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1. MR method for measuring microscopic histologic soft tissue textures

   https://doi.org/10.1002/mrm.28731  

   Sonn, Geoffrey A. ; Fan, Richard E. ; Kunder, Christian A. ; Gold, Garry E. ; James, Kristin M. ; Parker, Ian D. ; Carlson, Jean M. ; Cannizzaro, Scott M. ; James, Timothy W. ( February 2021 , Magnetic Resonance in Medicine)

   Provide a direct, non‐invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases.

   MR micro‐Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k‐values to characterize texture—as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k‐value phase encode to the excited spins in the VOI, and acquiring each individual k‐value data point in a single excitation—providing motion immunity and extended acquisition time for maximizing signal‐to‐noise ratio. Additional k‐value measurements from the same tissue can be made to characterize the tissue texture in the VOI—there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue.

   Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented.

   The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.

    

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2. 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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3. Dynamic off‐resonance correction for spiral real‐time MRI of speech

   https://doi.org/10.1002/mrm.27373  

   Lim, Yongwan ; Lingala, Sajan Goud ; Narayanan, Shrikanth S. ; Nayak, Krishna S. ( July 2018 , Magnetic Resonance in Medicine)

   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.

   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.

   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.

   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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4. 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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5. Deformable cardiac surface tracking by adaptive estimation algorithms

   https://doi.org/10.1038/s41598-023-28578-0  

   Tuna, E. Erdem ; Franson, Dominique ; Seiberlich, Nicole ; Çavuşoğlu, M. Cenk ( January 2023 , Scientific Reports)

   This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step. For the real cardiac MRI datasets, the average root-mean-square tracking errors of 2.61 mm and 3.42 mm are reported respectively for the fixed and varying image slice planes. This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations via a low-order probabilistic model with the future goal of utilizing this method for the targeted interventional cardiac procedures under MR image guidance. For the real cardiac MRI datasets, the presented method was able to track the points-of-interests located on different sections of the cardiac surface within a precision of 3 pixels. The analyses show that the use of deformable cardiac surface tracking algorithm can pave the way for performing precise targeted intracardiac ablation procedures under MRI guidance. The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.

    

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