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34 results on '"Karyna Isaieva"'

1. Performance Study of an MRI Motion-Compensated Reconstruction Program on Intel CPUs, AMD EPYC CPUs, and NVIDIA GPUs

Author

Mohamed Aziz Zeroual, Karyna Isaieva, Pierre-André Vuissoz, and Freddy Odille

Subjects
magnetic resonance imaging, reconstruction, motion, high-performance computing, parallel processing, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance Imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled Systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS’s performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel Xeon Gold and AMD EPYC. The roofline model is used to study the software–hardware interaction and quantify the code’s performance. For CPU–GPU comparison purposes, we propose a preliminary MATLAB–GPU implementation of the GRICS’s reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTX 5000 and A100. After the performance study, we propose some optimization patterns for the code’s execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI–OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel’s execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization.

Published
2024
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2. Super-Resolved Dynamic 3D Reconstruction of the Vocal Tract during Natural Speech

Author

Karyna Isaieva, Freddy Odille, Yves Laprie, Guillaume Drouot, Jacques Felblinger, and Pierre-André Vuissoz

Subjects
magnetic resonance imaging, super-resolution, vocal tract, dynamic MRI, speech, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

MRI is the gold standard modality for speech imaging. However, it remains relatively slow, which complicates imaging of fast movements. Thus, an MRI of the vocal tract is often performed in 2D. While 3D MRI provides more information, the quality of such images is often insufficient. The goal of this study was to test the applicability of super-resolution algorithms for dynamic vocal tract MRI. In total, 25 sagittal slices of 8 mm with an in-plane resolution of 1.6 × 1.6 mm2 were acquired consecutively using a highly-undersampled radial 2D FLASH sequence. The volunteers were reading a text in French with two different protocols. The slices were aligned using the simultaneously recorded sound. The super-resolution strategy was used to reconstruct 1.6 × 1.6 × 1.6 mm3 isotropic volumes. The resulting images were less sharp than the native 2D images but demonstrated a higher signal-to-noise ratio. It was also shown that the super-resolution allows for eliminating inconsistencies leading to regular transitions between the slices. Additionally, it was demonstrated that using visual stimuli and shorter text fragments improves the inter-slice consistency and the super-resolved image sharpness. Therefore, with a correct speech task choice, the proposed method allows for the reconstruction of high-quality dynamic 3D volumes of the vocal tract during natural speech.

Published
2023
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3. Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers

Author

Karyna Isaieva, Yves Laprie, Justine Leclère, Ioannis K. Douros, Jacques Felblinger, and Pierre-André Vuissoz

Subjects
Science
Abstract

Measurement(s) Vocal tract images • Speech Technology Type(s) Magnetic Resonance Imaging • Microphone Device Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16404453

Published
2021
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4. Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach

Author

Karyna Isaieva, Yves Laprie, Nicolas Turpault, Alexis Houssard, Jacques Felblinger, and Pierre-André Vuissoz

Subjects
Electronic computers. Computer science, QA75.5-76.95, Cybernetics, Q300-390
Abstract

Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present both intra- and inter-subject validation. We used real-time magnetic resonance images and manually annotated 1-pixel wide contours as inputs. Predicted probability maps were post-processed in order to obtain 1-pixel wide tongue contours. The results are very good and slightly outperform published results on automatic tongue segmentation.

Published
2020
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6. 3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant–Vowel Production from 2D Real Time MRI

Author

Ioannis K. Douros, Yu Xie, Chrysanthi Dourou, Karyna Isaieva, Pierre-André Vuissoz, Jacques Felblinger, and Yves Laprie

Subjects
spatiotemporal atlas, generic speaker model, adaptive gaussian kernel, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space.

Published
2022
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7. Intracellular Detection and Localization of Nanoparticles by Refractive Index Measurement

Author

Alain Géloën, Karyna Isaieva, Mykola Isaiev, Olga Levinson, Emmanuelle Berger, and Vladimir Lysenko

Subjects
label-free imaging, refractive index, nanoparticle, quantitative image analysis, real-time analysis, single cell analysis, Chemical technology, TP1-1185
Abstract

The measuring of nanoparticle toxicity faces an important limitation since it is based on metrics exposure, the concentration at which cells are exposed instead the true concentration inside the cells. In vitro studies of nanomaterials would benefit from the direct measuring of the true intracellular dose of nanoparticles. The objective of the present study was to state whether the intracellular detection of nanodiamonds is possible by measuring the refractive index. Based on optical diffraction tomography of treated live cells, the results show that unlabeled nanoparticles can be detected and localized inside cells. The results were confirmed by fluorescence measurements. Optical diffraction tomography paves the way to measuring the true intracellular concentrations and the localization of nanoparticles which will improve the dose-response paradigm of pharmacology and toxicology in the field of nanomaterials.

Published
2021
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8. Measurement of Tongue Tip Velocity from Real-Time MRI and Phase-Contrast Cine-MRI in Consonant Production

Author

Karyna Isaieva, Yves Laprie, Freddy Odille, Ioannis K. Douros, Jacques Felblinger, and Pierre-André Vuissoz

Subjects
velocity measurement, magnetic resonance imaging, articulatory motion, phase-contrast MRI, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

We evaluate velocity of the tongue tip with magnetic resonance imaging (MRI) using two independent approaches. The first one consists in acquisition with a real-time technique in the mid-sagittal plane. Tracking of the tongue tip manually and with a computer vision method allows its trajectory to be found and the velocity to be calculated as the derivative of the coordinate. We also propose to use another approach—phase contrast MRI—which enables velocities of the moving tissues to be measured directly. We recorded the sound simultaneously with the MR acquisition which enabled us to make conclusions regarding the relation between the movements and the sound. We acquired the data from two French-speaking subjects articulating /tata/. The results of both methods are in qualitative agreement and are consistent with other reviewer techniques used for evaluation of the tongue tip velocity.

Published
2020
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19. A hardware and software system for MRI applications requiring external device data

Author

Karyna Isaieva, Marc Fauvel, Nicolas Weber, Pierre‐André Vuissoz, Jacques Felblinger, Julien Oster, Freddy Odille, Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'Investigation Clinique - Innovation Technologique [Nancy] (CIC-IT), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-17-CE19-0022,Bracoil,Antenne adaptée pour l'IRM mammaire(2017), ANR-20-CE23-0008,Full3DTalkingHead,Tête parlante 3D complète avec simulations aéro-acoustiques(2020), ANR-19-ECVD-0004,MEIDIC-VTACH,Mobile Electro-Imaging for the preDICtion of Ventricular TACHyarrhythmia(2019), Isaieva, Karyna, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, Antenne adaptée pour l'IRM mammaire - - Bracoil2017 - ANR-17-CE19-0022 - AAPG2017 - VALID, Tête parlante 3D complète avec simulations aéro-acoustiques - - Full3DTalkingHead2020 - ANR-20-CE23-0008 - AAPG2020 - VALID, Mobile Electro-Imaging for the preDICtion of Ventricular TACHyarrhythmia - - MEIDIC-VTACH2019 - ANR-19-ECVD-0004 - ERA-CVD - VALID, and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Signal processing, Motion, [SPI]Engineering Sciences [physics], Hardware, Computer Systems, [SPI] Engineering Sciences [physics], Respiration, Physiological data, Humans, Radiology, Nuclear Medicine and imaging, Magnetic Resonance Imaging, Real-time, Software
Abstract

International audience; installation which might be impossible in some cases (e.g. pregnant women in lateral position, utilization of a tight flexible coil), and allows monitoring of only respiratory and heart activities. Moreover, being designed for clinical use, they offer no control over data acquisition and post-processing, which can be critical for some applications. For example, standard ECG processing can lead to incorrect QRS detection in case of steep gradient waveforms; no control over respiratory belt signal gain and offset (to avoid signal saturation) is available. Thus, often, accessory devices are thirdparty systems, which offer more flexibility, however have no communication interface with the MR system, or limited ones. Dedicated computer and electronics systems, as well as communication interfaces, are needed for use in either realtime/online applications or retrospective/offline applications. In the following we review several exemplary applications with the aim to derive typical requirements for such systems. Example applications that require real-time processing and communication include ECG denoising (7-10) and detection of the QRS complex (11-13), in order to trigger the MR sequence to the heart rate prospectively. The trigger is provided to the MR system by a TTL signal. The delays introduced by the real-time processing system, as defined by the latency (mean delay) and jitter (standard deviation of the delay), should be significantly lower that the temporal resolution of cardiac MR imaging, which is typically above 20 ms. Real-time display of the denoised ECG is also desired during anesthesia, cardiac stress MRI, or interventional MRI. Prospective motion correction (14) using external devices also relies on a real-time setup to modulate the MRI pulses (RF and magnetic field gradient pulses) in order to compensate for the effect of patient motion during spatial encoding. Low latency is also desired, ideally to be equal to the repetition time (TR) of the sequence or lower, i.e. 3 to 5 ms, though in practice it is limited by the time needed to process video images, e.g. 60 to 150 ms in (15). Software communication interfaces need to be defined with the vendor-dependent pulse sequence program in order to provide certain motion parameters (e.g. translations, rotations).

Published
2022
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21. Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers

Author

Ioannis Douros, Yves Laprie, Pierre-André Vuissoz, Jacques Felblinger, Karyna Isaieva, Justine Leclere, Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Centre Hospitalier Universitaire de Reims (CHU Reims), Centre d'Investigation Clinique - Innovation Technologique [Nancy] (CIC-IT), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Isaieva, Karyna

Subjects
Adult, Male, Statistics and Probability, Data Descriptor, Oral anatomy, Speech production, Computer science, Science, Speech recognition, Context (language use), Library and Information Sciences, 01 natural sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], 030218 nuclear medicine & medical imaging, Education, Young Adult, 03 medical and health sciences, Imaging, Three-Dimensional, 0302 clinical medicine, Vocal tract images, 0103 physical sciences, Humans, Speech, Segmentation, Articulatory gestures, 010301 acoustics, Language, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Communication, Middle Aged, Magnetic Resonance Imaging, Computer Science Applications, Metadata, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, France, Statistics, Probability and Uncertainty, Mr images, Articulation (phonetics), Vocal tract, Information Systems
Abstract

The study of articulatory gestures has a wide spectrum of applications, notably in speech production and recognition. Sets of phonemes, as well as their articulation, are language-specific; however, existing MRI databases mostly include English speakers. In our present work, we introduce a dataset acquired with MRI from 10 healthy native French speakers. A corpus consisting of synthetic sentences was used to ensure a good coverage of the French phonetic context. A real-time MRI technology with temporal resolution of 20 ms was used to acquire vocal tract images of the participants speaking. The sound was recorded simultaneously with MRI, denoised and temporally aligned with the images. The speech was transcribed to obtain phoneme-wise segmentation of sound. We also acquired static 3D MR images for a wide list of French phonemes. In addition, we include annotations of spontaneous swallowing., Measurement(s)Vocal tract images • SpeechTechnology Type(s)Magnetic Resonance Imaging • Microphone DeviceSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16404453

Published
2021
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22. Towards the prediction of the vocal tract shape from the sequence of phonemes to be articulated

Author

Karyna Isaieva, Pierre-André Vuissoz, Yves Laprie, Justine Leclere, Vinicius Ribeiro, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Souza Ribeiro, Vinicius de Paulo

Subjects
Speech production, Generalization, Computer science, speech production, Speech recognition, Articulator, 020206 networking & telecommunications, 02 engineering and technology, [INFO] Computer Science [cs], neural networks, Euclidean distance, 030507 speech-language pathology & audiology, 03 medical and health sciences, Position (vector), Data efficiency, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 0305 other medical science, Set (psychology), phoneme-to-articulatory, Vocal tract
Abstract

International audience; In this work, we address the prediction of speech articulators' temporal geometric position from the sequence of phonemes to be articulated. We start from a set of real-time MRI sequences uttered by a female French speaker. The contours of five articulators were tracked automatically in each of the frames in the MRI video. Then, we explore the capacity of a bidirectional GRU to correctly predict each articulator's shape and position given the sequence of phonemes and their duration. We propose a 5-fold cross-validation experiment to evaluate the generalization capacity of the model. In a second experiment, we evaluate our model's data efficiency by reducing training data. We evaluate the point-to-point Euclidean distance and the Pearson's correlations along time between the predicted and the target shapes. We also evaluate produced shapes of the critical articulators of specific phonemes. We show that our model can achieve good results with minimal data, producing very realistic vocal tract shapes.

Published
2021
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23. MRI Vocal Tract Sagittal Slices Estimation during Speech Production of CV

Author

Chrysanthi Dourou, Jacques Felblinger, Ajinkya Kulkarni, Pierre-André Vuissoz, Yu Xie, Yves Laprie, Karyna Isaieva, Ioannis Douros, Laprie, Yves, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Neurology, Wuhan University [China], School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
Speech production, Signal processing, speech resources enrichment, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Sagittal plane, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Set (abstract data type), RtMRI data, medicine.anatomical_structure, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], vocal tract, 0202 electrical engineering, electronic engineering, information engineering, medicine, Frame (artificial intelligence), 020201 artificial intelligence & image processing, Image transformation, Vocal tract
Abstract

International audience; In this paper we propose an algorithm for estimating vocal tract para sagittal slices in order to have a better overview of the behaviour of the articulators during speech production. The first step is to align the consonant-vowel (CV) data of the sagittal plains between them for the train speaker. Sets of transformations that connect the midsagittal frames with the neighbouring ones is acquired for the train speaker. Another set of transformations is calculated which transforms the midsagittal frames of the train speaker to the corresponding midsagittal frames of the test speaker and is used to adapt to the test speaker domain the previously computed sets of transformations. The newly adapted transformations are applied to the midsagittal frames of the test speaker in order to estimate the neighbouring sagittal frames. Several mono speaker models are combined to produce the final frame estimation. To evaluate the results, image cross-correlation between the original and the estimated frames was used. Results show good agreement between the original and the estimated frames.

Published
2021
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24. The Effect of X‐Ray Irradiation on Conductivity of C and 2 C Polytype TlInS 2 Ferroelectrics

Author

Karyna Isaieva, Yurii P. Gololobov, Mykola Borovyi, Mykola Isaiev, ISAIEV, Mykola, Taras Shevchenko National University of Kyiv, National Transport University, Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Laboratoire Énergies et Mécanique Théorique et Appliquée (LEMTA ), and Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010302 applied physics, [PHYS]Physics [physics], Materials science, electrical conductivity, ferroelectrics, Analytical chemistry, 02 engineering and technology, Conductivity, 021001 nanoscience & nanotechnology, Condensed Matter Physics, 01 natural sciences, Electronic, Optical and Magnetic Materials, incommensurate phases, [PHYS] Physics [physics], Electrical resistivity and conductivity, 0103 physical sciences, X ray irradiation, 0210 nano-technology, ComputingMilieux_MISCELLANEOUS, commensurate
Abstract

International audience

Published
2021
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25. Synthesize MRI vocal tract data during CV production

Author

Ioannis Douros, Chrysanthi Dourou, Yu Xie, jacques Felblinger, Karyna Isaieva, Pierre-André Vuissoz, Yves Laprie, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Department of Neurology, Wuhan University [China], Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Laprie, Yves

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience; A set of rtMR image transformations across time is computed during the production of CV that is afterwards applied to a new speaker in order to synthesize his/her CV pseudo rtMRI data. Synthesized images are compared with the original ones using image cross-correlation. 2 Purpose To be able to enlarge MRI speech corpus by synthesizing data.

Published
2020

26. Vocal tract sagittal slices estimation from MRI midsagittal slices during speech production of CV

Author

Ioannis Douros, Yu Xie, Chrysanthi Dourou, jacques Felblinger, Karyna Isaieva, Pierre-André Vuissoz, Yves Laprie, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Department of Neurology, Wuhan University [China], School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Laprie, Yves, Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience

Published
2020

27. Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach

Author

Alexis Houssard, Yves Laprie, Jacques Felblinger, Pierre-André Vuissoz, Nicolas Turpault, Karyna Isaieva, Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre d'Investigation Clinique - Innovation Technologique [Nancy] (CIC-IT), Centre d'investigation clinique [Nancy] (CIC), Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), and Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)

Subjects
0209 industrial biotechnology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Convolutional neural network, Task (project management), Mri image, 020901 industrial engineering & automation, Artificial Intelligence, Tongue, 0202 electrical engineering, electronic engineering, information engineering, medicine, Computer vision, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, ComputingMethodologies_COMPUTERGRAPHICS, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, medicine.anatomical_structure, Computer Science::Computer Vision and Pattern Recognition, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present both intra- and inter-subject validation. We used real-time magnetic resonance images and manually annotated 1-pixel wide contours as inputs. Predicted probability maps were post-processed in order to obtain 1-pixel wide tongue contours. The results are very good and slightly outperform published results on automatic tongue segmentation.

Published
2020
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28. Using Silence MR Image to Synthesise Dynamic MRI Vocal Tract Data of CV

Author

Chrysanthi Dourou, Pierre-André Vuissoz, Karyna Isaieva, Yu Xie, Jacques Felblinger, Ioannis Douros, Ajinkya Kulkarni, Yves Laprie, Laprie, Yves, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Department of Neurology, Wuhan University [China], Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, Frame (networking), 020206 networking & telecommunications, 02 engineering and technology, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Image (mathematics), Set (abstract data type), Silence, 030507 speech-language pathology & audiology, 03 medical and health sciences, image transformation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], vocal tract, Dynamic contrast-enhanced MRI, 0202 electrical engineering, electronic engineering, information engineering, Mr images, 0305 other medical science, Speech resources enrichment, Vocal tract, rtMRI data, pseudo rtMRI synthesis
Abstract

International audience; In this work we present an algorithm for synthesising pseudo rtMRI data of the vocal tract. rtMRI data on the midsagittal plane were used to synthesise target consonant-vowel (CV) using only a silence frame of the target speaker. For this purpose, several single speaker models were created. The input of the algorithm is a silence frame of both train and target speaker and the rtMRI data of the target CV. An image transformation is computed from each CV frame to the next one, creating a set of transformations that describe the dynamics of the CV production. Another image transformation is computed from the silence frame of train speaker to the silence frame of the target speaker and is used to adapt the set of transformations computed previously to the target speaker. The adapted set of transformations is applied to the silence of the target speaker tosynthesise his/her CV pseudo rtMRI data. Synthesised images from multiple single speaker models are frame aligned and then averaged to create the final version of synthesised images. Synthesised images are compared with the original ones using image cross-correlation. Results show good agreement between the synthesised and the original images.

Published
2020

29. A Multimodal Real-Time MRI Articulatory Corpus of French for Speech Research

Author

Arun A. Joseph, Jens Frahm, Dirk Voit, Yves Laprie, Karyna Isaieva, Freddy Odille, Ioannis Douros, Anastasiia Tsukanova, Jacques Felblinger, Pierre-André Vuissoz, Laprie, Yves, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Biomedizinische NMR Forschungs GmbH [Göttingen], Max-Planck-Institut für Biophysikalische Chemie - Max Planck Institute for Biophysical Chemistry [Göttingen], Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, and Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Larynx, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Speech production, Epiglottis, Computer science, speech production, Speech recognition, 02 engineering and technology, speech syn- thesis, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030507 speech-language pathology & audiology, 03 medical and health sciences, Tongue, 0202 electrical engineering, electronic engineering, information engineering, medicine, Index Terms: speech corpus, Spontaneous speech, multi-modal database, 020206 networking & telecommunications, Real-time MRI, medicine.anatomical_structure, Duration (music), French language, real-time MRI data, 0305 other medical science, Vocal tract, 3D MRI data
Abstract

International audience; In this work we describe the creation of ArtSpeechMRIfr: a real-time as well as static magnetic resonance imaging (rtMRI, 3D MRI) database of the vocal tract. The database contains also processed data: denoised audio, its phonetically aligned annotation, articulatory contours, and vocal tract volume information , which provides a rich resource for speech research. The database is built on data from two male speakers of French. It covers a number of phonetic contexts in the controlled part, as well as spontaneous speech, 3D MRI scans of sustained vocalic articulations, and of the dental casts of the subjects. The corpus for rtMRI consists of 79 synthetic sentences constructed from a phonetized dictionary that makes possible to shorten the duration of acquisitions while keeping a very good coverage of the phonetic contexts which exist in French. The 3D MRI includes acquisitions for 12 French vowels and 10 consonants, each of which was pronounced in several vocalic contexts. Ar-ticulatory contours (tongue, jaw, epiglottis, larynx, velum, lips) as well as 3D volumes were manually drawn for a part of the images.

Published
2019

30. Stochastic phase slips in toroidal Bose-Einstein condensates

Author

Karyna Isaieva, Alexander Yakimenko, Kyrylo Snizhko, Yuriy Bidasyuk, S. I. Vilchinskii, and Yevhenii Kuriatnikov

Subjects
Physics, Toroid, Phase (waves), FOS: Physical sciences, 01 natural sciences, 010305 fluids & plasmas, law.invention, law, Quantum Gases (cond-mat.quant-gas), 0103 physical sciences, Statistical physics, 010306 general physics, Condensed Matter - Quantum Gases, Bose–Einstein condensate, Energy (signal processing)
Abstract

Motivated by recent experiments we study the influence of thermal noise on the phase slips in toroidal Bose-Einstein condensates with a rotating weak link. We derive a generalized Arrhenius-like expression for the rate of stochastic phase slips. We develop a method to estimate the energy barrier separating different superflow states. The parameters at which the energy barrier disappears agree with the critical parameters for deterministic phase slips obtained from dynamics simulations, which confirms the validity of our energetic analysis. We reveal that adding thermal noise lowers the phase-slip threshold. However, the quantitative impact of the stochastic phase slips turns out to be too small to explain the significant discrepancy between theoretical and the experimental results., 16 pages, 9 figures

Published
2016

31. Vortex excitation in a stirred toroidal Bose-Einstein condensate

Author

Alexander Yakimenko, S. I. Vilchinskii, Karyna Isaieva, and Elena A. Ostrovskaya

Subjects
Physics, Condensed Matter::Quantum Gases, Toroid, Condensed matter physics, FOS: Physical sciences, Persistent current, Pattern Formation and Solitons (nlin.PS), Critical value, Nonlinear Sciences - Pattern Formation and Solitons, Atomic and Molecular Physics, and Optics, Vortex, law.invention, Physics::Fluid Dynamics, Quantum Gases (cond-mat.quant-gas), law, Annulus (firestop), Condensed Matter - Quantum Gases, Beam (structure), Excitation, Bose–Einstein condensate
Abstract

Motivated by the recent experiment [Wright et al., Phys. Rev. A 88, 063633 (2013)], we investigate formation of vortices in an annular BEC stirred by a narrow blue-detuned optical beam. In the framework of a two-dimensional mean field model, we study the dissipative dynamics of the condensate with parameters matched to the experimental conditions. Vortex-antivortex pairs appear near the center of the stirrer in the bulk of the condensate for slow motion of the stirring beam. When the barrier angular velocity is above some critical value, an outer edge surface mode develops and breaks into the vortices entering the condensate annulus. We determine the conditions for creation of the vortex excitations in the stirred toroidal condensate and compare our results with the experimental observations., 7 pages 5 figures

Published
2014

32. Stability of persistent currents in spinor Bose-Einstein condensates

Author

S. I. Vilchinskii, Michael Weyrauch, Karyna Isaieva, and Alexander Yakimenko

Subjects
Condensed Matter::Quantum Gases, Physics, Spinor, Component (thermodynamics), FOS: Physical sciences, High density, Stability (probability), Atomic and Molecular Physics, and Optics, law.invention, Vortex, Quantum Gases (cond-mat.quant-gas), law, Quantum mechanics, Dissipative system, Annulus (firestop), Condensed Matter - Quantum Gases, Bose–Einstein condensate
Abstract

Motivated by a recent experiment [S. Beattie, S. Moulder, R. J. Fletcher, and Z. Hadzibabic, PRL 110, 025301 (2013)] we study the superflow of atomic spinor Bose-Einstein condensates optically trapped in a ring-shaped geometry. Within a dissipative mean-field approach we simulate a two-component condensate in conditions adapted to the experiment. In qualitative agreement with the experimental findings, we observe persistent currents, if the spin-population imbalance is above some well-defined `critical' value. The triply charged vortices decay in quantized steps. The vortex lines escape from the center of the ring through dynamically created regions in the condensate annulus with reduced density of one component filled by atoms of the other component. The vortices then leave the ring-shaped high density region of the condensate and finally decay into elementary excitations., Comment: 5 pages, 3 figures

Published
2013
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33. Tracking the tongue contours in rt-MRI films with an autoencoder DNN approach

Author

Karyna Isaieva, Yves Laprie, Alexis Houssard, jacques Felblinger, Pierre-André Vuissoz, Laprie, Yves, Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

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