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Ultrasonic Attenuation Tomography Based on Log-Spectrum Analysis

This study introduces ultrasonic attenuation tomography for breast cancer diagnosis using reflected/scattered signals. The technique utilizes log-spectrum analysis to calculate mean attenuation coefficients and improve imaging accuracy compared to traditional methods. Results show promising advancements for non-invasive diagnosis.

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Ultrasonic Attenuation Tomography Based on Log-Spectrum Analysis

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  1. Ultrasonic Attenuation TomographyBased on Log-Spectrum Analysis Radovan Jiřík, Rainer Stotzka, Torfinn Taxt Brno University of Technology Department of Biomedical Engineering Brno, CZECH REPUBLIC Forschungszentrum Karlsruhe Institute forData-Processing and Electronics Eggenstein, GERMANY University of Bergen Department of Biomedicine Bergen, NORWAY

  2. 1. Introduction Aim: ultrasonic attenuation tomography for breast cancer diagnosis using

  3. 1. Introduction • B-mode ultrasonic imaging • low spatial resolution • low contrast • Ultrasound computed tomography • more data available • more complicated acquisition and signal processing • Ultrasound attenuation imaging • att. coef. closely related to tissue type and pathology • tomography setup possible for mammography • correction of reflection tomography images • standalone imaging modality

  4. undirected beam { 1. Introduction Main idea: processing of reflected / scattered signal sending transducer s(t) l t { tl = l / c receiving transducer Initial study presented

  5. mean attenuation coefficient 2. Model of RF signal Directly transmitted signal sending transducer s(t) l t { tl = l / c receiving transducer FFT S(w,l)

  6. s(t) t 2. Model of RF signal Reflected / scattered signal sending transducer l2 { l1 FFT receiving transducer S(w,l1+l2)

  7. 0 w0 w 0 w0 w 3. Method Segment of reflected / scattered signal - amplitude spectrum: Log-spectrum: Modified log-spectrum: Linear regression => (mean attenuation coefficient along the path l1+l2)

  8. y { x In the end – mean of the cumulated values calculated { { { 3. Method For each pixel - all combinations of sending and receiving positions

  9. 3. Method • Method analysis • All segments with the contribution of the computed pixel cumulated • Contribution of other pixels does not average out • Values shifted closer to the mean attenuation coefficient in the image • Influence of neighboring pixels • Estimation of : for non-sparse reflectors / scatterers log-spectrum not a linear function, but still a monotonous function

  10. 4. Results New attenuation imaging technique Standard unfiltered backprojection

  11. 5. Conclusion • Not only directly transmitted signal processed, reflected / scattered signal used in addition => significantly more data • Attenuation images with less geometry distortion than the backprojection algorithm • Simplifying assumptions used • Further research • huge set of linear / nonlinear equations • “Filtered backprojection” algorithm for non-straight propagation lines ??? • More complete model (non-sparse reflectors / scatterers)

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