A brain anomaly localization algorithm in an unsupervised machine learning (ML) framework is presented for electromagnetic brain imaging. The method is based on expected value estimation and takes the advantage of the highly symmetrical human brain. The algorithm processes signals collected from pairs of antennas that are positioned symmetrically around the head, discretizes the imaging domain into pixels, and computes the statistical fields between the antennas on the left and right sides of the head. Then, it concatenates their intensities along the axis normal to the imaging domain to compute the expected value for every pixel. The computed expected values are merged into a matrix containing expected values for all pixels. Pixels with higher intensity show the likelihood of an anomaly being present at that location. The assumption on brain symmetry from the electromagnetic perspective was tested on healthy volunteers using a 14-element array system with a working frequency band of 0.5 - 2.0 GHz. The obtained average similarity is 92% and it confirms the validity of the assumption. The same system is used to test the algorithm on different scenarios in simulations and experiments using realistic 3D head phantoms designed based on MRIs of real patients. The imaging results demonstrate the capability of the proposed algorithm to localize bleeding and estimate its size with less than 10% error in less than a minute, which makes it suitable for real-time use in emergency stroke scenarios.

中文翻译：

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电磁成像中脑异常定位的无监督算法

提出了一种无监督机器学习 (ML) 框架中的脑异常定位算法，用于电磁脑成像。该方法基于期望值估计，并利用了高度对称的人脑。该算法处理从围绕头部对称放置的天线对收集的信号，将成像域离散为像素，并计算头部左右两侧天线之间的统计场。然后，它沿着垂直于成像域的轴连接它们的强度，以计算每个像素的预期值。计算出的期望值被合并到一个矩阵中，该矩阵包含所有像素的期望值。具有较高强度的像素显示该位置存在异常的可能性。使用工作频段为 0.5 - 2.0 GHz 的 14 单元阵列系统对健康志愿者进行了从电磁角度对大脑对称性的假设进行测试。得到的平均相似度为 92%，证实了假设的有效性。使用基于真实患者 MRI 设计的逼真 3D 头部模型，使用相同的系统在模拟和实验中的不同场景中测试算法。成像结果证明了所提出的算法能够在不到一分钟的时间内以小于 10% 的误差对出血进行定位并估计其大小，这使其适合在紧急中风场景中实时使用。得到的平均相似度为 92%，证实了假设的有效性。使用基于真实患者 MRI 设计的逼真 3D 头部模型，使用相同的系统在模拟和实验中的不同场景中测试算法。成像结果证明了所提出的算法能够在不到一分钟的时间内以小于 10% 的误差对出血进行定位并估计其大小，这使其适合在紧急中风场景中实时使用。得到的平均相似度为 92%，证实了假设的有效性。使用基于真实患者 MRI 设计的逼真 3D 头部模型，使用相同的系统在模拟和实验中的不同场景中测试算法。成像结果证明了所提出的算法能够在不到一分钟的时间内以小于 10% 的误差对出血进行定位并估计其大小，这使其适合在紧急中风场景中实时使用。