Zhao Mingkang, Wi Hun, Lee Eun Jung, Woo Eung Je, Oh Tong In
Impedance Imaging Research Center and Department of Biomedical Engineering, Kyung Hee University, Korea.
Phys Med Biol. 2014 Oct 7;59(19):5831-47. doi: 10.1088/0031-9155/59/19/5831. Epub 2014 Sep 10.
Electrical impedance imaging has the potential to detect an early stage of breast cancer due to higher admittivity values compared with those of normal breast tissues. The tumor size and extent of axillary lymph node involvement are important parameters to evaluate the breast cancer survival rate. Additionally, the anomaly characterization is required to distinguish a malignant tumor from a benign tumor. In order to overcome the limitation of breast cancer detection using impedance measurement probes, we developed the high density trans-admittance mammography (TAM) system with 60 × 60 electrode array and produced trans-admittance maps obtained at several frequency pairs. We applied the anomaly detection algorithm to the high density TAM system for estimating the volume and position of breast tumor. We tested four different sizes of anomaly with three different conductivity contrasts at four different depths. From multifrequency trans-admittance maps, we can readily observe the transversal position and estimate its volume and depth. Specially, the depth estimated values were obtained accurately, which were independent to the size and conductivity contrast when applying the new formula using Laplacian of trans-admittance map. The volume estimation was dependent on the conductivity contrast between anomaly and background in the breast phantom. We characterized two testing anomalies using frequency difference trans-admittance data to eliminate the dependency of anomaly position and size. We confirmed the anomaly detection and characterization algorithm with the high density TAM system on bovine breast tissue. Both results showed the feasibility of detecting the size and position of anomaly and tissue characterization for screening the breast cancer.
由于与正常乳腺组织相比具有更高的导纳值,电阻抗成像有潜力检测乳腺癌的早期阶段。肿瘤大小和腋窝淋巴结受累程度是评估乳腺癌生存率的重要参数。此外,需要进行异常特征化以区分恶性肿瘤和良性肿瘤。为了克服使用阻抗测量探头检测乳腺癌的局限性,我们开发了具有60×60电极阵列的高密度跨导纳乳腺摄影(TAM)系统,并生成了在几个频率对下获得的跨导纳图。我们将异常检测算法应用于高密度TAM系统,以估计乳腺肿瘤的体积和位置。我们在四个不同深度测试了四种不同大小的异常,具有三种不同的电导率对比度。从多频跨导纳图中,我们可以很容易地观察到横向位置并估计其体积和深度。特别地,当应用使用跨导纳图的拉普拉斯算子的新公式时,可以准确获得深度估计值,该值与大小和电导率对比度无关。体积估计取决于乳腺模型中异常与背景之间的电导率对比度。我们使用频率差跨导纳数据对两个测试异常进行特征化,以消除异常位置和大小的依赖性。我们在牛乳腺组织上用高密度TAM系统验证了异常检测和特征化算法。两个结果都表明了检测异常大小和位置以及进行组织特征化以筛查乳腺癌的可行性。