Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Abstract

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequencydependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, from images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as help attenuation compensation other ultrasound-based imaging techniques.Keywords ultrasound • attenuation • speed of sound • computed tomography • limited angle tomography

A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization

“…While these techniques attain an improved estimation at the expense of spatial resolution, more recent works explore the possibility of extending the precisionresolution trade-off. Regularization incorporating a spatial prior was successful in simultaneously improving the resolution and the precision of QUS estimation for homogeneous regions [6,41,48]. However, uniform regularization, which was used in [6,41,48], might not be appropriate for biological tissue with the presence of inhomogeneities, as it will lead to oversmoothing in homogeneous regions in an attempt to compensate for the local inhomogeneities.…”

“…Regularization incorporating a spatial prior was successful in simultaneously improving the resolution and the precision of QUS estimation for homogeneous regions [6,41,48]. However, uniform regularization, which was used in [6,41,48], might not be appropriate for biological tissue with the presence of inhomogeneities, as it will lead to oversmoothing in homogeneous regions in an attempt to compensate for the local inhomogeneities. In our previous work, we presented a proof-of-concept study based on a spatially weighted total variation regularization method, where the adaptive regularization parameter was a function of local inhomogeneity [10].…”