乳腺癌一直危害着女性的身体健康，因此乳腺癌的早期检测能让患者获得及时的治疗，这对提高患者的预后存活率极其重要。由于具有辐射小、成本低等优点，乳腺癌的微波近场成像逐渐受到人们关注，成为主要研究方向之一。微波共焦成像主要依据肿瘤组织与正常组织之间电特性的差异导致的超宽带脉冲信号在不同组织间发生的散射效应。对这些散射回波信号进行分析提取可获得肿瘤的位置、形状等信息。但由于回波信号中大部分均为皮肤、脂肪等组织的杂波信号，所需的肿瘤响应被掩盖。直接对回波信号进行微波共焦成像会产生大量伪影，无法呈现出肿瘤。针对这一问题，本文通过使用多种信号处理方法从回波信号中去除伪影信号，提取出肿瘤响应，并使用微波共焦成像检验伪影去除效果。

在以往的一些伪影去除研究中，模型的构建主要考虑皮肤、脂肪以及肿瘤等组织，很少考虑乳腺等组织构成的复杂组织分布。因此本文使用核磁共振成像生成的两种不同组织分布的二维仿真模型用以拟合真实情况。鉴于人体组织具有色散效应，本文论证了正常组织和肿瘤组织在不同频率下的电特性以及Cole-Cole模型，并研究超宽带脉冲信号为微波共焦成像提供的理论基础。在进行伪影去除的过程中，本文通过使用基于熵的时间窗算法确定时间窗口，并将该窗口长度应用于维纳滤波器构建滤波信号。由于最小二乘回归容易受到多重共线性影响，本文使用偏最小二乘回归进行求解得到滤波信号。对信号进行频谱分析发现肿瘤响应的频率相对较低，且伪影信号主要出现于信号早期阶段，因此本文使用小波变换对滤波信号进行降噪处理获得期望肿瘤响应。由于在单站方案的成像结果依然有部分伪影，本文通过对多站方案下的回波信号进行分组，计算各自分组的相似程度，将达到一定程度的分组信号分别进行伪影去除方法处理。

在单站方案中，本文对期望肿瘤响应进行微波共焦成像并与混合伪影去除方法（Hybrid Artifact Removal, HAR）和理想去除方法对比。在30个采集位置的情况下本文提出方法与HAR方法的结果相差不大。但在12个采集位置HAR方法无法呈现肿瘤的情况下，本文提出方法成功得到肿瘤图像。在多站方案中，本文对期望肿瘤响应进行微波共焦成像并与理想去除方法对比。得益于多站方案的信号分组，本文提出方法能成功去除伪影信号并呈现出准确的肿瘤图像，与理想去除方法的成像结果对比仅有较小差距。

Breast cancer has endangered women's health since years. Therefore, patients detected by the early detection of breast cancer can receive timely treatment, which is important to improve the prognosis of patients. Due to the advantages of less radiation and low cost, microwave near-field imaging become one of the major research directions, attracting attention from the researchers. Microwave confocal imaging relies on the scattering effect of ultra-wide band pulse signals propagating in different tissues caused by the difference of electrical characteristics between tumor and normal tissues. The information describing the location, shape of tumor can be extracted from the analysis of the scattered signals. However, since most of the energy in the scattered signals is occupied by the clutter signals of tissues such as skin and fat, the tumor response is obscure. Microwave confocal imaging of the scattered signals will produce artifacts and cannot present the tumor. Aiming at this problem, this thesis uses several signal processing methods to remove artifacts from the signals, extracts the tumor response, and then uses microwave confocal imaging to verify the effect of artifact removal method.

 

In some previous studies of artifact removal methods, the model merely considered skin, fat, and tumor, rather than the complex tissue such as gland and others. Therefore, two different two-dimensional simulation model constructed by MRI to fit the reality situation are illustrated. In view of the dispersive effect of the human tissues, this thesis demonstrates the electrical characteristics of normal and tumor tissues at different frequency and the Cole-Cole model, with the study of the precondition provided by ultra-wideband pulse signals for microwave confocal imaging. In the process of artifact removal, the entropy-based time window algorithm is used to determine the time window which helps the Wiener filter to construct the filtered signal. Due to the least square’s regression solution method is vulnerable to multicollinearity influence, the partial least square’s regression method is more effective to get the filtered signal. Through the spectrum analysis of the signal, it is found that the frequency of tumor response is relatively low, and the artifact signal mainly appears at the early stage of the signal. Therefore, the advantages of wavelet transform are stimulated for performing noise reduction of the filtered signal and obtaining expected tumor response. Because several artifacts in the image of the monostatic is nonnegligible, scattered signals are required to be grouped in the multistatic. The similarity degree in each group is calculated to evaluating the qualification whether the grouped signal deserves to process by artifact removal methods.

 

In the monostatic, the expected tumor response is processed by using microwave confocal imaging to produce image, compared with HAR and the ideal. It was found that in 30 acquisition positions, the results of the proposed method make little difference with HAR. Whereas, in the case of the results of HAR unable to depict the tumor in 12 acquisition positions, the results of the proposed illustrate the tumor precisely. In the multistatic, the proposed method will be compared with the ideal. Benefit by the signal grouping, the proposed method removes artifacts and present an image of the tumor accurately, with a small gap compared with the ideal removal method.

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