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Enhancement of Gauss–Newton Inversion Method for Biological Tissue Imaging

机译:高斯-牛顿反演方法在生物组织成像中的应用

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摘要

The multiplicatively regularized Gauss–Newton inversion (GNI) algorithm is enhanced and utilized to obtain complex permittivity profiles of biological objects-of-interest. The microwave scattering data is acquired using a microwave tomography system comprised of 24 co-resident antennas immersed into a saltwater matching fluid. Two types of biological targets are imaged: ex vivo bovine legs and in vivo human forearms. Four different forms of the GNI algorithm are implemented: a blind inversion, a balanced inversion, a shape-and-location inversion, and a novel balanced shape-and-location inversion. The latter three techniques incorporate typical permittivity values of biological tissues as prior information to enhance the reconstructions. In those images obtained using the balanced shape-and-location reconstruction algorithm, the various parts of the tissue being imaged are clearly distinguishable. The reconstructed permittivity values in the bovine leg images agree with those obtained via direct measurement using a dielectric probe. The reconstructed images of the human forearms qualitatively agree with magnetic resonance imaging images, as well as with the expected dielectric values obtained from the literature.
机译:<?Pub Dtl?>乘法正则化的高斯-牛顿反演(GNI)算法得到增强,并用于获得感兴趣的生物对象的复杂介电常数分布。微波散射数据是使用微波层析成像系统获取的,该系统由浸入盐水匹配液中的24个共存天线组成。对两种类型的生物靶标成像:离体牛腿和体内人前臂。实现了GNI算法的四种不同形式:盲目反演,平衡反演,形状和位置反演以及新颖的平衡形状和位置反演。后三种技术结合了生物组织的典型介电常数值作为增强重建的先验信息。在使用平衡的形状和位置重建算法获得的那些图像中,可以清晰地区分正在成像的组织的各个部分。牛腿图像中重建的介电常数值与通过使用介电探针直接测量获得的介电常数值一致。人类前臂的重建图像在质量上与磁共振成像图像以及从文献中获得的预期介电值一致。

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