微波成像
介电常数
电介质
迭代重建
微波食品加热
计算机科学
断层摄影术
一般化
材料科学
热声学
声学
生物系统
人工智能
光学
物理
光电子学
数学
电信
数学分析
生物
作者
Zhaoxu Luo,Chenzhe Li,Dantong Liu,Baosheng Wang,Lejia Zhang,Yuexin Ma,Kuiwen Xu,Xiong Wang
标识
DOI:10.1109/tmtt.2023.3234466
摘要
Quantitative reconstruction of dielectric properties has enabled a wealth of biomedical applications. Although traditional microwave imaging and microwave-induced thermoacoustic tomography (MITAT) techniques have been widely explored for quantitative reconstruction, it is still highly challenging for them to deal with biological samples with high permittivity and conductivity. This work leverages deep-learning-enabled MITAT (DL-MITAT) approach to quantitatively reconstruct dielectric properties of biological samples with high quality. We construct a new network structure to separately reconstruct the permittivity and conductivity. By simulation and experimental testing, we demonstrate that the DL-MITAT technique is able to reliably reconstruct inhomogeneous biological samples with tumor, muscle, and fat. The experimental reconstruction error is only 5%. The network exhibits excellent generalization capability in terms of sample's geometry. This work provides a useful paradigm and alternative way for quantitative reconstruction of dielectric properties and paves the way toward practical applications.
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