Literature DB >> 26371543

Beamforming effects on generalized Nakagami imaging.

Xue Yu1, Yuexin Guo, Sheng-Min Huang, Meng-Lin Li, Wei-Ning Lee.   

Abstract

Ultrasound tissue characterization is crucial for the detection of tissue abnormalities. Since the statistics of the backscattered ultrasound signals strongly depend on density and spatial arrangement of local scatterers, appropriate modeling of the backscattered signals may be capable of providing unique physiological information on local tissue properties. Among various techniques, the Nakagami imaging, realized in a window-based estimation scheme, has a good performance in assessing different scatterer statistics in tissues. However, inconsistent m values have been reported in literature and obtained only from a local tissue region, abating the reliability of Nakagami imaging in tissue characterization. The discrepancies in m values in relevant literature may stem from the nonuniformity of the ultrasound image resolution, which is often neglected. We therefore hypothesized that window-based Nakagami m estimation was highly associated with the regional spatial resolution of ultrasound imaging. To test this hypothesis, our study investigated the effect of beamforming methods, including synthetic aperture (SA), coherent plane wave compounding (CPWC), multi-focusing (MF), and single-focusing (SF), on window-based m parameter estimation from the perspective of the resolution cell. The statistics of m parameter distribution as a function of imaging depth were characterized by their mean, variance, and skewness. The phantom with a low scatterer density (16 scatterers mm(-3)) had significantly lower m values compared to the ones with high scatterer densities (32 and 64 scatterers mm(-3)). Results from the homogeneous phantom with 64 scatterers mm(-3) showed that SA, MF, and CPWC had relatively uniform lateral resolutions compared to SF and thus relatively constant m estimates at different imaging depths. Our findings suggest that an ultrasound imaging regime exhibiting invariant spatial resolution throughout the entire imaging field of view would be the most appropriate for Nakagami imaging for tissue characterization.

Mesh:

Year:  2015        PMID: 26371543     DOI: 10.1088/0031-9155/60/19/7513

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  4 in total

1.  Small-window parametric imaging based on information entropy for ultrasound tissue characterization.

Authors:  Po-Hsiang Tsui; Chin-Kuo Chen; Wen-Hung Kuo; King-Jen Chang; Jui Fang; Hsiang-Yang Ma; Dean Chou
Journal:  Sci Rep       Date:  2017-01-20       Impact factor: 4.379

2.  Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma.

Authors:  Wei Li; Xiao-Zhou Lv; Xin Zheng; Si-Min Ruan; Hang-Tong Hu; Li-Da Chen; Yang Huang; Xin Li; Chu-Qing Zhang; Xiao-Yan Xie; Ming Kuang; Ming-De Lu; Bo-Wen Zhuang; Wei Wang
Journal:  Front Oncol       Date:  2021-03-26       Impact factor: 6.244

3.  Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis.

Authors:  Po-Hsiang Tsui; Ming-Chih Ho; Dar-In Tai; Ying-Hsiu Lin; Chiao-Yin Wang; Hsiang-Yang Ma
Journal:  Sci Rep       Date:  2016-09-08       Impact factor: 4.379

4.  Effect of ultrasound frequency on the Nakagami statistics of human liver tissues.

Authors:  Po-Hsiang Tsui; Zhuhuang Zhou; Ying-Hsiu Lin; Chieh-Ming Hung; Shih-Jou Chung; Yung-Liang Wan
Journal:  PLoS One       Date:  2017-08-01       Impact factor: 3.240

  4 in total

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