Feasibility of through-time spiral generalized autocalibrating partial parallel acquisition for low latency accelerated real-time MRI of speech

Sajan Goud Lingala; Yinghua Zhu; Yongwan Lim; Asterios Toutios; Yunhua Ji; Wei-Ching Lo; Nicole Seiberlich; Shrikanth Narayanan; Krishna S Nayak

doi:10.1002/mrm.26611

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Feasibility of through-time spiral generalized autocalibrating partial parallel acquisition for low latency accelerated real-time MRI of speech

Journal article

Peer reviewed

Feasibility of through-time spiral generalized autocalibrating partial parallel acquisition for low latency accelerated real-time MRI of speech

Sajan Goud Lingala, Yinghua Zhu, Yongwan Lim, Asterios Toutios, Yunhua Ji, Wei-Ching Lo, Nicole Seiberlich, Shrikanth Narayanan and Krishna S Nayak

Magnetic resonance in medicine, Vol.78(6), pp.2275-2282

12/2017

DOI: 10.1002/mrm.26611

PMCID: PMC6590694

PMID: 28185301

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Abstract

To evaluate the feasibility of through-time spiral generalized autocalibrating partial parallel acquisition (GRAPPA) for low-latency accelerated real-time MRI of speech. Through-time spiral GRAPPA (spiral GRAPPA), a fast linear reconstruction method, is applied to spiral (k-t) data acquired from an eight-channel custom upper-airway coil. Fully sampled data were retrospectively down-sampled to evaluate spiral GRAPPA at undersampling factors R = 2 to 6. Pseudo-golden-angle spiral acquisitions were used for prospective studies. Three subjects were imaged while performing a range of speech tasks that involved rapid articulator movements, including fluent speech and beat-boxing. Spiral GRAPPA was compared with view sharing, and a parallel imaging and compressed sensing (PI-CS) method. Spiral GRAPPA captured spatiotemporal dynamics of vocal tract articulators at undersampling factors ≤4. Spiral GRAPPA at 18 ms/frame and 2.4 mm /pixel outperformed view sharing in depicting rapidly moving articulators. Spiral GRAPPA and PI-CS provided equivalent temporal fidelity. Reconstruction latency per frame was 14 ms for view sharing and 116 ms for spiral GRAPPA, using a single processor. Spiral GRAPPA kept up with the MRI data rate of 18ms/frame with eight processors. PI-CS required 17 minutes to reconstruct 5 seconds of dynamic data. Spiral GRAPPA enabled 4-fold accelerated real-time MRI of speech with a low reconstruction latency. This approach is applicable to wide range of speech RT-MRI experiments that benefit from real-time feedback while visualizing rapid articulator movement. Magn Reson Med 78:2275-2282, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Algorithms

Artifacts

Calibration

Epiglottis - diagnostic imaging

Humans

Image Enhancement

Image Processing, Computer-Assisted

Larynx - diagnostic imaging

Magnetic Resonance Imaging

Models, Statistical

Pharynx - diagnostic imaging

Prospective Studies

Reproducibility of Results

Retrospective Studies

Sensitivity and Specificity

Signal Processing, Computer-Assisted

Software

Speech

Details

Title: Subtitle: Feasibility of through-time spiral generalized autocalibrating partial parallel acquisition for low latency accelerated real-time MRI of speech
Creators: Sajan Goud Lingala - University of Southern California
Yinghua Zhu - University of Southern California
Yongwan Lim - University of Southern California
Asterios Toutios - University of Southern California
Yunhua Ji - University of Southern California
Wei-Ching Lo - Case Western Reserve University
Nicole Seiberlich - Case Western Reserve University
Shrikanth Narayanan - University of Southern California
Krishna S Nayak - University of Southern California
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.78(6), pp.2275-2282
DOI: 10.1002/mrm.26611
PMID: 28185301
PMCID: PMC6590694
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Grant note: R01 HL094557 / NHLBI NIH HHS R01 DC007124 / NIDCD NIH HHS R01 DK098503 / NIDDK NIH HHS
Language: English
Date published: 12/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984196991402771

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