, and fuses the motion-corrected regions into a single motion-free image. The method has broadly sequence applicability without using external motion-free reference, navigator, or prospective tracking. It was validated with three multi-contrast 3D sequences at 0.55T in adult controlled-motion experiments and paediatric scans. Image quality was assessed using normalised gradient square (NGS) and blinded radiologist scoring. Uncorrected (No-MoCo), full field-of-view single-rigid-model correction (MoCo-fullFOV), and fused region-adaptive correction (MoCo-fused) were compared. Cross-correlation between maxillary and mandibular motion traces classified shared and independent mandibular motion pattern. RESULTS: Eight healthy adults and 11 paediatric patients, including 10 with traumatic dental injuries and one with delayed dental development, completed 42 acquisitions. Across all acquisitions, MoCo-fused improved NGS compared with No-MoCo (P<.0001) and MoCo-fullFOV (P=.0004). In mandibular-involved motion cases (n = 23), MoCo-fullFOV did not significantly improve image quality, whereas MoCo-fused improved both NGS and radiologist scores compared with No-MoCo. Lower cross-correlation between maxillary and mandibular motion traces was associated with greater NGS improvement for MoCo-fused over MoCo-fullFOV (Spearman r = -0.66, P<.0001). Paediatric examples showed improved visualisation of pulp signal and inflammatory change after motion correction. The method is open-sourced at https://github.com/ZihanNing/dental_MRI_motion_correction.git. CONCLUSIONS: Automated region-adaptive motion-corrected dental MRI improved image quality, particularly with mandibular motion.