Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction
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Abstract (arXiv)
As Micro-CT technology continues to refine its characterization of material microstructures, industrial CT ultra-precision inspection is generating increasingly large datasets, necessitating solutions to the trade-off between accuracy and efficiency in the 3D characterization of defects during ultra-precise detection. This article provides a unique perspective on recent advances in accurate and efficient 3D visualization using Micro-CT, tracing its evolution from medical imaging to industrial non-destructive testing (NDT). Among the numerous CT reconstruction and volume rendering methods, this article selectively reviews and analyzes approaches that balance accuracy and efficiency, offering a comprehensive analysis to help researchers quickly grasp highly efficient and accurate 3D reconstruction methods for microscopic features. By comparing the principles of computed tomography with advancements in microstructural technology, this article examines the evolution of CT reconstruction algorithms from analytical methods to deep learning techniques, as well as improvements in volume rendering algorithms, acceleration, and data reduction. Additionally, it explores advanced lighting models for high-accuracy, photorealistic, and efficient volume rendering. Furthermore, this article envisions potential directions in CT reconstruction and volume rendering. It aims to guide future research in quickly selecting efficient and precise methods and developing new ideas and approaches for real-time online monitoring of internal material defects through virtual-physical interaction, for applying digital twin model to structural health monitoring (SHM).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 271 references that are in the map. First posted on arXiv 21 Jan 2026.
Builds on
28 of 271 references.
References · 28
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
- 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
- 2017Image reconstruction by domain transform manifold learningcited by 231
- 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
- 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 211
- 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
- 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
- 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
- 2020On hallucinations in tomographic image reconstructioncited by 66
- 2016Deep Shading: Convolutional Neural Networks for Screen-Space Shadingcited by 46
- 2021TransCT: Dual-path Transformer for Low Dose Computed Tomographycited by 35
16 more
- 2017Sharpness-aware Low dose CT denoising using conditional generative adversarial networkcited by 28
- 2020MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstructioncited by 23
- 2023From CNN to Transformer: A Review of Medical Image Segmentation Modelscited by 22
- 2017Hierarchical 3D fully convolutional networks for multi-organ segmentationcited by 19
- 2022Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopycited by 19
- 2019Convolutional Sparse Coding for Compressed Sensing CT Reconstructioncited by 17
- 2019A Review on Deep Learning in Medical Image Reconstructioncited by 17
- 2020Deep Learning for Biomedical Image Reconstruction: A Surveycited by 16
- 2019Deep Learning for Low-Dose CT Denoisingcited by 14
- 2021Differentiable Direct Volume Renderingcited by 14
- 2020Deep Volumetric Ambient Occlusioncited by 12
- 2021Binary segmentation of medical images using implicit spline representations and deep learningcited by 7
- 2018Inviwo -- A Visualization System with Usage Abstraction Levelscited by 5
- 2021Real-Time Denoising of Volumetric Path Tracing for Direct Volume Renderingcited by 5
- 2014Flux-Limited Diffusion for Multiple Scattering in Participating Mediacited by 2
- 2018FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxelscited by 1
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