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1. Deblurring for spiral real‐time MRI using convolutional neural networks

   https://doi.org/10.1002/mrm.28393  

   Lim, Yongwan ; Bliesener, Yannick ; Narayanan, Shrikanth ; Nayak, Krishna S. ( July 2020 , Magnetic Resonance in Medicine)

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

   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.

   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.

   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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2. Improved 3D real‐time MRI of speech production

   https://doi.org/10.1002/mrm.28651  

   Zhao, Ziwei ; Lim, Yongwan ; Byrd, Dani ; Narayanan, Shrikanth ; Nayak, Krishna S. ( January 2021 , Magnetic Resonance in Medicine)

   To provide 3D real‐time MRI of speech production with improved spatio‐temporal sharpness using randomized, variable‐density, stack‐of‐spiral sampling combined with a 3D spatio‐temporally constrained reconstruction.

   We evaluated five candidate (k,t) sampling strategies using a previously proposed gradient‐echo stack‐of‐spiral sequence and a 3D constrained reconstruction with spatial and temporal penalties. Regularization parameters were chosen by expert readers based on qualitative assessment. We experimentally determined the effect of spiral angle increment andk_ztemporal order. The strategy yielding highest image quality was chosen as the proposed method. We evaluated the proposed and original 3D real‐time MRI methods in 2 healthy subjects performing speech production tasks that invoke rapid movements of articulators seen in multiple planes, using interleaved 2D real‐time MRI as the reference. We quantitatively evaluated tongue boundary sharpness in three locations at two speech rates.

   The proposed data‐sampling scheme uses a golden‐angle spiral increment in thek_x−k_yplane and variable‐density, randomized encoding alongk_z. It provided a statistically significant improvement in tongue boundary sharpness score (P < .001) in the blade, body, and root of the tongue during normal and 1.5‐times speeded speech. Qualitative improvements were substantial during natural speech tasks of alternating high, low tongue postures during vowels. The proposed method was also able to capture complex tongue shapes during fast alveolar consonant segments. Furthermore, the proposed scheme allows flexible retrospective selection of temporal resolution.

   We have demonstrated improved 3D real‐time MRI of speech production using randomized, variable‐density, stack‐of‐spiral sampling with a 3D spatio‐temporally constrained reconstruction.

    

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3. A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images

   https://doi.org/10.1038/s41597-021-00976-x  

   Lim, Yongwan ; Toutios, Asterios ; Bliesener, Yannick ; Tian, Ye ; Lingala, Sajan Goud ; Vaz, Colin ; Sorensen, Tanner ; Oh, Miran ; Harper, Sarah ; Chen, Weiyi ; et al ( July 2021 , Scientific Data)

   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. Intermittently tagged real‐time MRI reveals internal tongue motion during speech production

   https://doi.org/10.1002/mrm.27745  

   Chen, Weiyi ; Byrd, Dani ; Narayanan, Shrikanth ; Nayak, Krishna S. ( March 2019 , Magnetic Resonance in Medicine)

   To demonstrate a tagging method compatible with RT‐MRI for the study of speech production.

   Tagging is applied as a brief interruption to a continuous real‐time spiral acquisition. Tagging can be initiated manually by the operator, cued to the speech stimulus, or be automatically applied with a fixed frequency. We use a standard 2D 1‐3‐3‐1 binomial SPAtial Modulation of Magnetization (SPAMM) sequence with 1 cm spacing in both in‐plane directions. Tag persistence in tongue muscle is simulated and validated in vivo. The ability to capture internal tongue deformations is tested during speech production of American English diphthongs in native speakers.

   We achieved an imaging window of 650‐800 ms at 1.5T, with imaging signal to noise ratio ≥ 17 and tag contrast to noise ratio ≥ 5 in human tongue, providing 36 frames/s temporal resolution and 2 mm in‐plane spatial resolution with real‐time interactive acquisition and view‐sharing reconstruction. The proposed method was able to capture tongue motion patterns and their relative timing with adequate spatiotemporal resolution during the production of American English diphthongs and consonants.

   Intermittent tagging during real‐time MRI of speech production is able to reveal the internal deformations of the tongue. This capability will allow new investigations of valuable spatiotemporal information on the biomechanics of the lingual subsystems during speech without reliance on binning speech utterance repetition.

    

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5. Aliasing artifact reduction in spiral real‐time MRI

   https://doi.org/10.1002/mrm.28746  

   Tian, Ye ; Lim, Yongwan ; Zhao, Ziwei ; Byrd, Dani ; Narayanan, Shrikanth ; Nayak, Krishna S. ( March 2021 , Magnetic Resonance in Medicine)

   To mitigate a common artifact in spiral real‐time MRI, caused by aliasing of signal outside the desired FOV. This artifact frequently occurs in midsagittal speech real‐time MRI.

   Simulations were performed to determine the likely origin of the artifact. Two methods to mitigate the artifact are proposed. The first approach, denoted as “large FOV” (LF), keeps an FOV that is large enough to include the artifact signal source during reconstruction. The second approach, denoted as “estimation‐subtraction” (ES), estimates the artifact signal source before subtracting a synthetic signal representing that source in multicoil k‐space raw data. Twenty‐five midsagittal speech‐production real‐time MRI data sets were used to evaluate both of the proposed methods. Reconstructions without and with corrections were evaluated by two expert readers using a 5‐level Likert scale assessing artifact severity. Reconstruction time was also compared.

   The origin of the artifact was found to be a combination of gradient nonlinearity and imperfect anti‐aliasing in spiral sampling. The LF and ES methods were both able to substantially reduce the artifact, with an averaged qualitative score improvement of 1.25 and 1.35 Likert levels for LF correction and ES correction, respectively. Average reconstruction time without correction, with LF correction, and with ES correction were 160.69 ± 1.56, 526.43 ± 5.17, and 171.47 ± 1.71 ms/frame.

   Both proposed methods were able to reduce the spiral aliasing artifacts, with the ES‐reduction method being more effective and more time efficient.

    

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