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Purpose <p>The article proposes an automated method for estimating mean scatterer spacing (MSS) in biological tissues using ultrasound signals. It employs singular spectrum analysis (SSA) with a novel entropy-based criterion to enhance accuracy and eliminate the need for manual parameter tuning. The method is validated using both simulated and real signals, demonstrating its effectiveness in detecting microstructural changes in tissues.</p> Methods <p>The proposed SSA + entropy method estimates the MSS from ultrasound signals using SSA combined with a relative entropy-based criterion. This approach determines the optimal number of eigenvector pairs to reconstruct the periodic part of the signal by identifying the minimum in the entropy curve—a point that corresponds to the number of underlying periodic components, even in noisy conditions. For experimental validation, ex vivo bovine skeletal muscle was analyzed. RF data were collected at a controlled temperature of using a 3.5-MHz transducer. The MSS results obtained via SSA were also independently verified using microscopy and image analysis of muscle fiber spacing, confirming the effectiveness of the entropy-based SSA approach.</p> Results <p>The MSS estimation method's accuracy decreases as jitter or Ad noise levels increase, with the method performing well when the estimated MSS is within 10% of the true value. Additionally, the standard deviation of MSS estimates increases as Ad levels and jitter values rise. In phantom experiments, MSS was measured using different transducer frequencies with varying results at each frequency. When applied to ex vivo bovine skeletal muscle tissue, the MSS estimation was validated using both RF and imaging methods, with three consistent groups of MSS values observed, confirming the method's applicability and reliability.</p> Conclusion <p>The proposed method combines SSA and entropy to automatically estimate medium periodicity in ultrasound, eliminating the need for operator intervention. It successfully identifies periodicities in both simulated and real data, with the ability to detect multiple periodicities depending on frequency and backscattered energy.</p>

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Singular spectrum analysis – an automatic detection technique for mean scatterer spacing estimation using ultrasound signals

  • 期刊论文

  • 发表日期：

    2025年5月3日

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  • Adriana Kauati,
  • Guillermo Cortela,
  • Marcello L. R. de Campos,
  • Wagner C. A. Pereira

摘要

Purpose

The article proposes an automated method for estimating mean scatterer spacing (MSS) in biological tissues using ultrasound signals. It employs singular spectrum analysis (SSA) with a novel entropy-based criterion to enhance accuracy and eliminate the need for manual parameter tuning. The method is validated using both simulated and real signals, demonstrating its effectiveness in detecting microstructural changes in tissues.

Methods

The proposed SSA + entropy method estimates the MSS from ultrasound signals using SSA combined with a relative entropy-based criterion. This approach determines the optimal number of eigenvector pairs to reconstruct the periodic part of the signal by identifying the minimum in the entropy curve—a point that corresponds to the number of underlying periodic components, even in noisy conditions. For experimental validation, ex vivo bovine skeletal muscle was analyzed. RF data were collected at a controlled temperature of using a 3.5-MHz transducer. The MSS results obtained via SSA were also independently verified using microscopy and image analysis of muscle fiber spacing, confirming the effectiveness of the entropy-based SSA approach.

Results

The MSS estimation method's accuracy decreases as jitter or Ad noise levels increase, with the method performing well when the estimated MSS is within 10% of the true value. Additionally, the standard deviation of MSS estimates increases as Ad levels and jitter values rise. In phantom experiments, MSS was measured using different transducer frequencies with varying results at each frequency. When applied to ex vivo bovine skeletal muscle tissue, the MSS estimation was validated using both RF and imaging methods, with three consistent groups of MSS values observed, confirming the method's applicability and reliability.

Conclusion

The proposed method combines SSA and entropy to automatically estimate medium periodicity in ultrasound, eliminating the need for operator intervention. It successfully identifies periodicities in both simulated and real data, with the ability to detect multiple periodicities depending on frequency and backscattered energy.