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Creators/Authors contains: "Lingala, Sajan Goud"

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  1. ; ; ( , IEEE Transactions on Computational Imaging)
    This work develops a novel set of algorithms, al- ternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI1 and altGDmin-MRI2), for accelerated dynamic MRI by assuming an approximate low-rank (LR) model on the matrix formed by the vectorized images of the sequence. The LR model itself is well-known in the MRI literature; our contribution is the novel GD-based algorithms which are much faster, memory-efficient, and ‘general’ compared with existing work; and careful use of a 3-level hierarchical LR model. By ‘general’, we mean that, with a single choice of parameters, our method provides accurate reconstructions for multiple acceler- ated dynamic MRI applications, multiple sampling rates and sampling schemes. We show that our methods outperform many of the popular existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively undersampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo- radial undersampling, at multiple sampling rates. Evaluations on some prospectively undersampled datasets are also provided. Our second contribution is a mini-batch subspace tracking extension that can process new measurements and return reconstructions within a short delay after they arrive. The recovery algorithm itself is also faster than its batch counterpart. 
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    Free, publicly-accessible full text available July 1, 2024
  2. ; ; ; ( , ICASSP 2022)
    Full Text Available 
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Scientific Data)
    Abstract

    Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.

     
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  4. ; ; ; ( , Magnetic Resonance in Medicine)
    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.

     
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  5. ; ; ; ; ; ( , Magnetic Resonance in Medicine)
    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 mm3spatial resolution, 61‐ms temporal resolution, and a 200 × 200 × 70 mm3FOV. 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 inkx‐ky, linear Cartesian order alongkz, 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.

     
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