Intelligent Microwave System for Breast Cancer Detection

近年来，微波作为一种潜在的乳腺癌检测技术出现了，它避免了与X射线相关的不适、风险和成本以及MRI的过高成本。早期的基于微波的检测方式是基于介电特性的成像，目的是将高对比度的乳房内含物与癌症组织相关联。这些技术需要使用在超宽频率范围内工作的多个天线。然而，这些技术的挑战在于，典型的女性乳房是高度异质性的，包含一个由腺体脂肪和纤维结缔组织组成的复杂网络。此外，人体组织是高度分散的(频率相关)，因此使用宽带天线极其困难。由于乳腺组织介电特性的丰富变化，我们仍然相信微波成像具有强大的作用。为了解决早期基于微波的方法的两个基本挑战，我们建议开发近场天线探测器。与传统的近场探头形成鲜明对比的是，这些探头将被设计成在一个异常狭窄的频率范围内工作，同时保持高增益。狭窄的频率范围将使乳房组织的频率依赖性质变得无关紧要。该探头的近场方面，加上亚GHz频率，允许低功率非电离微波能量在乳房内显著穿透。因此，异常的存在将创造一个差异化的共振轮廓，因为异常不是乳房自然构成的一部分。为了使检测系统在实践中具有健壮性和适用性，我们将乳腺组织的异质性和不同女性之间的统计差异作为探头开发的一个组成部分。将开发神经网络形式的人工智能算法来训练探测器，以实现最大的检测概率。

In recent years, microwaves have emerged as a potential technique for breast cancer detection which avoids the discomfort, risks and costs associated with x-rays and excessive cost of MRI. Early microwave-based detection modalities were based on imaging of the dielectric properties with the goal of correlating high contrast breast inclusions with cancerous tissues. These techniques required the use of a plurality of antennas operating over an ultra-wide range of frequencies. The challenge with these techniques, however, was that a typical female breast is highly heterogeneous and contains a complex network of glandular adipose and fibroconnective tissues. Also, human tissues are highly dispersive (frequency-dependent), thus making the use of wideband antennas extremely difficult. Because of the rich variation in the dielectric properties of breast tissues, we remain convinced that microwave imaging has a strong role to play. To address the two fundamental challenges of earlier microwaves-based methods, we propose to develop near-field antenna probes. The probes will be designed to operate over an exceptionally narrow range of frequencies while maintaining high gain, in sharp contrast to traditional near-field probes. The narrow frequency range will render the frequency-dependent nature of the breast tissues irrelevant. The near-field aspect of the probe, coupled with sub-GHz frequencies, allow significant penetration of low-power non-ionizing microwave energy within the breast. Therefore, the presence of an anomaly will create a differentiated resonance profile since the anomaly is not part of the natural makeup of the breast. For the detection system to be robust and suitable in practice, we consider the heterogeneity of the breast tissue and statistical variation amongst different woman as an integral part of the probe development. Artificial intelligence algorithms in the form of neural networks will be developed to train the probe to achieve maximum detection probability.