# Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction

arXiv: https://arxiv.org/abs/2601.15098 · HTML: https://mlsanity.com/paper/2601.15098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 271 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation](https://mlsanity.com/paper/2212.04497/) (2022)
- [A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction](https://mlsanity.com/paper/1610.09736/) (2016)
- [On hallucinations in tomographic image reconstruction](https://mlsanity.com/paper/2012.00646/) (2020)
- [Deep Shading: Convolutional Neural Networks for Screen-Space Shading](https://mlsanity.com/paper/1603.06078/) (2016)
- [TransCT: Dual-path Transformer for Low Dose Computed Tomography](https://mlsanity.com/paper/2103.00634/) (2021)
- [Sharpness-aware Low dose CT denoising using conditional generative adversarial network](https://mlsanity.com/paper/1708.06453/) (2017)
- [MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction](https://mlsanity.com/paper/2006.00171/) (2020)
- [From CNN to Transformer: A Review of Medical Image Segmentation Models](https://mlsanity.com/paper/2308.05305/) (2023)
- [Hierarchical 3D fully convolutional networks for multi-organ segmentation](https://mlsanity.com/paper/1704.06382/) (2017)
- [Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy](https://mlsanity.com/paper/2201.02867/) (2022)
- [Convolutional Sparse Coding for Compressed Sensing CT Reconstruction](https://mlsanity.com/paper/1903.08549/) (2019)
- [A Review on Deep Learning in Medical Image Reconstruction](https://mlsanity.com/paper/1906.10643/) (2019)
- [Deep Learning for Biomedical Image Reconstruction: A Survey](https://mlsanity.com/paper/2002.12351/) (2020)
- [Differentiable Direct Volume Rendering](https://mlsanity.com/paper/2107.12672/) (2021)
- [Deep Learning for Low-Dose CT Denoising](https://mlsanity.com/paper/1902.10127/) (2019)
- [Deep Volumetric Ambient Occlusion](https://mlsanity.com/paper/2008.08345/) (2020)
- [Binary segmentation of medical images using implicit spline representations and deep learning](https://mlsanity.com/paper/2102.12759/) (2021)
- [Inviwo -- A Visualization System with Usage Abstraction Levels](https://mlsanity.com/paper/1811.12517/) (2018)
- [Real-Time Denoising of Volumetric Path Tracing for Direct Volume Rendering](https://mlsanity.com/paper/2106.08034/) (2021)
- [Flux-Limited Diffusion for Multiple Scattering in Participating Media](https://mlsanity.com/paper/1403.8105/) (2014)
- [FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels](https://mlsanity.com/paper/1810.05220/) (2018)

## Built on by (most cited first, 1 of 1)

- [SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction](https://mlsanity.com/paper/2609.13688/) (2026)

## 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).