电阻抗断层成像
迭代重建
反问题
计算机科学
巴(单位)
噪音(视频)
先验与后验
算法
人工智能
断层摄影术
计算机视觉
图像(数学)
数学
物理
光学
数学分析
哲学
认识论
气象学
标识
DOI:10.1088/1361-6579/aa63d7
摘要
Electrical impedance tomography (EIT) is an emerging imaging modality that uses harmless electrical measurements taken on electrodes at a body's surface to recover information about the internal electrical conductivity and or permittivity. The image reconstruction task of EIT is a highly nonlinear inverse problem that is sensitive to noise and modeling errors making the image reconstruction task challenging. D-bar methods solve the nonlinear problem directly, bypassing the need for detailed and time-intensive forward models, to provide absolute (static) as well as time-difference EIT images. Coupling the D-bar methodology with the inclusion of high confidence a priori data results in a noise-robust regularized image reconstruction method. In this work, the a priori D-bar method for complex admittivities is demonstrated effective on experimental tank data for absolute imaging for the first time. Additionally, the method is adjusted for, and tested on, time-difference imaging scenarios. The ability of the method to be used for conductivity, permittivity, absolute as well as time-difference imaging provides the user with great flexibility without a high computational cost.
科研通智能强力驱动
Strongly Powered by AbleSci AI