Chang Chen-Han, Huang Sheng-Wen, Yang Hsin-Chia, Chou Yi-Hong, Li Pai-Chi
Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Ultrasound Med Biol. 2007 Nov;33(11):1681-7. doi: 10.1016/j.ultrasmedbio.2007.05.012. Epub 2007 Jul 16.
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.
本研究的目的是确定利用声速和组织衰减在临床上区分乳腺癌与健康组织的有效性。之前已提出重建声速和衰减系数分布的方法，并在仿组织体模上进行了测试。这些方法仅需要由线性换能器阵列采集的原始通道数据，因此可以在现有的临床系统上实现。在本文中，这些方法在临床数据上进行了测试。共评估了19例经活检证实的病例，包括5例癌（CA）、7例纤维腺瘤（FA）、6例脂肪组织（脂肪）和1例油囊肿。使用由一个5MHz、128通道线性阵列组成的单一成像设置来同时获取B模式图像数据、飞行时间数据和衰减数据。声速和衰减系数可以在B模式图像中手动选择的感兴趣区域内外进行重建。为了减少由组织不均匀性引起的失真，应用了一种从脉冲回波数据导出的最优滤波器（用水代替乳腺组织）。我们发现，CA、FA和脂肪组织相对于周围组织的声速分别为49.8±35.2、2.6±27.3和-25.1±44.9 m/s（平均值±标准差），而相对衰减系数分别为0.21±0.58、0.27±0.62和-0.02±0.59 dB/cm/MHz。这些结果表明，通过为相对声速选择一个合适的阈值（即18.5 m/s），可以将CA与FA和脂肪区分开来。然而，同一类型组织内衰减的巨大差异使得简单的阈值化无效。尽管如此，本文所述方法有潜力减少活检阴性，并提高临床乳腺癌检测的准确性。
