Parametric Imaging for Ultrasonic Breast Scans Using the Generalized Spectrum

1. Parametric Imaging for Ultrasonic Breast Scans Using the Generalized Spectrum L. Huang, K.D. Donohue Electrical and Computer Engineering Department, University of Kentucky, Lexington, KY, USA V. Genis School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA F. Forsberg Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA This work was supported in part by of the National Cancer Institute and the National Institutes of Health Grant PO1-CA52823.

2. Imaging Pulse A-scan from liver tissue Pressure Pressure 0 0 0 1 2 3 0 50 100 150 Microseconds Microseconds A-scan section from liver tissue Pressure 0 82 84 86 88 90 92 94 96 98 100 Ultrasonic RF Signal Propagation path effects Scatterer Response Microseconds

3. Information Processing Issues • A-Scan Signal Characteristics: • System response • Scatterer response and configurations • Propagation path to each scatterer • Processing Objectives: • Extract parameters useful for differentiating between tissue states • Provide quantitative estimates of material properties • Processing Obstacles: • Variability in material properties and measurement systems • Nonstationarities due to material structures • Distortion due to overlying propagation path

4. Objective Apply Generalized Spectrum (GS) in order to: • Segment RF B-Scan data into statistically similar regions • Extract relevant parameters each region • Display parameters in image directly related to the anatomical image Details of GS application to tissue characterization: • “Duct Detection and Wall Spacing Estimation in Breast Tissue,” L. Huang, et. al. Ultrasonic Imaging, Jul. 2000. • “Tissue Classification with Generalized Spectrum Parameters,” K.D. Donohue, et. al., Ultrasound in Medicine and Biology, 2001.

5. Periodic Scatterers Diffuse Scatterers Amplitude Amplitude 0 0 20 21 22 23 24 25 26 20 21 22 23 24 25 26 Microseconds Microseconds Specular Scatterers Regular Spacings Amplitude Amplitude 0 0 20 21 22 23 24 25 26 20 21 22 23 24 25 26 Microseconds Microseconds Scatterer Configurations

6. A-scan Segment Coherently Average over Analysis Window Average over Diagonals Outer Product FFT Collapsed Average Generalized Spectrum The Generalized Spectrum (GS) extends the Power Spectral Density (PSD) to statistically characterize the phase from cyclostationary processes. Let Y(f) be the discrete Fourier Transform of A-scan segment y(t) windowed about some point in time to, the GS is defined as:

7. GS 15 Collapsed Average 1 MHz 10 .6 Spectral Correlation 5 5 10 15 .3 MHz 0 0 1 2 3 4 5 6 7 8 9 10 MHz GS For Diffuse Scatterers For diffuse-only scatterers (Stationary Process), no phase coherence exists between different DFT terms of the signal:

8. GS Collapsed Average 15 1 .6 MHz 10 Spectral Correlation .3 0 0 1 2 3 4 5 6 7 8 9 10 5 5 10 15 MHz MHz GS For Periodic Scatterers For periodic scatterers, phase coherence exists between frequencies separate by an amount inversely related to their spacing in time T:

9. GS 15 MHz 10 Collapsed Average 1 5 5 10 15 MHz .6 Spectral Correlation .3 0 0 1 2 3 4 5 6 7 8 9 10 MHz GS For Specular Scatterers Specular scatterers result in phase coherence between all frequencies:

10. Specular Diffuse Regular Classification Statistics: Specular Likelihood Defined as the area under the CA curve:

11. Specular Diffuse Regular Classification Statistics: Diffuse Likelihood Defined as the as the summation of IDFT of CA:

12. Specular Diffuse Regular Classification Statistics: Regular Likelihood Defined as the summation of the absolute value of the gradient of CA:

13. Simulation Study Simulated A-Scans with diffuse, regular, and coherent structures were used to test performance of the classification statistics: • Analysis window allowed for 10 independent A-scan segments to be averaged per estimate • Varied structure-to-diffuse energy ratio: 0 dB to 12 dB • Varied density of specular scatterers: 1 to 16 per analysis window. • Varied standard deviation of regular scatterer spacings: 2 to 10% • Various GS normalizations schemes were compared: • Analysis Window Energy Normalization • Segment Energy Normalization • Segment System Normalization • ROC areas for the following binary classification tests were computed: • Diffuse vs. Specular • Diffuse vs. Regular • Regular vs. Specular

14. Simulation Results Diffuse vs. Specular • Specular and diffuse likelihood ROC areas: .98 to 1 for all densities, SNRs, and normalizations. Diffuse vs. Regular • Diffuse likelihood ROC areas: .99 to 1 for all spacing variations and SNRs • Regular likelihood ROC areas: .5 to 1 where system normalization had worse performance and analysis window normalization had the best performance Regular vs. Specular • Specular likelihood ROC areas: .73 to 1 for all spacing variations and SNRs where system normalization had worse performance • Regular likelihood ROC areas: from .5 to .99 where system normalization had worse performance

15. Phantom Study Phantom: (Dansk Fantom Service, Denmark) 6 x 12 x 8 cm block • attenuation: .5 dB/(cm*MHz) • sound speed: of 1497 m/sec • Density: 1027 Kg/m3 • Regular scatterers: cylindrical holes distributed throughout and filled with the preservative fluid with attenuation -0.03 dB/(cm*MHz), sound speed 1504 m/s, and density of 998 Kg/m3. Scanner: • ATL UM9 HDI ultrasound system using the L10-5 probe • The focal point was set at 4.25 cm. • RF data were sampled at 20 MHz with 12 bits • Post TGC, pre-demodulator and pre-logarithmic compression rf signal output to disk storage media

16. Phantom Sagittal Transverse

21. Clinical Examples • Characterized likelihood statistics with histograms to determine thresholds for segmenting clinical B-scans into diffuse, specular, and regular scattering regions. • Apply False Alarm Rate Threshold to diffuse likelihood statistics to separate diffuse from coherent regions (1% Threshold) (segment energy normalized GS). • Apply False Alarm Rate Threshold to regular likelihood statistics on coherent regions to separate specular from regular (30% Threshold) (segment energy normalized GS).

22. Parametric Images In diffuse regions computed PSD and estimated center frequency and slope of roll-off (dB/MHz) • Set pixel values proportional to center frequency • Set pixel values proportional to slope In specular regions computed area under CA • Set pixel values proportional to CA area In regular regions estimated spacings • Set pixel values proportional to spacing in cm

23. Likelihood statistics of malignant tumor scan

24. Parametric images of malignant tumor scans

25. Comparison with non segmented parametric images

26. Likelihood statistics of malignant tumor scan

27. Parametric images malignant tumor scan

28. Likelihood statistics of benign tumor scan

29. Parametric images benign tumor scan

30. Conclusions • The GS provides a means for classifying A-scan segments into regions of diffuse (statistically stationary), specular, and regular (nonstationary) scattering structures. • The GS segmentation can be used in conjunction with other estimators to improve efficiency and reliability of the estimated parameters. • Real-time applications of parametric images can be useful for guiding the scan plane to structures of interest and extracting quantitative information.