Review of recent research on hepatitis C therapy for 54th annual meeting of the American association for the study of liver diseases

Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k-space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact-free images from either the aliased images (SENSE-type reconstruction) or from the undersampled data (GRAPPA-type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath-hold times resulting in fewer motion-corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.23639

Parallel MR imaging.

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

/pdf/image-denoising-in-mixed-poisson-gaussian-noise-1npx33n1mn.pdf

Image Denoising in Mixed Poisson–Gaussian Noise

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

USC-TIMIT is an extensive database of multimodal speech production data, developed to complement existing resources available to the speech research community and with the intention of being continuously refined and augmented. The database currently includes real-time magnetic resonance imaging data from five male and five female speakers of American English. Electromagnetic articulography data have also been presently collected from four of these speakers. The two modalities were recorded in two independent sessions while the subjects produced the same 460 sentence corpus used previously in the MOCHA-TIMIT database. In both cases the audio signal was recorded and synchronized with the articulatory data. The database and companion software are freely available to the research community.

/pdf/real-time-magnetic-resonance-imaging-and-electromagnetic-3l5611p3kt.pdf

Real-time magnetic resonance imaging and electromagnetic articulography database for speech production research (TC)

Understanding human speech production is of great interest from engineering, linguistic, and several other research points of view. While several types of data available to speech understanding studies lead to different avenues for research, in this article we focus on real-time (RT) magnetic resonance imaging (MRI) as an emerging technique for studying speech production. We discuss the details and challenges of RT magnetic resonance (MR) acquisition and analysis, and modeling approaches that make use of MRI data for studying speech production. MOTIVATION From an engineer’s point of view, detailed knowledge about speech production gives rise to refined models for the speech signal that can be exploited for the design of powerful speech recognition, coding, and synthesis systems. From a linguist’s point of view, speech research may be conducted to address open questions in the areas of phonetics and phonology. These include: 1) what articulatory mechanisms explain the inter- and intrasubject variability of speech, 2) what aspects of the vocal tract shaping are critically controlled by the brain for conveying meaning and emotions, and 3) how does prosody affect the articulatory timing. From other research points of view, speech production is important to understand language acquisition and language disorders. All of these efforts require intimate knowledge of the speech generation mechanisms. Different types of data are available to the speech researcher—from audio and video recordings of speech production to muscle activity data produced by electromyography, respiratory data from subglottal or interoral pressure transduction, and images of the larynx obtained through video laryngoscopy. While the vocal tract posture and movement can be investigated using a host of techniques summarized in Table 1 including X ray (microbeam), cinefluography, ultrasound, palatography, electromagnetometry (EMA), RT-MRI has a particular advantage in that it produces complete views of the entire vocal tract including the pharyngeal structures in a safe and noninvasive manner. With RT-MRI, a midsaggital image of the vocal tract from the glottis (bottom) to the lips (left) can be acquired as illustrated in Figure 1(a). In this image, we can trace the air-tissue boundaries of the anatomical components that are of interest to the speech researcher and obtain a representation similar to Figure 1(b). These components, also known as articulators, are controlled by the brain during speech production and are used to change the shape of the vocal tract tube. With it, they also change the filter function for the excitation signal generated at the glottis and elsewhere along the airway. Hence the motion of the articulators shapes the sounds of speech and other human vocalizations. The signal processing challenges when studying these using RT-MRI lie in the fast acquisition of high-quality RT MRI images including simultaneous noise-robust audio recording [1], the subsequent detection of the relevant features from each image, and the analysis and modeling of the time-varying vocal tract shape for the purpose of gaining deeper understanding of the underlying principles that govern the speech production process.

/pdf/seeing-speech-capturing-vocal-tract-shaping-using-real-time-17nyqmx04l.pdf

Seeing Speech: Capturing Vocal Tract Shaping Using Real-Time Magnetic Resonance Imaging

In speech-production research, three-dimensional (3D) MRI of the upper airway has provided insights into vocal tract shaping and data for its modeling. Small movements of articulators can lead to large changes in the produced sound, therefore improving the resolution of these data sets, within the constraints of a sustained speech sound (6 –12 s), is an important area for investigation. The purpose of the study is to provide a first application of compressed sensing (CS) to high-resolution 3D upper airway MRI using spatial finite difference as the sparsifying transform, and to experimentally determine the benefit of applying constraints on image phase. Estimates of image phase are incorporated into the CS reconstruction to improve the sparsity of the finite difference of the solution. In a retrospective subsampling experiment with no sound production, 5 and 4 were the highest acceleration factors that produced acceptable image quality when using a phase constraint and when not using a phase constraint, respectively. The prospective use of a 5 undersampled acquisition and phaseconstrained CS reconstruction enabled 3D vocal tract MRI during sustained sound production of English consonants /s/, //, /l/, and /r/ with 1.5 1.5 2.0 mm 3 spatial resolution an d7so fscan

/pdf/accelerated-three-dimensional-upper-airway-mri-using-2d32up8v4z.pdf

Accelerated three-dimensional upper airway MRI using compressed sensing.

In this paper real-time (RT) magnetic resonance imaging (MRI) is used to study speech production especially capturing vocal tract shaping.

Seeing speech: Capturing vocal tract shaping using real-time magnetic resonance imaging [Exploratory DSP]

Purpose
The aim of this work was to develop and evaluate an MRI-based system for study of dynamic vocal tract shaping during speech production, which provides high spatial and temporal resolution.

Methods
The proposed system utilizes (a) custom eight-channel upper airway coils that have high sensitivity to upper airway regions of interest, (b) two-dimensional golden angle spiral gradient echo acquisition, (c) on-the-fly view-sharing reconstruction, and (d) off-line temporal finite difference constrained reconstruction. The system also provides simultaneous noise-cancelled and temporally aligned audio. The system is evaluated in 3 healthy volunteers, and 1 tongue cancer patient, with a broad range of speech tasks.

Results
We report spatiotemporal resolutions of 2.4 × 2.4 mm2 every 12 ms for single-slice imaging, and 2.4 × 2.4 mm2 every 36 ms for three-slice imaging, which reflects roughly 7-fold acceleration over Nyquist sampling. This system demonstrates improved temporal fidelity in capturing rapid vocal tract shaping for tasks, such as producing consonant clusters in speech, and beat-boxing sounds. Novel acoustic-articulatory analysis was also demonstrated.

Conclusion
A synergistic combination of custom coils, spiral acquisitions, and constrained reconstruction enables visualization of rapid speech with high spatiotemporal resolution in multiple planes. Magn Reson Med 77:112–125, 2017. © 2016 Wiley Periodicals, Inc.

Yoon-Chul Kim

Papers

Real-time magnetic resonance imaging and electromagnetic articulography database for speech production research (TC)

Seeing Speech: Capturing Vocal Tract Shaping Using Real-Time Magnetic Resonance Imaging

Accelerated three-dimensional upper airway MRI using compressed sensing.

Seeing speech: Capturing vocal tract shaping using real-time magnetic resonance imaging [Exploratory DSP]

A fast and flexible MRI system for the study of dynamic vocal tract shaping.