ArticleIEEE transactions on computational imaging2021
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems.
Article in IEEE transactions on computational imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport.Journal of biomedical optics · 2026Article
- Fast 3D Partial Boundary Data EIT Reconstructions Using Direct Inversion CGO-Based Methods.IEEE transactions on bio-medical engineering · 2026Article
- Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography.IEEE transactions on bio-medical engineering · 2026Article
- Direct Estimation of Electric Field Distribution in Circular ECT Sensors Using Graph Convolutional Networks.Sensors (Basel, Switzerland) · 2025Article
- Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing.Biomedical optics express · 2024Article
- Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.Biomedical optics express · 2024Article
- Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging⋆.Physiological measurement · 2023Article
- Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.Sensors (Basel, Switzerland) · 2023Article
- Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Advances of deep learning in electrical impedance tomography image reconstruction.Frontiers in bioengineering and biotechnology · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has good generalizability to different domain shapes and meshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.