Literature DB >> 18407848

Single-ensemble-based eigen-processing methods for color flow imaging--Part II. The matrix pencil estimator.

Alfred C H Yu1, Richard S C Cobbold.   

Abstract

Parametric spectral estimators can potentially be used to obtain flow estimates directly from raw slow-time ensembles whose clutter has not been suppressed. We present a new eigen-based parametric flow estimation method called the matrix pencil, whose principles are based on a matrix form under the same name. The presented method models the slow-time signal as a sum of dominant complex sinusoids in the slow-time ensemble, and it computes the principal Doppler frequencies by using a generalized eigen-value problem-formulation and matrix rank reduction principles. Both fixed rank (rank-one, rank-two) and adaptive-rank matrix pencil flow estimators are proposed, and their potential applicability to color flow signal processing is discussed. For the adaptive-rank estimator, the nominal rank was defined as the minimum eigen-structure rank that yields principal frequency estimates with a spread greater than a prescribed bandwidth. In our initial performance evaluation, the fixed-rank matrix pencil estimators were applied to raw color flow data (transmit frequency: 5 MHz; pulse repetition period: 0.175 ms; ensemble size: 14) acquired from a steady flow phantom (70 cm/s at centerline) that was surrounded by rigid-tissue-mimicking material. These fixed-rank estimators produced velocity maps that are well correlated with the theoretical flow profile (correlation coefficient: 0.964 to 0.975). To facilitate further evaluation, the matrix pencil estimators were applied to synthetic slow-time data (transmit frequency: 5 MHz; pulse repetition period: 1.0 ms; ensemble size: 10) modeling flow scenarios without and with tissue motion (up to 1 cm/s). The bias and root-mean-squared error of the estimators were computed as a function of blood-signal-to-noise ratio and blood velocity. The matrix pencil flow estimators showed that they are comparatively less biased than most of the existing frequency-based flow estimators like the lagone autocorrelator.

Mesh:

Year:  2008        PMID: 18407848     DOI: 10.1109/TUFFC.2008.683

Source DB:  PubMed          Journal:  IEEE Trans Ultrason Ferroelectr Freq Control        ISSN: 0885-3010            Impact factor:   2.725


  6 in total

1.  Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system.

Authors:  U-Wai Lok; Pengfei Song; Joshua D Trzasko; Ron Daigle; Eric A Borisch; Chengwu Huang; Ping Gong; Shanshan Tang; Wenwu Ling; Shigao Chen
Journal:  Ultrasonics       Date:  2020-04-25       Impact factor: 2.890

2.  Complex principal components for robust motion estimation.

Authors:  F William Mauldin; Francesco Viola; William F Walker
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2010-11       Impact factor: 2.725

3.  The singular value filter: a general filter design strategy for PCA-based signal separation in medical ultrasound imaging.

Authors:  F William Mauldin; Dan Lin; John A Hossack
Journal:  IEEE Trans Med Imaging       Date:  2011-06-20       Impact factor: 10.048

4.  Reduction of echo decorrelation via complex principal component filtering.

Authors:  F William Mauldin; Francesco Viola; William F Walker
Journal:  Ultrasound Med Biol       Date:  2009-06-10       Impact factor: 2.998

5.  Clutter Mitigation in Echocardiography Using Sparse Signal Separation.

Authors:  Javier S Turek; Michael Elad; Irad Yavneh
Journal:  Int J Biomed Imaging       Date:  2015-06-24

6.  High frame rate doppler ultrasound bandwidth imaging for flow instability mapping.

Authors:  Billy Y S Yiu; Adrian J Y Chee; Guo Tang; Wenbo Luo; Alfred C H Yu
Journal:  Med Phys       Date:  2019-03-04       Impact factor: 4.071

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.