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This work develops and evaluates a self‐navigated variable density spiral (VDS)‐based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3‐ms long) were used to minimize sensitivity to off‐resonance. A custom 16‐channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self‐navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity‐encoding–based nonlocal soft‐weighted temporal regularization scheme. Our approach was compared with view‐sharing, low‐rank, temporal finite difference, extra dimension‐based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm2/pixel and a temporal resolution of 17.4 ms/frame for single‐slice imaging, and 52.2 ms/frame for concurrent three‐slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self‐navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T. [ABSTRACT FROM AUTHOR]

Purpose: To develop a custom coil and evaluate its utility for accelerated upper and infraglottic airway MRI at 3 T. Methods: A 16‐channel flexible and anatomy‐conforming coil was developed to provide localized sensitivity over upper and infraglottic airway regions of interest. Parallel‐imaging capabilities were compared against existing head and head–neck coils. SENSE geometry factor losses were quantified for retrospectively accelerating 3D MRI. Blinded image‐quality ratings from two experts were performed. Spiral GRAPPA reconstructions were evaluated for a speaking task at a time resolution of 40 ms. Contrast‐to‐noise ratios between air and tissue at key landmarks along the vocal tract were compared. SENSE imaging with the custom coil in the lateral recumbent posture was evaluated. Multislice imaging was performed to image swallowing at 17 ms/frame via constrained reconstruction. Results: The custom coil showed improved SENSE imaging up to 3‐fold acceleration when accelerated along either the anterior–posterior or the superior–inferior direction and a net 4‐fold acceleration when accelerated along both directions. Spiral GRAPPA reconstructions with the custom coil showed higher contrast‐to‐noise ratio when compared with existing coils. In the lateral posture, robust SENSE imaging was achieved at up to 2‐fold and 3‐fold acceleration levels in the superior–inferior and anterior–posterior directions, respectively. Key events of swallowing in the multislice dynamic images were identified by an otolaryngologist. Conclusion: The coil provided improved parallel imaging of upper and infraglottic airway in both supine and lateral recumbent postures. It enabled efficient accelerated dynamic imaging of speaking and swallowing. [ABSTRACT FROM AUTHOR]

Purpose: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. Results: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. Conclusions: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a technique for 3D dynamic MRI of the full vocal tract at high temporal resolution during natural speech. Methods: We demonstrate 2.4 × 2.4 × 5.8 mm3 spatial resolution, 61‐ms temporal resolution, and a 200 × 200 × 70 mm3 FOV. The proposed method uses 3D gradient‐echo imaging with a custom upper‐airway coil, a minimum‐phase slab excitation, stack‐of‐spirals readout, pseudo golden‐angle view order in kx‐ky, linear Cartesian order along kz, and spatiotemporal finite difference constrained reconstruction, with 13‐fold acceleration. This technique is evaluated using in vivo vocal tract airway data from 2 healthy subjects acquired at 1.5T scanner, 1 with synchronized audio, with 2 tasks during production of natural speech, and via comparison with interleaved multislice 2D dynamic MRI. Results: This technique captured known dynamics of vocal tract articulators during natural speech tasks including tongue gestures during the production of consonants "s" and "l" and of consonant–vowel syllables, and was additionally consistent with 2D dynamic MRI. Coordination of lingual (tongue) movements for consonants is demonstrated via volume‐of‐interest analysis. Vocal tract area function dynamics revealed critical lingual constriction events along the length of the vocal tract for consonants and vowels. Conclusion: We demonstrate feasibility of 3D dynamic MRI of the full vocal tract, with spatiotemporal resolution adequate to visualize lingual movements for consonants and vocal tact shaping during natural productions of consonant–vowel syllables, without requiring multiple repetitions. [ABSTRACT FROM AUTHOR]

Purpose: To improve the depiction and tracking of vocal tract articulators in spiral real‐time MRI (RT‐MRI) of speech production by estimating and correcting for dynamic changes in off‐resonance. Methods: The proposed method computes a dynamic field map from the phase of single‐TE dynamic images after a coil phase compensation where complex coil sensitivity maps are estimated from the single‐TE dynamic scan itself. This method is tested using simulations and in vivo data. The depiction of air–tissue boundaries is evaluated quantitatively using a sharpness metric and visual inspection. Results: Simulations demonstrate that the proposed method provides robust off‐resonance correction for spiral readout durations up to 5 ms at 1.5T. In ‐vivo experiments during human speech production demonstrate that image sharpness is improved in a majority of data sets at air–tissue boundaries including the upper lip, hard palate, soft palate, and tongue boundaries, whereas the lower lip shows little improvement in the edge sharpness after correction. Conclusion: Dynamic off‐resonance correction is feasible from single‐TE spiral RT‐MRI data, and provides a practical performance improvement in articulator sharpness when applied to speech production imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a model‐based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast‐enhanced MRI (DCE‐MRI) data. Methods: The proposed method poses the tracer‐kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down‐sampled brain tumor DCE‐MRI datasets. We also demonstrate application to 30‐fold prospectively undersampled brain tumor DCE‐MRI. Results: In DRO studies with up to 60‐fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third‐party TK solver. In retrospective undersampling studies, this method provided patient‐specific AIF with normalized root mean‐squared‐error (normalized by the 90th percentile value) less than 8% at up to 100‐fold undersampling. In the 30‐fold undersampled prospective study, the proposed method provided high‐resolution whole‐brain TK maps and patient‐specific AIF. Conclusion: The proposed model‐based DCE‐MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient‐specific AIF. TK maps and patient‐specific AIF with high fidelity can be reconstructed at up to 100‐fold undersampling in k,t‐space. Magn Reson Med 79:2804–2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]