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Comparisons of lesion detectability in parametric ultrasound imaging vs. B-mode imaging
ABSTRACTS, ULTRASONIC IMAGING AND TISSUE CHARACTERIZATION SYMPOSIUM
2.5 A COMPARISON OF IDEAL AND HUMAN LESION DETECTION IN SIMULATED and L.J. Kizlik, Ohio University, ULTRASONIC IMAGES, J.J Giesey Department of Electrical Engineering, Athens, OH 45701. The coherent nature of ultrasound causes areas of homogeneous called speckle which tissue to have a granular appearance interferes with the ability to detect a lesion within a uniform Schemes have been developed to reduce speckle by background. averaging images but these methods also reduce the resolution of the images leaving the improvement in lesion detectability in doubt. the trade-off between speckle In an effort to quantize called the signal-to-noise reduction and resolution loss, a figure, ratio over an area (SNR), has been developed. This predicts the ability of an ideal observer to detect a lesion in an uniform lesion-to-background background. This is a function of the the degree of compounding, and the contrast, the lesion diameter, lateral and axial correlation cell size. Human detection of lesions is a complex process and has shown the various factors that comprise to be less than ideal. Therefore, the ideal detectability as expressed in the SNR may affect human issue has serious detectability in a different manner. This implications in the development and evaluation of speckle reducing systems. In this research, human observer detectability was measured as the lesion contrast and diameter, compounding, and speckle spot size were varied in simulated ultrasonic images. As expected, there was an increase in detection performance as the number of images compounded increase with all other parameters held constant. Also, for a given degree of compounding, detection increased increasing lesion size or performance with either contrast. However, it was found that as the degree of compounding is increased, the detectability for a given SNR decreases. 2.6 COMPARISONS OF LESION DETECTABILITY IN PARAMETRIC ULTRASOUND IMAGING vs. B-MODE IMAGING, Michael F. Insana and Timothy J. Hall, Dept. of Diagnostic Radiology, University of Kansas Medical Center, Kansas City, KS 66103. Unlike a B-mode image, where the echo amplitude is displayed, a parametric image displays individual properties of the medium that determine the echo amplitude, such as the average scatterer size. The ultimate utility of any parametric image depends on both the estimation accuracy and image quality. The signal-to-noise analysis of Wagner and Smith (11 provides an objective measure of image quality that we have used to compare parametric images with conventional ultrasonography. The lesion signal-to-noise ration (SNRX) was used to predict the detectability of low-contrast lesions in speckle-limited images, based on the area and contrast of the lesion, the speckle SNR, and the size of the resolution cell, These properties were measured for scatterer size, scattering strength, and integrated backscatter coefficient (IBC) images, and compared with B-mode imaging is noise reduction. Similarly, the principal advantage of scattering strength imaging is contrast enhancement, and the principal advantage of imaging is IBC spatial resolution enhancement. The advantages of using modern spectral estimation techniques in parameter estimation were investigated in terms of image quality. The minimum-variance spectral estimator, with an AR model order of 19, yielded approximately a 3-fold improvement in spectral noise with no appreciable increase in bias or computation time when compared to the periodogram. This can lead to a significant improvement in SNRl for parametric imaging using noisy echo signals.
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ABSTRACTS, ULTRASONIC IMAGING AND TISSUE CHARACTERIZATION SYMPOSIUM
Signal-to-noise analysis is useful for predicting in which applications parametric imaging will be most useful, and for designing imaging systems optimized for those applications. This work was supported in part by the Whitaker Foundation. [ll Smith, S.W., Wagner, R.F., Sandrik, J.M. and Lopez, H. IEEE Trans. Sonics Ultrason SU-30, 164-73 (1983). 2.7 SPECTRAL ESTIMATION USING AN AUTOREGRESSIVE MOVING AVERAGE MODEL, K.A. Wear and R.F. for Devices Wagner, Center and Radiological Health, Food and Drug Administration, 12720 Twinbrook Pkwy, Rockville, MD 20857. Spectral estimation is of fundamental importance in ultrasonic tissue characterization. Power spectra are often estimated from the squared moduli of FFT’s of digitized radiofrequency (rf) signals. If the data contains a substantial amount of noise, or possesses an intrinsically random nature (as is the case with ultrasound speckle), then this method can exhibit substantial variance. The present study addresses the efficacy of an autoregressive moving average (ARMA) model (11 for spectral estimation. In order to compare the accuracy of the ARMA method with that of the squared modulus of the FFT, simulated rf signals with known power spectral densities were generated using a computer model. Tissue was modeled as a dense suspension of ultrasonic scatterers with random locations and scattering strengths. Compared with the FFT method, the moving average method exhibited a five-fold reduction in mean square error. The ARMA model was also employed for estimation of spectra of rf obtained using a clinical ultrasonic imaging system (Diasonics DS-20) and a tissue-mimicking phantom (glass beads embedded in agar). Compared with the FFT approach, the moving average method demonstrated a 60% reduction in coefficient of variation (ratio of standard deviation to mean of the spectral estimate) as a function of frequency. [ll Harple, S.L., Diaital Snect~vsis with ADDlicatu I (Prentice-Hall, Englewood Cliffs, NJ, 1987). 3. TISSUE
MOTION
3.1 RANDOM STRUCTURE-INDEPENDENT IMAGING OF THE PHASE SHIFT PARAMETERS AND MOVEMENT OF SCATTERING OBJECTS IN THE REFLECTION MODE, E. Hori, Y. Yamakoshi and T. Sato, The Graduate School at Nagatsuta, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama-shi, 227 Japan. In imaging ultrasonic parameters of scattering objects in the reflection mode, the random structures of the objects adversely affect the precision of the results. linear averaging, which reduces the deviation Conventionally, by the factor l/Jn, where n is the number of averages, or nonlinear which eliminates extremelyprocessing such as median filtering, deviated data, have been applied. In this paper, we show a new method which can eliminate the It is based effect of random structures completely, in principle. on the observed data oriented quasi-real time calibration of the That is, the variation of sensitivity of the detecting process. the sensitivity due to the random structure is compensated for according to the observed structure itself; hence, a kind of random structure-independent precise estimation and imaging of ultrasonic parameters of any scattering objects in the reflection mode becomes possible. The details of the method applied to imaging of the
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2.5 A COMPARISON OF IDEAL AND HUMAN LESION DETECTION IN SIMULATED and L.J. Kizlik, Ohio University, ULTRASONIC IMAGES, J.J Giesey Department of Electrical Engineering, Athens, OH 45701. The coherent nature of ultrasound causes areas of homogeneous called speckle which tissue to have a granular appearance interferes with the ability to detect a lesion within a uniform Schemes have been developed to reduce speckle by background. averaging images but these methods also reduce the resolution of the images leaving the improvement in lesion detectability in doubt. the trade-off between speckle In an effort to quantize called the signal-to-noise reduction and resolution loss, a figure, ratio over an area (SNR), has been developed. This predicts the ability of an ideal observer to detect a lesion in an uniform lesion-to-background background. This is a function of the the degree of compounding, and the contrast, the lesion diameter, lateral and axial correlation cell size. Human detection of lesions is a complex process and has shown the various factors that comprise to be less than ideal. Therefore, the ideal detectability as expressed in the SNR may affect human issue has serious detectability in a different manner. This implications in the development and evaluation of speckle reducing systems. In this research, human observer detectability was measured as the lesion contrast and diameter, compounding, and speckle spot size were varied in simulated ultrasonic images. As expected, there was an increase in detection performance as the number of images compounded increase with all other parameters held constant. Also, for a given degree of compounding, detection increased increasing lesion size or performance with either contrast. However, it was found that as the degree of compounding is increased, the detectability for a given SNR decreases. 2.6 COMPARISONS OF LESION DETECTABILITY IN PARAMETRIC ULTRASOUND IMAGING vs. B-MODE IMAGING, Michael F. Insana and Timothy J. Hall, Dept. of Diagnostic Radiology, University of Kansas Medical Center, Kansas City, KS 66103. Unlike a B-mode image, where the echo amplitude is displayed, a parametric image displays individual properties of the medium that determine the echo amplitude, such as the average scatterer size. The ultimate utility of any parametric image depends on both the estimation accuracy and image quality. The signal-to-noise analysis of Wagner and Smith (11 provides an objective measure of image quality that we have used to compare parametric images with conventional ultrasonography. The lesion signal-to-noise ration (SNRX) was used to predict the detectability of low-contrast lesions in speckle-limited images, based on the area and contrast of the lesion, the speckle SNR, and the size of the resolution cell, These properties were measured for scatterer size, scattering strength, and integrated backscatter coefficient (IBC) images, and compared with B-mode imaging is noise reduction. Similarly, the principal advantage of scattering strength imaging is contrast enhancement, and the principal advantage of imaging is IBC spatial resolution enhancement. The advantages of using modern spectral estimation techniques in parameter estimation were investigated in terms of image quality. The minimum-variance spectral estimator, with an AR model order of 19, yielded approximately a 3-fold improvement in spectral noise with no appreciable increase in bias or computation time when compared to the periodogram. This can lead to a significant improvement in SNRl for parametric imaging using noisy echo signals.
131
ABSTRACTS, ULTRASONIC IMAGING AND TISSUE CHARACTERIZATION SYMPOSIUM
Signal-to-noise analysis is useful for predicting in which applications parametric imaging will be most useful, and for designing imaging systems optimized for those applications. This work was supported in part by the Whitaker Foundation. [ll Smith, S.W., Wagner, R.F., Sandrik, J.M. and Lopez, H. IEEE Trans. Sonics Ultrason SU-30, 164-73 (1983). 2.7 SPECTRAL ESTIMATION USING AN AUTOREGRESSIVE MOVING AVERAGE MODEL, K.A. Wear and R.F. for Devices Wagner, Center and Radiological Health, Food and Drug Administration, 12720 Twinbrook Pkwy, Rockville, MD 20857. Spectral estimation is of fundamental importance in ultrasonic tissue characterization. Power spectra are often estimated from the squared moduli of FFT’s of digitized radiofrequency (rf) signals. If the data contains a substantial amount of noise, or possesses an intrinsically random nature (as is the case with ultrasound speckle), then this method can exhibit substantial variance. The present study addresses the efficacy of an autoregressive moving average (ARMA) model (11 for spectral estimation. In order to compare the accuracy of the ARMA method with that of the squared modulus of the FFT, simulated rf signals with known power spectral densities were generated using a computer model. Tissue was modeled as a dense suspension of ultrasonic scatterers with random locations and scattering strengths. Compared with the FFT method, the moving average method exhibited a five-fold reduction in mean square error. The ARMA model was also employed for estimation of spectra of rf obtained using a clinical ultrasonic imaging system (Diasonics DS-20) and a tissue-mimicking phantom (glass beads embedded in agar). Compared with the FFT approach, the moving average method demonstrated a 60% reduction in coefficient of variation (ratio of standard deviation to mean of the spectral estimate) as a function of frequency. [ll Harple, S.L., Diaital Snect~vsis with ADDlicatu I (Prentice-Hall, Englewood Cliffs, NJ, 1987). 3. TISSUE
MOTION
3.1 RANDOM STRUCTURE-INDEPENDENT IMAGING OF THE PHASE SHIFT PARAMETERS AND MOVEMENT OF SCATTERING OBJECTS IN THE REFLECTION MODE, E. Hori, Y. Yamakoshi and T. Sato, The Graduate School at Nagatsuta, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama-shi, 227 Japan. In imaging ultrasonic parameters of scattering objects in the reflection mode, the random structures of the objects adversely affect the precision of the results. linear averaging, which reduces the deviation Conventionally, by the factor l/Jn, where n is the number of averages, or nonlinear which eliminates extremelyprocessing such as median filtering, deviated data, have been applied. In this paper, we show a new method which can eliminate the It is based effect of random structures completely, in principle. on the observed data oriented quasi-real time calibration of the That is, the variation of sensitivity of the detecting process. the sensitivity due to the random structure is compensated for according to the observed structure itself; hence, a kind of random structure-independent precise estimation and imaging of ultrasonic parameters of any scattering objects in the reflection mode becomes possible. The details of the method applied to imaging of the
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