Ultrasonic attenuation imaging in a rodent thyroid cancer model

Abstract

The incidence of diagnosed thyroid cancer has increased significantly over the last decades. Although advances in ultrasonic imaging have increased the malignancy detection rate, current ultrasonic imaging markers do not provide a sufficient level of diagnostic accuracy to replace biopsy. Recent studies suggest that ultrasound parameters derived from backscatter coefficients may allow differentiating among different types of thyroid tumors and normal tissues in a rodent model ex vivo. In this work, the potential use of attenuation coefficient (AC) estimates for the same purpose was explored. A sample set of 24 excised mice thyroids were scanned using a 40-MHz, f/3 single element transducer. The experimental dataset contained six animals that developed papillary thyroid carcinoma (PTC), five that developed follicular variant papillary thyroid carcinoma (FV-PTC), five that developed c-Cell adenoma (c-Cell) and eight that did not develop thyroid abnormalities (control). AC slope maps were generated with a spectral log difference method using 0.5mm by 0.5mm data blocks. Outliers of each slice due to artifacts in AC estimation were discarded using the Thompson Tau method. Finally, a Kruskal-Wallis test was conducted to analyze if statistically significant differences in the mean AC slope among the four groups existed. The median and interquartile range for each group were 1.29 and 0.22 dB/cmMHz for the control group, 1.64 and 0.09 dB/cm-MHz for c-Cell, 1.16 and 0.12 dB/cm-MHz for PTC and 1.33 and 0.08 dB/cmMHz for FV-PTC, respectively. These values are consistent with previous reports of attenuation in thyroid tissues. The KruskalWallis test reported statistically significant differences between the c-Cell group and the other groups of study and between the PTC and FV-PTC groups (p<0.05). These preliminary results suggest that the AC may be used to characterize thyroid tissues.

Regularized Spectral Log Difference Technique for Ultrasonic Attenuation Imaging

“…with the diffraction compensation function developed in [37] and used previously for the SLD technique in [38], i.e.,…”

Improving the quality of attenuation imaging using full angular spatial compounding

“…As shown in [14], the resulting attenuation coefficient slope for each region of interest can be obtained by performing a linear fit to (1), (1) where is the distance from the transducer to the center of the window of analysis, subscripts d and p represent the distal and proximal window of the data block respectively, is the received voltage signal from the scattering volume, is the analytical BSC diffraction compensation function for singleelement transducers proposed by [15] and c is a constant term product of assuming that BSC varies in the form .…”

“…The majority of the previously mentioned algorithms make use of a calibrated reference phantom to compensate for diffraction. In contrast, in this work the analytic attenuation estimation method for single-element transducers presented in [14] has been used. The spectral information of RF data was processed to generate attenuation maps on custom MATLAB (MathWorks, Natick, MA) software.…”