Analysis of tongue motion has been proven useful in gaining a better understanding of speech and swallowing disorders. Tagged magnetic resonance imaging (MRI) has been used to image tongue motion, and the harmonic phase processing (HARP) method has been used to compute 3D motion from these images. However, HARP can fail with large motions due to so-called tag (or phase) jumping, yielding highly inaccurate results. The phase vector incompressible registration algorithm (PVIRA) was developed using the HARP framework to yield smooth, incompressible, and diffeomorphic motion fields, but it can also suffer from tag jumping. In this paper, we propose a new method to avoid tag jumping occurring in the later frames of tagged MR image sequences. The new approach uses PVIRA between successive time frames and then adds their stationary velocity fields to yield a starting point from which to initialize a final PVIRA stage between troublesome frames. We demonstrate on multiple data sets that this method avoids tag jumping and produces superior motion estimates compared with existing methods.
The human tongue muscles plays an important role in multiple vital human functions. Most tongue regions are extensively interdigitated with two orthogonal muscle fibers. Reconstruction of the tongue muscle fiber orientations can help understand the deformation of each muscle group and its function. High angular resolution diffusion imaging (HARDI), one of the diffusion weighted imaging techniques, has been used to resolve the crossing muscle fibers in the tongue. Most existing fiber reconstruction methods use HARDI data to estimate the fiber orientation distribution function (fODF), from which the distinct fiber orientations can be identified by a peak finding algorithm. The assignment of the primary and second fiber orientations can be inconsistent with neighboring voxels. In this paper, we propose a fiber matching algorithm to refine the display of the fiber orientations, which can be used as a post-processing step for fiber reconstruction. The fiber matching algorithm takes the fiber orientations that are reconstructed by a deep convolutional neural network as input, and computes the similarity between neighboring fibers under different assignments. The optimal assignments are achieved by solving a quadratic unconstrained binary optimization model. The proposed method was shown to greatly improve the fiber assignments on synthetic tongue fiber orientations. Application to post-mortem human tongue indicated that the proposed method can reconstruct the complex muscle fibers of the human tongue and improve the visualization of the fiber orientations.
Speech is generated through complex contacts of the tongue with the palate and teeth. Evaluation of the tonguepalate contact can be beneficial in studies of linguistics, diagnosis and treatment of speech disorders, and speech synthesis. In this paper, we propose a method of tongue-palate contact assessment based on cine MR images during speech. We use a 2D U-Net to segment the space between the top of the tongue and the palates on the sagittal slices of the cine images. Then a series of MR palatograms are generated by computing the vertical thickness of the segmented space on all the sagittal slices and projecting onto the axial plane. Compared to static palatography and electropalatography, the proposed method assesses the tongue-palate contact information as well as the tongue-to-palate distances over time. We generate a sequence of MR palatograms for two healthy subjects, from three uttered phrases. During pronunciation of the selected phrases, the tongue-palate contact points and the relative tongue-to-palate distances were similar between the subjects.
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