The Experts below are selected from a list of 2958 Experts worldwide ranked by ideXlab platform

D L Kwong - One of the best experts on this subject based on the ideXlab platform.

  • integrated in band optical signal to Noise ratio monitor implemented on soi platform
    Optics Express, 2012
    Co-Authors: Lianxi Jia, Junfeng Song, Tsungyang Liow, Qing Fang, D L Kwong
    Abstract:

    Based on different coherence properties of signal and Noise, we measured the in-band optical signal-to-Noise ratio using an integrated thermally tunable Mach-Zehnder optical delay interferometer on SOI platform. The experimental results exhibit errors smaller than 1 dB for signals with bit rate <40 Gbps over an OSNR range of 9~30 dB. The effects of the extinction ratio, Noise Equivalent Bandwidth and arm length difference on the implementation of measurement are analyzed.

Elliot R Mcveigh - One of the best experts on this subject based on the ideXlab platform.

  • image reconstruction in snr units a general method for snr measurement
    Magnetic Resonance in Medicine, 2005
    Co-Authors: Peter Kellman, Elliot R Mcveigh
    Abstract:

    The method for phased array image reconstruction of uniform Noise images may be used in conjunction with proper image scaling as a means of reconstructing images directly in SNR units. This facilitates accurate and precise SNR measurement on a per pixel basis. This method is applicable to root-sum-of- squares magnitude combining, B1-weighted combining, and parallel imaging such as SENSE. A procedure for image recon- struction and scaling is presented, and the method for SNR measurement is validated with phantom data. Alternative meth- ods that rely on Noise only regions are not appropriate for parallel imaging where the Noise level is highly variable across the field-of-view. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for re- constructing images directly in SNR units. The procedure in- cludes scaling for Noise Equivalent Bandwidth of digital receiv- ers, FFTs and associated window functions (raw data filters), and array combining. Magn Reson Med 54:1439 -1447, 2005. Signal-to-Noise ratio (SNR) is a frequently used metric of image quality, yet despite numerous proposed methods for measuring SNR in MR images (1- 4), a number of impor- tant issues remain. In this article, current methods and limitations will be reviewed. A method for reconstructing images directly in SNR units is described. This approach facilitates accurate and precise SNR measurement on a per pixel basis and is applicable to root-sum-of-squares mag- nitude combining, B1-weighted combining, and parallel imaging. Alternative methods that rely on measurement of Noise from regions of interest in the signal-free background (1,2) are not appropriate for parallel imaging where the Noise level is highly variable across the field-of-view. The variation in Noise across the FOV, the so-called g-factor (5), arises due to the ill-condition of the inverse solution used in parallel imaging. As parallel imaging (5) is now widely used, a procedure is needed to accurately and precisely measure SNR. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units. The pro- cedure includes scaling for Noise Equivalent Bandwidth of digital receivers, FFTs and associated window functions (raw data filters), and array combining. SNR images provide a number of benefits. Protocols may be optimized by quantitative measurement of image SNR as parameters such as TE, TR, Bandwidth, and readout flip angle are varied. Comparison between sequences and pro- tocols is easier to perform and more reliable, as well as comparison between various coil designs and coil place- ment. Clinical benefits include quantitative characteriza- tion of lesions and tissue. Questions such as how much lesion enhancement or how much change is observed day- to-day may be answered with greater confidence. Quanti- tative measurements of contrast-to-Noise (CNR) determine if a region has a statistically significant difference in in- tensity. Variation in tissue intensity due to heterogeneity may be discriminated from variation due to thermal Noise. Quantitative measures of SNR and CNR are useful in first- pass contrast enhanced perfusion studies. Post-Processing of images is also facilitated with SNR scaled images, e.g., simple thresholding. SNR is frequently calculated using Noise SD values es- timated directly from the reconstructed image or series of images. A number of methods for estimating the Noise SD are described briefly in the following. These methods have limitations that serve as a motivation for using SNR scaled image reconstruction, which is based on pre-scan Noise measurement. The methods of Noise estimation fall into 3 general classes: (a) estimating background Noise based on a Noise only region; (b) estimating Noise based on temporal differencing or, more generally, temporal filtering; and (c) estimating Noise based on spatial derivative or highpass filter.

Lianxi Jia - One of the best experts on this subject based on the ideXlab platform.

  • integrated in band optical signal to Noise ratio monitor implemented on soi platform
    Optics Express, 2012
    Co-Authors: Lianxi Jia, Junfeng Song, Tsungyang Liow, Qing Fang, D L Kwong
    Abstract:

    Based on different coherence properties of signal and Noise, we measured the in-band optical signal-to-Noise ratio using an integrated thermally tunable Mach-Zehnder optical delay interferometer on SOI platform. The experimental results exhibit errors smaller than 1 dB for signals with bit rate <40 Gbps over an OSNR range of 9~30 dB. The effects of the extinction ratio, Noise Equivalent Bandwidth and arm length difference on the implementation of measurement are analyzed.

Peter Kellman - One of the best experts on this subject based on the ideXlab platform.

  • image reconstruction in snr units a general method for snr measurement
    Magnetic Resonance in Medicine, 2005
    Co-Authors: Peter Kellman, Elliot R Mcveigh
    Abstract:

    The method for phased array image reconstruction of uniform Noise images may be used in conjunction with proper image scaling as a means of reconstructing images directly in SNR units. This facilitates accurate and precise SNR measurement on a per pixel basis. This method is applicable to root-sum-of- squares magnitude combining, B1-weighted combining, and parallel imaging such as SENSE. A procedure for image recon- struction and scaling is presented, and the method for SNR measurement is validated with phantom data. Alternative meth- ods that rely on Noise only regions are not appropriate for parallel imaging where the Noise level is highly variable across the field-of-view. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for re- constructing images directly in SNR units. The procedure in- cludes scaling for Noise Equivalent Bandwidth of digital receiv- ers, FFTs and associated window functions (raw data filters), and array combining. Magn Reson Med 54:1439 -1447, 2005. Signal-to-Noise ratio (SNR) is a frequently used metric of image quality, yet despite numerous proposed methods for measuring SNR in MR images (1- 4), a number of impor- tant issues remain. In this article, current methods and limitations will be reviewed. A method for reconstructing images directly in SNR units is described. This approach facilitates accurate and precise SNR measurement on a per pixel basis and is applicable to root-sum-of-squares mag- nitude combining, B1-weighted combining, and parallel imaging. Alternative methods that rely on measurement of Noise from regions of interest in the signal-free background (1,2) are not appropriate for parallel imaging where the Noise level is highly variable across the field-of-view. The variation in Noise across the FOV, the so-called g-factor (5), arises due to the ill-condition of the inverse solution used in parallel imaging. As parallel imaging (5) is now widely used, a procedure is needed to accurately and precisely measure SNR. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units. The pro- cedure includes scaling for Noise Equivalent Bandwidth of digital receivers, FFTs and associated window functions (raw data filters), and array combining. SNR images provide a number of benefits. Protocols may be optimized by quantitative measurement of image SNR as parameters such as TE, TR, Bandwidth, and readout flip angle are varied. Comparison between sequences and pro- tocols is easier to perform and more reliable, as well as comparison between various coil designs and coil place- ment. Clinical benefits include quantitative characteriza- tion of lesions and tissue. Questions such as how much lesion enhancement or how much change is observed day- to-day may be answered with greater confidence. Quanti- tative measurements of contrast-to-Noise (CNR) determine if a region has a statistically significant difference in in- tensity. Variation in tissue intensity due to heterogeneity may be discriminated from variation due to thermal Noise. Quantitative measures of SNR and CNR are useful in first- pass contrast enhanced perfusion studies. Post-Processing of images is also facilitated with SNR scaled images, e.g., simple thresholding. SNR is frequently calculated using Noise SD values es- timated directly from the reconstructed image or series of images. A number of methods for estimating the Noise SD are described briefly in the following. These methods have limitations that serve as a motivation for using SNR scaled image reconstruction, which is based on pre-scan Noise measurement. The methods of Noise estimation fall into 3 general classes: (a) estimating background Noise based on a Noise only region; (b) estimating Noise based on temporal differencing or, more generally, temporal filtering; and (c) estimating Noise based on spatial derivative or highpass filter.

Junfeng Song - One of the best experts on this subject based on the ideXlab platform.

  • integrated in band optical signal to Noise ratio monitor implemented on soi platform
    Optics Express, 2012
    Co-Authors: Lianxi Jia, Junfeng Song, Tsungyang Liow, Qing Fang, D L Kwong
    Abstract:

    Based on different coherence properties of signal and Noise, we measured the in-band optical signal-to-Noise ratio using an integrated thermally tunable Mach-Zehnder optical delay interferometer on SOI platform. The experimental results exhibit errors smaller than 1 dB for signals with bit rate <40 Gbps over an OSNR range of 9~30 dB. The effects of the extinction ratio, Noise Equivalent Bandwidth and arm length difference on the implementation of measurement are analyzed.