Multimodal Image Reconstruction of Electrical Impedance Tomography Using Kernel Method

Liu, Zhe; Yang, Yunjie

doi:10.1109/tim.2021.3132830

Cited by 14 publications

(7 citation statements)

References 38 publications

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“…Comparing the results with those obtained in other works [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] in the field of EIT reconstruction using machine learning methods, it can be concluded that at least some of the models presented in this work (especially the CART model) dominate the published achievements in terms of the obtained measures of reconstruction quality.…”

Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

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The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.

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Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

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show abstract

“…The segmentation algorithm needs to be carefully chosen and tuned for a specific application, and its complexity is even higher than the reconstruction algorithm in some cases. Liu et al further reported a kernel-based, segmentation-free dualmodal image reconstruction algorithm, which can alleviate the burden of selecting and tuning the segmentation algorithm meanwhile preserving inclusion's structure [24]. In addition, Li et al combined CT with EIT through Cross Gradient regularization [25] and Liang et al integrated ultrasound image into EIT [26] [27].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

et al. 2023

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The intrinsic issue of low spatial resolution of Electrical Impedance Tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance-optical dual-modal imaging framework and a deep learning model named Multi-Scale Feature Cross Fusion Network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework's performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This paper further proposes a two-stage deep neural network, which is the enhanced version of the MSFCF-Net (named En-MSFCF-Net), to automatically improve mask image and conduct information fusion and image reconstruction. Compared to MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, the En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and modelbased algorithms. Both qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.

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“…Recently, Liu et al reported an impedance optics-dual-modal imaging framework for 3D cell culture imaging, where they used a multiscale feature cross-fusion network (MSFCF-Net) to fuse the information between different modalities. In addition, Liu et al (2022a) also proposed a multimodal reconstruction algorithm based on the Kernel method that originated from machine learning and obtained excellent EIT images ( Liu and Yang, 2022 ). In comparison with traditional methods, multi-modality learning based on deep learning has several advantages.…”

Section: Discussionmentioning

confidence: 99%

Advances of deep learning in electrical impedance tomography image reconstruction

Zhang

Tian

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.

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Multimodal Image Reconstruction of Electrical Impedance Tomography Using Kernel Method

Cited by 14 publications

References 38 publications

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance–Optical Dual-Modal Imaging

Advances of deep learning in electrical impedance tomography image reconstruction

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