The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

J Penttila - One of the best experts on this subject based on the ideXlab platform.

  • hybrid ultra low field mri and magnetoencephalography system based on a commercial whole head neuromagnetometer
    Magnetic Resonance in Medicine, 2013
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, Juhani Dabek, Jaakko O. Nieminen, J Penttila
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

  • Hybrid ultra‐low‐field MRI and magnetoencephalography system based on a commercial whole‐head neuromagnetometer
    Magnetic Resonance in Medicine, 2012
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, J Penttila, Juhani Dabek, Jaakko O. Nieminen, Juha Simola
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Andrey Zhdanov - One of the best experts on this subject based on the ideXlab platform.

  • hybrid ultra low field mri and magnetoencephalography system based on a commercial whole head neuromagnetometer
    Magnetic Resonance in Medicine, 2013
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, Juhani Dabek, Jaakko O. Nieminen, J Penttila
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

  • Hybrid ultra‐low‐field MRI and magnetoencephalography system based on a commercial whole‐head neuromagnetometer
    Magnetic Resonance in Medicine, 2012
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, J Penttila, Juhani Dabek, Jaakko O. Nieminen, Juha Simola
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Ahmed H. Tewfik - One of the best experts on this subject based on the ideXlab platform.

  • Fast dynamic magnetic resonance imaging using tagging RF pulses
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Vimal Singh, Ahmed H. Tewfik
    Abstract:

    A critical requirement for dynamic magnetic resonance imaging (MRI) is to reduce image acquisition times while maintaining high spatial resolutions to capture the underlying process with high-information rates. This paper presents a sparse Signal recovery based fast MRI method which uses: 1) dictionary learning for sparse representation of Signals for Encoding of data redundancy in physiological functions and, 2) a tagging radio-frequency pulses based novel MR Signal Encoding formulation to uniformly sample the k-space, even at high acceleration factors. The preliminary results of dynamic MR image recovery experiments using tagging based MR Signal acquisition method on an in-vivo myocardial perfusion dataset outperforms the equivalent dynamic MRI method implemented with variable density k-space under-sampling.

  • Sparse magnetic resonance imaging using tagging RF pulses
    2013 IEEE 10th International Symposium on Biomedical Imaging, 2013
    Co-Authors: Vimal Singh, Ahmed H. Tewfik
    Abstract:

    Few fast magnetic resonance (MR) imaging techniques have proposed modifications to the MR Signal Encoding formulation in order to improve the performance guarantees of image recovery using compressed sensing. A limitation of the previously proposed Encoding formulations is their difficult realization on the physical hardware. The deviation of realizable formulation from the theoretical model leads to operating characteristics which are clinically infeasible. In this paper, a novel MR Signal Encoding formulation using tagging radio-frequency pulses is proposed. The proposed formulation uses tagging pulses to uniquely modulate the longitudinal magnetization in the field-of-view for each MR excitation. The modulation of magnetization leads to mixing of information in the spatial Fourier space which improves the incoherence between the sensing and the sparsifying basis. The physical realization of the proposed formulation is promising due to the use of clinically active RF pulses. The preliminary results for image recovery experiments using the proposed formulation on an in-vivo dataset are comparably close and at times better than the results of the difficult-to-realize state-of-the-art formulation.

Panu T Vesanen - One of the best experts on this subject based on the ideXlab platform.

  • hybrid ultra low field mri and magnetoencephalography system based on a commercial whole head neuromagnetometer
    Magnetic Resonance in Medicine, 2013
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, Juhani Dabek, Jaakko O. Nieminen, J Penttila
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

  • Hybrid ultra‐low‐field MRI and magnetoencephalography system based on a commercial whole‐head neuromagnetometer
    Magnetic Resonance in Medicine, 2012
    Co-Authors: Panu T Vesanen, Koos C J Zevenhoven, Lauri Parkkonen, Andrey Zhdanov, Juho Luomahaara, Juha Hassel, J Penttila, Juhani Dabek, Jaakko O. Nieminen, Juha Simola
    Abstract:

    Ultra-low-field MRI uses microtesla fields for Signal Encoding and sensitive superconducting quantum interference devices for Signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG-MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG-MRI measurements are described. The performance of the device is demonstrated by presenting ultra-low-field-MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

H.c. Papadopoulos - One of the best experts on this subject based on the ideXlab platform.

  • Sequential Signal Encoding and estimation for distributed sensor networks
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001
    Co-Authors: M.m. Abdallah, H.c. Papadopoulos
    Abstract:

    We develop algorithms for sequential Signal Encoding from sensor measurements, and for Signal estimation via fusion of channel-corrupted versions of these Encodings. For Signals described by state space models, we present optimized sequential binary-valued Encodings constructed via threshold-controlled scalar quantization of a running Kalman filter Signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these Encodings corrupted by binary symmetric channels.

  • ICASSP - Sequential Signal Encoding and estimation for distributed sensor networks
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001
    Co-Authors: Mohamed Abdallah, H.c. Papadopoulos
    Abstract:

    We develop algorithms for sequential Signal Encoding from sensor measurements, and for Signal estimation via fusion of channel-corrupted versions of these Encodings. For Signals described by state space models, we present optimized sequential binary-valued Encodings constructed via threshold-controlled scalar quantization of a running Kalman filter Signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these Encodings corrupted by binary symmetric channels.

  • Sequential Signal Encoding from noisy measurements using quantizers with dynamic bias control
    IEEE Transactions on Information Theory, 2001
    Co-Authors: H.c. Papadopoulos, G.w. Wornell, A.v. Oppenheim
    Abstract:

    Signal estimation from a sequential Encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an information-bearing Signal from low-bandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before Signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedback-based control inputs, resulting in a hierarchy of systems that trade performance for complexity.

  • Sequential Signal Encoding and estimation for wireless sensor networks
    2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060), 2000
    Co-Authors: H.c. Papadopoulos
    Abstract:

    We develop extensions to our techniques in Papadopoulos et al. for Signal estimation from sequential Encodings in the form of quantized measurements communicated over binary symmetric channels. We show that the channel quality affects not only the quality of the Encoding but also its optimality. We also construct Encodings from optimized pseudo-noise and feedback-based control inputs, and efficient Signal estimators from channel corrupted versions of the Encodings.