Modeling acoustic attenuation of soft tissue with a minimum-phase filter

Area of Science:

• Acoustics
• Biomedical Engineering
• Signal Processing

Background:

• Soft biological tissues exhibit frequency-dependent acoustic attenuation.
• This attenuation often follows a linear log-magnitude characteristic with frequency.
• Accurate simulation of this behavior is crucial for ultrasound imaging and therapy.

Purpose of the Study:

• To implement a finite-impulse-response (FIR) digital filter model for simulating linear frequency-dependent acoustic attenuation in biological tissues.
• To ensure causality of the filter model using the minimum-phase constraint.
• To evaluate the accuracy of the proposed minimum-phase model compared to a linear-phase model.

Main Methods:

• Developed a finite-impulse-response (FIR) digital filter model.
• Applied the minimum-phase constraint, leveraging the Hilbert Transform relationship between log-magnitude and phase.
• Calculated the filter's unit-sample response via inverse Fourier Transform.
• Validated the model using plexiglas with known linear attenuation characteristics.

Main Results:

• The minimum-phase FIR filter model successfully simulated linear acoustic attenuation.
• Experimental results showed the minimum-phase model yielded a root-mean-square error 3 times smaller than the linear-phase model.
• The study analyzed the impact of sampling period and FIR filter size on model performance.

Conclusions:

• The minimum-phase FIR filter model provides a more accurate simulation of soft tissue acoustic attenuation compared to linear-phase models.
• This digital filter approach offers a valuable tool for realistic modeling in biomedical acoustics.
• Further research can explore the effects of varying filter parameters on simulation accuracy.