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Rusho RZ, Ahmed AH, Kruger S, Alam W, Meyer D, Howard D, Story B, Jacob M, Lingala SG. Prospectively accelerated dynamic speech magnetic resonance imaging at 3 T using a self-navigated spiral-based manifold regularized scheme. NMR IN BIOMEDICINE 2024:e5135. [PMID: 38440911 DOI: 10.1002/nbm.5135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/06/2024]
Abstract
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.
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Affiliation(s)
- Rushdi Zahid Rusho
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Abdul Haseeb Ahmed
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Stanley Kruger
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Wahidul Alam
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - David Meyer
- Janette Ogg Voice Research Center, Shenandoah University, Winchester, Virginia, USA
| | - David Howard
- Department of Electronic Engineering, Royal Holloway, University of London, London, UK
| | - Brad Story
- Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, Arizona, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Sajan Goud Lingala
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
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2
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Lim Y, Kumar P, Nayak KS. Speech production real-time MRI at 0.55 T. Magn Reson Med 2024; 91:337-343. [PMID: 37799039 DOI: 10.1002/mrm.29843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/11/2023] [Accepted: 08/10/2023] [Indexed: 10/07/2023]
Abstract
PURPOSE To demonstrate speech-production real-time MRI (RT-MRI) using a contemporary 0.55T system, and to identify opportunities for improved performance compared with conventional field strengths. METHODS Experiments were performed on healthy adult volunteers using a 0.55T MRI system with high-performance gradients and a custom 8-channel upper airway coil. Imaging was performed using spiral-based balanced SSFP and gradient-recalled echo (GRE) pulse sequences using a temporal finite-difference constrained reconstruction. Speech-production RT-MRI was performed with three spiral readout durations (8.90, 5.58, and 3.48 ms) to determine trade-offs with respect to articulator contrast, blurring, banding artifacts, and overall image quality. RESULTS Both spiral GRE and bSSFP captured tongue boundary dynamics during rapid consonant-vowel syllables. Although bSSFP provided substantially higher SNR in all vocal tract articulators than GRE, it suffered from banding artifacts at TR > 10.9 ms. Spiral bSSFP with the shortest readout duration (3.48 ms, TR = 5.30 ms) had the best image quality, with a 1.54-times boost in SNR compared with an equivalent GRE sequence. Longer readout durations led to increased SNR efficiency and blurring in both bSSFP and GRE. CONCLUSION High-performance 0.55T MRI systems can be used for speech-production RT-MRI. Spiral bSSFP can be used without suffering from banding artifacts in vocal tract articulators, provide better SNR efficiency, and have better image quality than what is typically achieved at 1.5 T or 3 T.
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Affiliation(s)
- Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Prakash Kumar
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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Erattakulangara S, Kelat K, Meyer D, Priya S, Lingala SG. Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model. Bioengineering (Basel) 2023; 10:bioengineering10050623. [PMID: 37237693 DOI: 10.3390/bioengineering10050623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80-100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human.
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Affiliation(s)
- Subin Erattakulangara
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Karthika Kelat
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - David Meyer
- Janette Ogg Voice Research Center, Shenandoah University, Winchester, VA 22601, USA
| | - Sarv Priya
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
| | - Sajan Goud Lingala
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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Nayak KS, Lim Y, Campbell-Washburn AE, Steeden J. Real-Time Magnetic Resonance Imaging. J Magn Reson Imaging 2022; 55:81-99. [PMID: 33295674 PMCID: PMC8435094 DOI: 10.1002/jmri.27411] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 01/03/2023] Open
Abstract
Real-time magnetic resonance imaging (RT-MRI) allows for imaging dynamic processes as they occur, without relying on any repetition or synchronization. This is made possible by modern MRI technology such as fast-switching gradients and parallel imaging. It is compatible with many (but not all) MRI sequences, including spoiled gradient echo, balanced steady-state free precession, and single-shot rapid acquisition with relaxation enhancement. RT-MRI has earned an important role in both diagnostic imaging and image guidance of invasive procedures. Its unique diagnostic value is prominent in areas of the body that undergo substantial and often irregular motion, such as the heart, gastrointestinal system, upper airway vocal tract, and joints. Its value in interventional procedure guidance is prominent for procedures that require multiple forms of soft-tissue contrast, as well as flow information. In this review, we discuss the history of RT-MRI, fundamental tradeoffs, enabling technology, established applications, and current trends. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Krishna S. Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA,Address reprint requests to: K.S.N., 3740 McClintock Ave, EEB 400C, Los Angeles, CA 90089-2564, USA.
| | - Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Adrienne E. Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer Steeden
- Institute of Cardiovascular Science, Centre for Cardiovascular Imaging, University College London, London, UK
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Lim Y, Toutios A, Bliesener Y, Tian Y, Lingala SG, Vaz C, Sorensen T, Oh M, Harper S, Chen W, Lee Y, Töger J, Monteserin ML, Smith C, Godinez B, Goldstein L, Byrd D, Nayak KS, Narayanan SS. A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images. Sci Data 2021; 8:187. [PMID: 34285240 PMCID: PMC8292336 DOI: 10.1038/s41597-021-00976-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 06/22/2021] [Indexed: 12/11/2022] Open
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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Affiliation(s)
- Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Asterios Toutios
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Sajan Goud Lingala
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Colin Vaz
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Tanner Sorensen
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Miran Oh
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Sarah Harper
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Weiyi Chen
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Yoonjeong Lee
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Johannes Töger
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Mairym Lloréns Monteserin
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Caitlin Smith
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Bianca Godinez
- Department of Linguistics, California State University Long Beach, Long Beach, California, USA
| | - Louis Goldstein
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Dani Byrd
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Shrikanth S Narayanan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA.
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Feng X, Wang Z, Meyer CH. Real-time dynamic vocal tract imaging using an accelerated spiral GRE sequence and low rank plus sparse reconstruction. Magn Reson Imaging 2021; 80:106-112. [PMID: 33957210 DOI: 10.1016/j.mri.2021.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/17/2021] [Accepted: 04/29/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE To develop a real-time dynamic vocal tract imaging method using an accelerated spiral GRE sequence and low rank plus sparse reconstruction. METHODS Spiral k-space sampling has high data acquisition efficiency and thus is suited for real-time dynamic imaging; further acceleration can be achieved by undersampling k-space and using a model-based reconstruction. Low rank plus sparse reconstruction is a promising method with fast computation and increased robustness to global signal changes and bulk motion, as the images are decomposed into low rank and sparse terms representing different dynamic components. However, the combination with spiral scanning has not been well studied. In this study an accelerated spiral GRE sequence was developed with an optimized low rank plus sparse reconstruction and compared with L1-SPIRiT and XD-GRASP methods. The off-resonance was also corrected using a Chebyshev approximation method to reduce blurring on a frame-by-frame basis. RESULTS The low rank plus sparse reconstruction method is sensitive to the weights of the low rank and sparse terms. The optimized reconstruction showed advantages over other methods with reduced aliasing and improved SNR. With the proposed method, spatial resolution of 1.3*1.3 mm2 with 150 mm field-of-view (FOV) and temporal resolution of 30 frames-per-second (fps) was achieved with good image quality. Blurring was reduced using the Chebyshev approximation method. CONCLUSION This work studies low rank plus sparse reconstruction using the spiral trajectory and demonstrates a new method for dynamic vocal tract imaging which can benefit studies of speech disorders.
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Affiliation(s)
- Xue Feng
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Craig H Meyer
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
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Martin J, Ruthven M, Boubertakh R, Miquel ME. Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract. J Imaging 2020; 6:86. [PMID: 34460743 PMCID: PMC8320850 DOI: 10.3390/jimaging6090086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 08/08/2020] [Accepted: 08/24/2020] [Indexed: 11/16/2022] Open
Abstract
Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.
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Affiliation(s)
- Joe Martin
- MR Physics, Guy’s and St Thomas’ NHS Foundation Trust, St Thomas’s Hospital, London SE1 7EH, UK;
| | - Matthieu Ruthven
- Clinical Physics, Barts Health NHS Trust, St Bartholomew’s Hospital, London EC1A 7BE, UK;
| | - Redha Boubertakh
- Singapore Bioimaging Consortium (SBIC), Singapore 138667, Singapore;
| | - Marc E. Miquel
- Clinical Physics, Barts Health NHS Trust, St Bartholomew’s Hospital, London EC1A 7BE, UK;
- Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre (BRC), William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
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9
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Arendt CT, Eichler K, Mack MG, Leithner D, Zhang S, Block KT, Berdan Y, Sader R, Wichmann JL, Gruber-Rouh T, Vogl TJ, Hoelter MC. Comparison of contrast-enhanced videofluoroscopy to unenhanced dynamic MRI in minor patients following surgical correction of velopharyngeal dysfunction. Eur Radiol 2020; 31:76-84. [PMID: 32740819 DOI: 10.1007/s00330-020-07098-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/02/2020] [Accepted: 07/21/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To compare dynamic magnetic resonance imaging (MRI) with videofluoroscopy (VFS) regarding image quality and assessment of gap size between soft palate (SP) and posterior pharyngeal wall (PPW) in children and adolescents following surgical correction of velopharyngeal dysfunction (VPD). METHODS Twenty-one patients undergoing unenhanced 3-T MRI and contrast-enhanced VFS were included in this IRB-approved prospective study. The MRI scan protocol comprised refocused gradient-echo sequences in transverse and sagittal planes during speech, with TE 1.97 ms, TR 3.95 ms, flip angle 8°, matrix size 128 × 128, and 5-mm slice thickness. Radial k-space sampling and sliding window reconstruction were used to achieve an image acquisition rate of 28 frames per second (fps). VFS with 30 fps was similarly performed in both planes. Closure of the velopharyngeal port during phonation was evaluated by two experienced radiologists. RESULTS Eleven (52.4%) patients displayed a complete closure, whereas ten (47.6%) patients showed a post-operative gap during speech. VFS and MRI equally identified the cases with persistent or recurrent VPD. Differences in SP-PPW distance between VFS (3.9 ± 1.6 mm) and MRI (4.1 ± 1.5 mm) were not statistically significant (p = 0.5). The subjective overall image quality of MRI was rated inferior (p < 0.001) compared with VFS, with almost perfect inter-rater agreement (κ = 0.90). The presence of susceptibility artifacts did not limit anatomical measurements. CONCLUSION Dynamic MRI is equally reliable as VFS to assess persistent or recurrent inadequate velum closure in patients following surgical treatment of VPD. KEY POINTS • Unenhanced 3-T dynamic MRI and contrast-enhanced videofluoroscopy are equally useful for the identification of patients with incomplete velopharyngeal closure during speech. • MRI using refocused gradient-echo acquisition with radial k-space sampling and sliding window reconstruction generates diagnostic images with 28 frames per second. • MRI can offer a radiation-free alternative to currently established videofluoroscopy for young patients.
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Affiliation(s)
- C T Arendt
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany.
| | - K Eichler
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - M G Mack
- Radiology Munich, Munich, Germany
| | - D Leithner
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - S Zhang
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - K T Block
- Department of Radiology, University Hospital Basel, Basel, Switzerland
- Department of Radiology, New York University Langone Health, New York, NY, USA
| | - Y Berdan
- Department of Oral, Cranio-Maxillofacial and Facial Plastic Surgery, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - R Sader
- Department of Oral, Cranio-Maxillofacial and Facial Plastic Surgery, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - J L Wichmann
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - T Gruber-Rouh
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - T J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - M C Hoelter
- Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt am Main, Germany
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10
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Pelland CM, Feng X, Borowitz KC, Meyer CH, Blemker SS. A Dynamic Magnetic Resonance Imaging-Based Method to Examine In Vivo Levator Veli Palatini Muscle Function During Speech. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2019; 62:2713-2722. [PMID: 31390279 PMCID: PMC6802907 DOI: 10.1044/2019_jslhr-s-18-0459] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/19/2019] [Accepted: 04/27/2019] [Indexed: 06/10/2023]
Abstract
Purpose The aim of this study was to develop a method able to quantify levator veli palatini (LVP) muscle shortening and contraction velocities using dynamic magnetic resonance imaging (MRI) throughout speech samples and relate these measurements to velopharyngeal portal dimensions. Method Six healthy adults (3 men and 3 women, M = 24.5 years) produced syllables representing 4 different manners of production during real-time dynamic MRI scans. We acquired an oblique-coronal slice of the velopharyngeal mechanism, which captured the length of the LVP, and manually segmented each frame. LVP shortening and muscle velocities were calculated from the acquired images. Results Using our method, we found that subjects demonstrated greater LVP shortening and higher maximum contraction velocities during fricative and plosive syllable production than during nasal or vowel syllable production. LVP shortening and maximum contraction velocity positively correlated with velopharyngeal port depth. Conclusions In vivo LVP function differs between manners of production, as expected, and an individual's velopharyngeal portal dimensions influence LVP function. These measures, contextualized with the force-length and force-velocity muscle relationships, provide new insight into LVP function. Future studies could use this method to investigate LVP function in healthy speakers and individuals with velopharyngeal dysfunction and how function relates to velopharyngeal anatomy.
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Affiliation(s)
- Catherine M. Pelland
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville
| | - Xue Feng
- Department of Biomedical Engineering, University of Virginia, Charlottesville
| | - Kathleen C. Borowitz
- Department of Rehabilitation Therapy Services, Speech-Language Pathology, University of Virginia Health System, Charlottesville
| | - Craig H. Meyer
- Department of Biomedical Engineering, University of Virginia, Charlottesville
- Department of Radiology, University of Virginia, Charlottesville
| | - Silvia S. Blemker
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville
- Department of Biomedical Engineering, University of Virginia, Charlottesville
- Department of Orthopaedic Surgery, University of Virginia, Charlottesville
- Department of Ophthalmology, University of Virginia, Charlottesville
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Ruthven M, Freitas AC, Boubertakh R, Miquel ME. Application of radial GRAPPA techniques to single- and multislice dynamic speech MRI using a 16-channel neurovascular coil. Magn Reson Med 2019; 82:948-958. [PMID: 31016802 DOI: 10.1002/mrm.27779] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/07/2019] [Accepted: 03/29/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate: (1) the feasibility of using through-time radial GeneRalized Autocalibrating Partially Parallel Acquisitions (rGRAPPA) and hybrid radial GRAPPA (h-rGRAPPA) in single- and multislice dynamic speech MRI; (2) whether single-slice dynamic speech MRI at a rate of 15 frames per second (fps) or higher and with adequate image quality can be achieved using these radial GRAPPA techniques. METHODS Seven healthy adult volunteers were imaged at 3T using a 16-channel neurovascular coil and 2 spoiled gradient echo sequences (radial trajectory, field of view = 192 × 192 mm2 , acquired pixel size = 2.4 × 2.4 mm2 ). One sequence imaged a single slice at 16.8 fps, the other imaged 2 interleaved slices at 7.8 fps per slice. Image sets were reconstructed using rGRAPPA and h-rGRAPPA, and their image quality was compared using the root mean square error, structural similarity index, and visual assessments. RESULTS Image quality deteriorated when fewer than 170 calibration frames were used in the rGRAPPA reconstruction. rGRAPPA image sets demonstrated: (1) in 97% of cases, a similar image quality to h-rGRAPPA image sets reconstructed using a k-space segment size of 4, (2) in 98% of cases, a better image quality than h-rGRAPPA image sets reconstructed using a k-space segment size of 32. CONCLUSION This study confirmed: (1) the feasibility of using rGRAPPA and h-rGRAPPA in single- and multislice dynamic speech MRI, (2) that single-slice speech imaging at a frame rate higher than 15 fps and with adequate image quality can be achieved using these radial GRAPPA techniques.
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Affiliation(s)
- Matthieu Ruthven
- Clinical Physics, Barts Health NHS Trust, St Bartholomew's Hospital, London, United Kingdom
| | - Andreia C Freitas
- William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, United Kingdom.,ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal
| | - Redha Boubertakh
- Clinical Physics, Barts Health NHS Trust, St Bartholomew's Hospital, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, United Kingdom
| | - Marc E Miquel
- Clinical Physics, Barts Health NHS Trust, St Bartholomew's Hospital, London, United Kingdom.,William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, United Kingdom
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Kim YC. Fast upper airway magnetic resonance imaging for assessment of speech production and sleep apnea. PRECISION AND FUTURE MEDICINE 2018. [DOI: 10.23838/pfm.2018.00100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
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13
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Dietz B, Fallone BG, Wachowicz K. Nomenclature for real‐time magnetic resonance imaging. Magn Reson Med 2018; 81:1483-1484. [DOI: 10.1002/mrm.27487] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 12/15/2022]
Affiliation(s)
- Bryson Dietz
- Division of Medical Physics, Department of Oncology University of Alberta, Cross Cancer Institute Edmonton Canada
| | - B. Gino Fallone
- Department of Medical Physics Cross Cancer Institute Edmonton Canada
- Departments of Oncology and Physics University of Alberta Edmonton Canada
| | - Keith Wachowicz
- Department of Medical Physics Cross Cancer Institute Edmonton Canada
- Departments of Oncology and Physics University of Alberta Edmonton Canada
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