Advances in X-ray computed tomography image processing methods and quantitative analysis

X-ray computed tomography (X-CT) is an important nondestructive three-dimensional imaging technique for characterizing internal structures across various fields, including geography, geoscience, materials science, biomedicine, and porous media research. Compared with destructive testing and two-dimensional section observation, X-CT enables the digital reconstruction of internal structures, providing a basis for quantitative characterization, geometric measurement, and process-oriented analysis. However, CT images are not direct optical records of a sample’s true structure; rather, they represent three-dimensional attenuation fields reconstructed from multi-angle projection data through mathematical algorithms. The grayscale value of each voxel depends on multiple factors, including material composition, density, X-ray energy spectrum, imaging geometry, detector response, sampling strategy, and reconstruction method. Therefore, grayscale values lack a simple one-to-one correspondence with material categories. Reliable structural interpretation requires a comprehensive processing workflow that transforms raw grayscale volume data into physically meaningful, computable, and comparable quantitative information.

Exploring the full X-CT processing chain, this review systematically summarizes the major methods, technical roles, and applicable limitations of image reconstruction, preprocessing, artifact correction, target segmentation, three-dimensional visualization, structural quantification, parameter extraction, and common software platforms. Image reconstruction determines the quality of the input data for subsequent processing, while preprocessing and artifact correction improve the signal-to-noise ratio, reduce systematic distortions, and enhance structural boundaries. Segmentation is the key step that converts continuous grayscale data into discrete structural categories such as pores, particles, fractures, matrix phases, or defects. Three-dimensional characterization and parameter extraction further generate quantitative indicators, including porosity, specific surface area, connectivity, tortuosity, and pore-network topology. Because these parameters derive from a chain of interdependent procedures, the reliability of CT-based quantitative analysis depends not only on individual algorithms but also on the consistency, transparency, and quality control of the entire workflow.

The review identifies noise, artifacts, insufficient spatial resolution, partial-volume effects, and grayscale overlap as the major factors restricting the accuracy and robustness of CT image analysis. Noise and artifacts may distort grayscale distributions and structural boundaries; insufficient spatial resolution and partial-volume effects may blur interfaces and lead to the omission or underestimation of small-scale pores, throats, and fractures; grayscale overlap in complex multiphase samples often triggers threshold-based segmentation instability. These uncertainties propagate and amplify along the processing chain, impacting the accuracy and comparability of porosity, connectivity, specific surface area, tortuosity, and topological parameters. In addition, differences in preprocessing strategies, segmentation algorithms, region-of-interest selection, parameter definitions, and software implementations may reduce reproducibility across studies. Therefore, CT-derived parameters must be understood as structural estimates under specific imaging conditions and processing workflows rather than absolute representations of the true structure.

Recent advances in artificial intelligence, three-dimensional quantitative analysis, multimodal fusion, and reproducible workflows are transforming X-CT image processing. Machine learning and deep learning methods provide new opportunities for denoising, artifact correction, and complex-structure segmentation, especially in low-contrast, noisy, or large-scale three-dimensional datasets. Nevertheless, model performance depends strongly on training-data representativeness, annotation quality, imaging conditions, generalization, and physical interpretability. By pairing X-CT with complementary techniques, such as FIB-SEM, EDS, EBSD, NMRI, or spectral imaging, multimodal fusion integrates structural, compositional, and functional data across scales, thereby extending CT analysis from morphological description to structure–composition–function interpretation. At the same time, standardized reporting, open data, open-source workflows, benchmark datasets, and uncertainty assessment are essential for improving reproducibility and cross-study comparability. Future X-CT image processing should not simply pursue algorithmic complexity or full automation. Instead, it must balance algorithm robustness, workflow transparency, and result verifiability, thereby enhancing the credibility of quantitative analysis of complex samples and strengthening the role of X-CT in cross-scale structural characterization and multidisciplinary research.