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To enable a more comprehensive view of articulations during speech through near-isotropic 3D dynamic MRI with high spatiotemporal resolution and large vocal-tract coverage.Using partial separability model-based low-rank reconstruction coupled with a sparse acquisition of both spatial and temporal models, we are able to achieve near-isotropic resolution 3D imaging with a high frame rate. The total acquisition time of the speech acquisition is shortened by introducing a sparse temporal sampling that interleaves one temporal navigator with four randomized phase and slice-encoded imaging samples. Memory and computation time are improved through compressing coils based on the region of interest for low-rank constrained reconstruction with an edge-preserving spatial penalty.The proposed method has been evaluated through experiments on several speech samples, including a standard reading passage. A near-isotropic 1.875 × 1.875 × 2 mmThree-dimensional dynamic images of the vocal tract structures during speech with high spatiotemporal resolution and axial coverage is capable of enhancing linguistic research, enabling visualization of soft tissue motions that are not possible with other modalities.

Magnetic resonance (MR) imaging is becoming an established tool in capturing articulatory and physiological motion of the structures and muscles throughout the vocal tract and enabling visual and quantitative assessment of real-time speech activities. Although motion capture speed has been regularly improved by the continual developments in high-speed MR technology, quantitative analysis of multi-subject group data remains challenging due to variations in speaking rate and imaging time among different subjects. In this paper, a workflow of post-processing methods that matches different MR image datasets within a study group is proposed. Each subject's recorded audio waveform during speech is used to extract temporal domain information and generate temporal alignment mappings from their matching pattern. The corresponding image data are resampled by deformable registration and interpolation of the deformation fields, achieving inter-subject temporal alignment between image sequences. A four-dimensional dynamic MR speech atlas is constructed using aligned volumes from four human subjects. Similarity tests between subject and target domains using the squared error, cross correlation, and mutual information measures all show an overall score increase after spatiotemporal alignment. The amount of image variability in atlas construction is reduced, indicating a quality increase in the multi-subject data for groupwise quantitative analysis.

We present a GPU-accelerated constrained reconstruction algorithm which improves image quality and reconstruction speed for compressed fluorescence lifetime microscopy.