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This work proposes a self-navigated variable density spiral(VDS) basedmanifold regularization scheme to prospectively improve dynamic speech MRI at3T. Short readout 1.3ms spirals were used to minimize off-resonance. A custom16-channel speech coil was used for improved parallel imaging of vocal tract.The manifold model leveraged similarities between frames sharing similar speechpostures without explicit motion binning. The self-navigating capability of VDSwas leveraged to learn the Laplacian matrix of the manifold. Reconstruction wasposed as a SENSE-based non-local soft weighted temporal regularization scheme.Our approach was compared against view-sharing, low-rank, finite difference,extra-dimension-based sparsity reconstruction constraints. Under-samplingexperiments were conducted on five volunteers performing repetitive andarbitrary speaking tasks at different speaking rates. Quantitative evaluationin terms of mean square error over moving edges were performed in aretrospectively under-sampled data. For prospective under-sampling, blindedimage quality evaluation in the categories of alias artifacts, spatialblurring, and temporal blurring were performed by three voice research experts.Region of interest analysis at articulator boundaries were performed to assessarticulatory motion. Our scheme provided improved reconstruction over theothers. With prospective under-sampling, a spatial resolution of 2.4mm2/pixeland a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2ms/frame for 3-slice imaging were achieved. We demonstrated implicit motionbinning by analyzing the mechanics of the Laplacian matrix. Our methoddemonstrated superior image quality scores in reducing spatial and temporalblurring. While it exhibited faint alias artifacts similar to temporalfinite-difference, it provided statistically significant improvements overremaining constraints.

Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme