Abstract

The aim of this study was to determine the efficacy of using sound velocity and tissue attenuation to clinically discriminate breast cancer from healthy tissues. The methods for reconstructing the sound-velocity and attenuation-coefficient distributions were previously proposed and tested on tissue-mimicking phantoms. The methods require only raw channel data acquired by a linear transducer array and can therefore be implemented on existing clinical systems. In this paper, these methods are tested on clinical data. A total of 19 biopsy-proven cases, consisting of five carcinomas (CAs), seven fibroadenomas (FAs), six adipose tissue (fat) and one oil cyst, were evaluated. A single imaging setup consisting of a 5-MHz, 128-channel linear array was used to simultaneously obtain B-mode image data, time-of-flight data and attenuation data. The sound velocity and attenuation coefficient can be reconstructed inside and outside a region of interest manually selected in the B-mode image. To reduce distortion caused by tissue inhomogeneities, an optimal filter derived from pulse-echo data—with water replacing the breast tissue—is applied. We found that the sound velocities in CA, FA and fat tissues relative to those in the surrounding tissues were 49.8 ± 35.2, 2.6 ± 27.3 and −25.1 ± 44.9 m/s (mean ± SD), respectively, whereas the relative attenuation coefficients were 0.21 ± 0.58, 0.27 ± 0.62 and −0.02 ± 0.59 dB/cm/MHz. These results indicate that CA can be discriminated from FA and fat by choosing an appropriate threshold for the relative sound velocity ( i.e., 18.5 m/s). However, the large variations in the attenuation within the same type of tissue make simple thresholding ineffective. Nevertheless, the method described in this paper has the potential to reduce negative biopsies and to improve the accuracy of breast cancer detection in clinics. (E-mail: paichi@cc.ee.ntu.edu.tw)

Full Text
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