Tissue classification with generalized spectrum parameters

Donohue, Kevin D.; Huang, Liang; Burks, T.F.; Forsberg, Flemming; Piccoli, Catherine W.

doi:10.1016/s0301-5629(01)00468-9

Cited by 74 publications

(48 citation statements)

References 30 publications

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“…The presence of regular scatterers is fairly straightforward and there are a number of robust algorithms for the estimation of mean scatterer spacing [3], [18]. Several techniques for characterizing specular and diffuse scatters also exist (one of the better motivated approaches is described in [19]). …”

Section: Discussionmentioning

confidence: 99%

Noninvasive Estimation of Tissue Temperature Via High-Resolution Spectral Analysis Techniques

Amini¹,

Ebbini

Georgiou

2005

IEEE Trans. Biomed. Eng.

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Abstract-We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).

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Section: Discussionmentioning

confidence: 99%

Noninvasive Estimation of Tissue Temperature Via High-Resolution Spectral Analysis Techniques

Amini¹,

Ebbini

Georgiou

2005

IEEE Trans. Biomed. Eng.

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“…A number of experimental studies utilizing these models have been performed (Insana 1996;Hosokawa et al 1994;Garra et al 1994), including characterization of renal disease (Insana 1996;Garra et al 1994), liver disease (Stetson and Sommer 1997), breast tumors (Anderson et al 2001;Donohue et al 2001), skin (Fournier et al 2001) and atherosclerotic plaque (Moore et al 1998;Watson et al 2000;Nair et al 2001), among others. This general methodology has been applied by our research group for characterization of ocular tumors (Coleman et al 2004;Liu et al 2004;Silverman et al 2003) and prostate cancer (Feleppa et al 2002;Feleppa et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Improved visualization of high-intensity focused ultrasound lesions

Silverman

Muratore

Ketterling

et al. 2006

Ultrasound in Medicine & Biology

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Spectral parameter imaging in both the fundamental and harmonic of backscattered radiofrequency (RF) data was used for immediate visualization of high-intensity focused ultrasound (HIFU) lesion sites. A focused 5-MHz HIFU transducer with a coaxial 9-MHz focused single-element diagnostic transducer was used to create and scan lesions in chicken breast and freshly excised rabbit liver. Bmode images derived from the backscattered RF signal envelope were compared with midband fit (MBF) spectral parameter images in the fundamental (9-MHz) and harmonic (18-MHz) bands of the diagnostic probe. Images of HIFU-induced lesions derived from the MBF to the calibrated spectrum showed improved contrast (~ 3 dB) of tumor margins versus surround compared to images produced from the conventional signal envelope. MBF parameter images produced from the harmonic band showed higher contrast in attenuated structures (core, shadow) compared to either the conventional envelope (3.3 dB core; 11.6 dB shadow) or MBF images of the fundamental band (4.4 dB core; 7.4 dB shadow). The gradient between the lesion and surround was 3.4 dB/mm, 6.9 dB/mm and 17.2 dB/mm for B-mode, MBF-fundamental mode and MBF-harmonic mode respectively. Images of threshold and 'popcorn' lesions produced in freshly excised rabbit liver were most easily visualized and boundaries best defined using MBF-harmonic mode.

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“…Equation (8) can be rewritten as (10) where , and R D is a spatial ACF describing the two-way directivity function of the beam, (11) and R G is a spatial ACF of the 2-D gating function, i.e., (12) Equation (10) is the general equation of the calibrated 2-D power spectrum for tissues exhibiting wide-sense stationarity. It demonstrates that 2-D power spectra of rf signals are basically determined by three ACFs: the ACF of the 2-D gating function, the ACF of the ultrasonic two-way beam directivity function, and, the ACF of the tissue acoustic impedance function.…”

Section: The Initial Assumptions Of the Model Arementioning

confidence: 99%

“…10 Among these groups, anisotropy in attenuation and in backscatter was reported for tissues, e.g., skeletal muscles, 11 myocardium, 7,12 and kidney. 8,13 In the early 1980s, Lizzi et al developed a calibrated (normalized) 1-D spectrum method to quantitatively characterize tissue in terms of its physical properties.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic tissue characterization via 2‐D spectrum analysis: Theory and in vitro measurements

Liu¹,

Lizzi²,

Ketterling³

et al. 2007

Medical Physics

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A theoretical model is described for application in ultrasonic tissue characterization using a calibrated 2-D spectrum analysis method. This model relates 2-D spectra computed from ultrasonic backscatter signals to intrinsic physical properties of tissue microstructures, e.g., size, shape, and acoustic impedance. The model is applicable to most clinical diagnostic ultrasound systems. Two experiments employing two types of tissue architectures, spherical and cylindrical scatterers, are conducted using ultrasound with center frequencies of 10 and 40 MHz, respectively. Measurements of a tissue-mimicking phantom with an internal suspension of microscopic glass beads are used to validate the theoretical model. Results from in vitro muscle fibers are presented to further elucidate the utility of 2-D spectrum analysis in ultrasonic tissue characterization.

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Tissue classification with generalized spectrum parameters

Cited by 74 publications

References 30 publications

Noninvasive Estimation of Tissue Temperature Via High-Resolution Spectral Analysis Techniques

Noninvasive Estimation of Tissue Temperature Via High-Resolution Spectral Analysis Techniques

Improved visualization of high-intensity focused ultrasound lesions

Ultrasonic tissue characterization via 2‐D spectrum analysis: Theory and in vitro measurements

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