The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform

Xiaobo Qu - One of the best experts on this subject based on the ideXlab platform.

  • Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy
    IEEE Transactions on Biomedical Engineering, 2018
    Co-Authors: Hengfa Lu, Jiaxi Ying, Xinlin Zhang, Zhong Chen, Jian Yang, Xiaobo Qu
    Abstract:

    Goal: The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing Signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency Signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm. Results: Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential Signal Property serves as an useful tool to model the time-domain MRS Signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane. Significance: Low-intensity Signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover Signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., Signal enhancement, feature extraction, and fast sampling.

  • EMBC - Accelerated magnetic resonance spectroscopy with Vandermonde factorization
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2017
    Co-Authors: Xiaobo Qu, Jiaxi Ying, Zhong Chen
    Abstract:

    Multi-dimensional magnetic resonance spectroscopy is an important tool for studying molecular structures, interactions and dynamics in bio-engineering. The data acquisition time, however, is relatively long and non-uniform sampling can be applied to reduce this time. To obtain the full spectrum,a reconstruction method with Vandermonde factorization is proposed. This method explores the general Signal Property in magnetic resonance spectroscopy: Its time domain Signal is approximated by a sum of a few exponentials. Results on synthetic and realistic data show that the new approach can achieve faithful spectrum reconstruction and outperforms state-of-the-art low rank Hankel matrix method.

Zhong Chen - One of the best experts on this subject based on the ideXlab platform.

  • Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy
    IEEE Transactions on Biomedical Engineering, 2018
    Co-Authors: Hengfa Lu, Jiaxi Ying, Xinlin Zhang, Zhong Chen, Jian Yang, Xiaobo Qu
    Abstract:

    Goal: The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing Signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency Signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm. Results: Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential Signal Property serves as an useful tool to model the time-domain MRS Signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane. Significance: Low-intensity Signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover Signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., Signal enhancement, feature extraction, and fast sampling.

  • EMBC - Accelerated magnetic resonance spectroscopy with Vandermonde factorization
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2017
    Co-Authors: Xiaobo Qu, Jiaxi Ying, Zhong Chen
    Abstract:

    Multi-dimensional magnetic resonance spectroscopy is an important tool for studying molecular structures, interactions and dynamics in bio-engineering. The data acquisition time, however, is relatively long and non-uniform sampling can be applied to reduce this time. To obtain the full spectrum,a reconstruction method with Vandermonde factorization is proposed. This method explores the general Signal Property in magnetic resonance spectroscopy: Its time domain Signal is approximated by a sum of a few exponentials. Results on synthetic and realistic data show that the new approach can achieve faithful spectrum reconstruction and outperforms state-of-the-art low rank Hankel matrix method.

Jiaxi Ying - One of the best experts on this subject based on the ideXlab platform.

  • Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy
    IEEE Transactions on Biomedical Engineering, 2018
    Co-Authors: Hengfa Lu, Jiaxi Ying, Xinlin Zhang, Zhong Chen, Jian Yang, Xiaobo Qu
    Abstract:

    Goal: The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing Signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency Signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm. Results: Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential Signal Property serves as an useful tool to model the time-domain MRS Signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane. Significance: Low-intensity Signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover Signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., Signal enhancement, feature extraction, and fast sampling.

  • EMBC - Accelerated magnetic resonance spectroscopy with Vandermonde factorization
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2017
    Co-Authors: Xiaobo Qu, Jiaxi Ying, Zhong Chen
    Abstract:

    Multi-dimensional magnetic resonance spectroscopy is an important tool for studying molecular structures, interactions and dynamics in bio-engineering. The data acquisition time, however, is relatively long and non-uniform sampling can be applied to reduce this time. To obtain the full spectrum,a reconstruction method with Vandermonde factorization is proposed. This method explores the general Signal Property in magnetic resonance spectroscopy: Its time domain Signal is approximated by a sum of a few exponentials. Results on synthetic and realistic data show that the new approach can achieve faithful spectrum reconstruction and outperforms state-of-the-art low rank Hankel matrix method.

Hengfa Lu - One of the best experts on this subject based on the ideXlab platform.

  • Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy
    IEEE Transactions on Biomedical Engineering, 2018
    Co-Authors: Hengfa Lu, Jiaxi Ying, Xinlin Zhang, Zhong Chen, Jian Yang, Xiaobo Qu
    Abstract:

    Goal: The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing Signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency Signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm. Results: Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential Signal Property serves as an useful tool to model the time-domain MRS Signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane. Significance: Low-intensity Signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover Signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., Signal enhancement, feature extraction, and fast sampling.

Anand N. Ganesan - One of the best experts on this subject based on the ideXlab platform.

  • Information Theory and Atrial Fibrillation (AF): A Review
    Frontiers Media S.A., 2018
    Co-Authors: Dhani Dharmaprani, Lukah Dykes, Andrew D. Mcgavigan, Pawel Kuklik, Kenneth Pope, Anand N. Ganesan
    Abstract:

    Atrial Fibrillation (AF) is the most common cardiac rhythm disorder seen in hospitals and in general practice, accounting for up to a third of arrhythmia related hospitalizations. Unfortunately, AF treatment is in practice complicated by the lack of understanding of the fundamental mechanisms underlying the arrhythmia, which makes detection of effective ablation targets particularly difficult. Various approaches to AF mapping have been explored in the hopes of better pinpointing these effective targets, such as Dominant Frequency (DF) analysis, complex fractionated electrograms (CFAE) and unipolar reconstruction (FIRM), but many of these methods have produced conflicting results or require further investigation. Exploration of AF using information theoretic-based approaches may have the potential to provide new insights into the complex system dynamics of AF, whilst also providing the benefit of being less reliant on empirically derived definitions in comparison to alternate mapping approaches. This work provides an overview of information theory and reviews its applications in AF analysis, with particular focus on AF mapping. The works discussed in this review demonstrate how understanding AF from a Signal Property perspective can provide new insights into the arrhythmic phenomena, which may have valuable clinical implications for AF mapping and ablation in the future