MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks

• URL: http://arxiv.org/abs/2503.00762v1
• Date: Sun, 02 Mar 2025 07:06:42 GMT
• Title: MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks
• Authors: Fangming Shi, Jinzhen Liu, Xiangqian Meng, Yapeng Zhou, Hui Xiong,
• Abstract summary: This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT)<n>It is capable of operating in both supervised and unsupervised learning modes.<n> Experimental results indicate that MR-EIT outperforms the comparison methods in terms of Structural Similarity (SSIM) and Relative Image Error (RIE)
• Score: 14.303339179604537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT), referred to as MR-EIT, which is capable of operating in both supervised and unsupervised learning modes. MR-EIT integrates an ordered feature extraction module and an unordered coordinate feature expression module. The former achieves the mapping from voltage to two-dimensional conductivity features through pre-training, while the latter realizes multi-resolution reconstruction independent of the order and size of the input sequence by utilizing symmetric functions and local feature extraction mechanisms. In the data-driven mode, MR-EIT reconstructs high-resolution images from low-resolution data of finite element meshes through two stages of pre-training and joint training, and demonstrates excellent performance in simulation experiments. In the unsupervised learning mode, MR-EIT does not require pre-training data and performs iterative optimization solely based on measured voltages to rapidly achieve image reconstruction from low to high resolution. It shows robustness to noise and efficient super-resolution reconstruction capabilities in both simulation and real water tank experiments. Experimental results indicate that MR-EIT outperforms the comparison methods in terms of Structural Similarity (SSIM) and Relative Image Error (RIE), especially in the unsupervised learning mode, where it can significantly reduce the number of iterations and improve image reconstruction quality.

Related papers

• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography [9.873236202827]
  Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution.<n>Deep learning approaches have shown promise but often rely on complex network architectures with a large number of parameters.<n>We propose an Ultra-Lightweight Quantum-Assisted Inference framework for EIT image reconstruction.
  arXiv  Detail & Related papers  (2025-07-18T15:57:53Z)
• Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding [50.54887630778593]
  Compressive imaging (CI) reconstruction aims to recover high-dimensional images from low-dimensional measurements compressed.<n>Existing unsupervised representations may struggle to achieve a desired balance between representation ability and efficiency.<n>We propose Decomposed multi-resolution Grid encoding (GridTD), an unsupervised continuous representation framework for CI reconstruction.
  arXiv  Detail & Related papers  (2025-07-10T12:36:20Z)
• Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer [10.761506243784744]
  Interferometric Hyperspectral Imaging (IHI) is a critical technique for large-scale remote sensing tasks.<n>IHI is susceptible to complex errors arising from imaging steps, and its quality is limited by existing signal processing-based reconstruction algorithms.<n>We propose a novel IHI reconstruction pipeline to address two key challenges: the lack of training datasets and the difficulty in eliminating IHI-specific degradation components.
  arXiv  Detail & Related papers  (2025-06-27T03:36:00Z)
• Conditional Diffusion Model for Electrical Impedance Tomography [17.831065873724153]
  Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing.<n>Due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image.<n>A conditional diffusion model with voltage consistency (CDMVC) is proposed in this study to address this issue.
  arXiv  Detail & Related papers  (2025-01-10T07:58:38Z)
• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction [2.7802624923496353]
  We introduce the Magnetic Resonance Image Processing Transformer (MR-IPT), a ViT-based framework designed to enhance the generalizability and robustness of accelerated MRI reconstruction.<n>By leveraging a shared transformer backbone, MR-IPT effectively learns universal feature representations, allowing it to generalize across diverse reconstruction tasks.<n>Our findings suggest that transformer-based general models can significantly advance MRI reconstruction, offering improved adaptability and stability compared to traditional deep learning approaches.
  arXiv  Detail & Related papers  (2024-05-23T23:13:02Z)
• Efficient Visual State Space Model for Image Deblurring [83.57239834238035]
  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. We propose a simple yet effective visual state space model (EVSSM) for image deblurring.
  arXiv  Detail & Related papers  (2024-05-23T09:13:36Z)
• ESTformer: Transformer Utilizing Spatiotemporal Dependencies for EEG Super-resolution [14.2426667945505]
  ESTformer is an EEG framework utilizingtemporal dependencies based on the Transformer. The ESTformer applies positional encoding methods and the Multi-head Self-attention mechanism to the space and time dimensions.
  arXiv  Detail & Related papers  (2023-12-03T12:26:32Z)
• Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction [62.955327005837475]
  Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. We present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types.
  arXiv  Detail & Related papers  (2023-10-29T13:58:57Z)
• vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems [7.043932618116216]
  vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems) is a novel Deep Learning (DL)-based method for solving ill-posed inverse problems arising in Medical Imaging (MI) For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.
  arXiv  Detail & Related papers  (2023-09-18T17:26:22Z)
• Real-World Image Super-Resolution by Exclusionary Dual-Learning [98.36096041099906]
  Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input. Deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets. We propose Real-World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1-based cooperative learning.
  arXiv  Detail & Related papers  (2022-06-06T13:28:15Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition [1.933681537640272]
  Radiation-induced photocurrent in semiconductor devices can be simulated using complex physics-based models. It is computationally infeasible to evaluate detailed models for multiple individual circuit elements. We show a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large-scale circuit simulations.
  arXiv  Detail & Related papers  (2020-08-27T18:21:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.