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Creators/Authors contains: "Bliesener, Yannick"

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  1. ; ; ; ; ; ; ; ; ; ; 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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  2. ; ; ; ( , Magnetic Resonance in Medicine)
    Purpose

    To develop and evaluate a fast and effective method for deblurring spiral real‐time MRI (RT‐MRI) using convolutional neural networks.

     Methods

    We demonstrate a 3‐layer residual convolutional neural networks to correct image domain off‐resonance artifacts in speech production spiral RT‐MRI without the knowledge of field maps. The architecture is motivated by the traditional deblurring approaches. Spatially varying off‐resonance blur is synthetically generated by using discrete object approximation and field maps with data augmentation from a large database of 2D human speech production RT‐MRI. The effect of off‐resonance range, shift‐invariance of blur, and readout durations on deblurring performance are investigated. The proposed method is validated using synthetic and real data with longer readouts, quantitatively using image quality metrics and qualitatively via visual inspection, and with a comparison to conventional deblurring methods.

     Results

    Deblurring performance was found superior to a current autocalibrated method for in vivo data and only slightly worse than an ideal reconstruction with perfect knowledge of the field map for synthetic test data. Convolutional neural networks deblurring made it possible to visualize articulator boundaries with readouts up to 8 ms at 1.5 T, which is 3‐fold longer than the current standard practice. The computation time was 12.3 ± 2.2 ms per frame, enabling low‐latency processing for RT‐MRI applications.

     Conclusion

    Convolutional neural networks deblurring is a practical, efficient, and field map‐free approach for the deblurring of spiral RT‐MRI. In the context of speech production imaging, this can enable 1.7‐fold improvement in scan efficiency and the use of spiral readouts at higher field strengths such as 3 T.

     
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