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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: Jul 11, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

通过人工智能技术对非同位素物体进行微波成像.

Shu-Han Liao¹, Chien-Ching Chiu¹, Po-Hsiang Chen¹

¹Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

人工智能,特别是卷积神经网络 (CNN),成功地重建了双轴异轴物体的微波图像. 这种方法克服了横向电子 (TE) 两极化带来的挑战,比传统方案更有效.

关键词:

异型物体是异型物体.人工智能是一种人工智能.卷积神经网络的神经网络.介电物体是一种介电物体.反向散射问题反向散射问题微波成像技术微波成像技术

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Published on: November 25, 2009

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Last Updated: Jul 11, 2025

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科学领域:

电磁学电磁学电磁学电磁学
人工智能的人工智能
微波成像技术微波成像技术

背景情况:

由于介电常数不同,双轴异构散射器对微波成像具有复杂的挑战.
横向电子 (TE) 极化波比横向磁性 (TM) 波遇到更高的非线性,使图像重建复杂化.
现有的方法很难准确地重建微波图像从分散的野外信息的异质物体.

研究的目的:

开发和验证基于人工智能的方法,用于对双轴异轴物体的微波成像.
为了解决与微波成像中TE极化相关的复杂性.
将不同初始图像重建方案的有效性与CNN相比较.

主要方法:

使用主导电流方案 (DCS) 和反向传播方案 (BPS) 进行初始图像估计.
应用训练有素的卷积神经网络 (CNN) 来再生和完善微波图像.
进行了数值模拟,以评估CNN的性能和概括能力.

主要成果:

美国有线电视新闻网 (CNN) 展示了良好的概括能力,即使训练数据有限.
提出的基于CNN的方法成功地重建了双轴异轴物体的微波图像.
对比显示,当与CNN集成时,占主导地位的当前方案 (DCS) 超过了反向传播方案 (BPS).

结论:

卷积神经网络为涉及异质物体的复杂微波成像问题提供了一个有希望的解决方案.
由人工智能驱动的方法有效地克服了TE两极化带来的高非线性挑战.
与BPS相比,主导电流方案 (DCS) 是基于CNN的微波异性散射器图像重建的更合适的前体.