KEYWORDS: Transducers, Tissues, Spatial frequencies, Spectrum analysis, Ultrasonics, Ultrasonography, Acoustics, Data acquisition, Integration, Medical research
We have conducted a general study that relates calibrated 2-D ultrasonic spectral parameters to the physical properties of sub-resolution tissue scatterers. Our 2-D spectra are computed form digital radio-frequency echo data obtained as the transducer linearly scans along the cross-range (scan direction) with increments smaller than the half beam width. Acquired data are Fourier transformed with respect to range (beam) and cross-range (scan) directions. To quantitatively measure and classify the physical properties of tissues, we have defined two spectral functions and four spectral parameters. The 2-D spectral functions are: radially integrated spectral power (RISP) and angularly integrated spectral power (AISP). The summary parameters are: peak value and 3-dB width of the RISP, slope and intercept of the AISP. These parameter are understood in terms of the beam properties, transducer parameters and the physical properties of the tissue microstructures including size, shape, orientation, concentration and acoustic impedance. Our theoretical model indicates that 1) the 3-dB width of the RISP is predominantly determined by the scatterer size along the beam direction; 2) the slope of the linear fit of the AISP is predominantly determined by the scatterer size along range direction; 3) the concentration and the relative acoustic impedance fluctuation of the scatterers change the overall spectrum magnitude. The predictions of the theoretical model have been verified using beef muscle fibers examined with 40 MHz center frequency.
We have developed a family of objective features in order to provide non-invasive, reliable means of distinguishing benign from malignant breast lesions. These include acoustic features (echogenicity, heterogeneity, shadowing) and morphometric features (area, aspect ratio, border irregularity, margin definition). These quantitative descriptors are designed to be independent of instrument properties and physician expertise. Our analysis included manual tracing of lesion boundaries and adjacent areas on grayscale images generated from RF data. To derive quantitative acoustic features, we computed spectral parameter maps of radio-frequency (RF) echo signals (calibrated with system transfer function and corrected for diffraction) within these areas. We quantified morphometric features by geometric and fractal analysis of traced lesion boundaries. Although no single parameter can reliably discriminate cancerous from non-cancerous breast lesions, multifeature analysis provides excellent discrimination of cancerous and non-cancerous lesions. RF echo-signal data used in this study were acquired during routine ultrasonic examinations of biopsy-scheduled patients at three clinical sites. Our data analysis for 130 patients produced an ROC-curve area of 0.9164 +/- 0.0346. Among the quantitative descriptors, lesion heterogeneity, aspect ratio, and a border irregularity descriptor were the most useful; some morphometric features (such as the border irregularity descriptor) were particularly effective in lesion classification.
Two- and three-dimensional depictions of ultrasound echo signal data have potential for helping to detect and diagnose disease and to plan and monitor therapy. The utilization of very-high-frequency ultrasound and spectrum analysis of radio- frequency echo signals extends the capabilities of ultrasonic imaging for these purposes. Images generated using these techniques can present tissue architecture with exquisite resolution and can provide information on underlying properties of scatterers in the tissue. Changes in properties over time can be used to monitor disease progression or response to therapy. Relating tissue echo-signal parameters obtained from unknown tissue to database values of known tissue types can provide means of characterizing tissue for the purposes of detection or diagnosis and treatment planning. These potential applications are illustrated using examples from plaque, ophthalmic, skin, and prostate studies.
We are developing quantitative descriptors of breast lesions in order to provide reliable, operator-independent means of non-invasive breast cancer identification. These quantitative descriptors include lesion internal features assessed using spectrum analysis of ultrasonic radio-frequency (RF) echo signals and morphometric features related to lesion shape. Internal features include quantitative measures of 'echogenicity,' 'heterogeneity,' and 'shadowing;' these were computed by generating spectral-parameter images of the lesion and surrounding tissue. Spectral-parameter values were generated at each pixel in the parameter image using a sliding-window Fourier analysis. Lesions were traced on B-mode images and traces were used in conjunction with spectral parameter values to compute echogenicity, heterogeneity, and shadowing. Initial results show that no single parameter may be sufficiently precise in identifying cancerous breast lesions; the results also show that the use of multiple features can substantially improve discrimination. This paper describes the background, research objective, and methodology. Clinical examples are included to illustrate the practical application of our methodology.
Spectrum analysis of ultrasonic radio-frequency echo signals has proven to be an effective means of characterizing tissues of the eye and liver, thrombi, plaque, etc. Such characterization can be of value in detecting, differentiating, and monitoring disease. In some clinical applications, linear methods of tissue classification cannot adequately differentiate among the various manifestations of cancerous and non-cancerous tissue; in these cases, non-linear methods, such as neural-networks, are required for tissue typing. Combining spectrum-analysis methods for quantitatively characterizing tissue properties with neural-network methods for classifying tissue, a powerful new means of guiding biopsies, targeting therapy, and monitoring treatment may be available. Current studies are investigating potential applications of these methods that use novel tissue-typing images presented in two and three dimensions. Results to date show significant sensitivity improvements of possible benefit in cancer detection and effective tissue-type imaging that promise improved means of planning and monitoring treatment of prostate cancer.
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