Understanding patterns of tongue motion in speech using 3D motion estimation is challenging. Harmonic phase analysis has been used to perform noninvasive tongue motion and strain estimation using tagged magnetic resonance imaging (MRI). Two main contributions have been made in this thesis. First, the filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. For this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. Second, the datasets with inconsistent motion need to be excluded to yield meaningful motion estimation. For this work, we used a tracking-based method to evaluate the motion consistency between datasets and gave a strategy to identify the inconsistent dataset. Experiments including 2 normal subjects were done to validate our method. In all, the first work about 3D filter design improves the motion estimation accuracy and the second work about motion consistency test ensures the meaningfulness of the estimation results
