# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 2 The Development History of CT Technology
### Development of CT Technology  
Since its inception, CT has been subject to a series of advancements in technology, proving indispensable in the examination of porous substances without causing damage. Despite these developments, achieving precision in measurements remains a complex task, necessitating ongoing refinement and calibration [59]. Discrepancies in the dimensional assessments conducted by CT can be attributed to a multitude of elements, such as the setup of the testing apparatus, the constraints on image resolution, the emissions of radiation, subsequent image processing, and the intrinsic characteristics of the tested materials [60]. [PERSON] et al. [61] crafted an automated calibration technique that leveraged the pattern of sine waves to precisely rectify any misalignment of the rotational axis. [PERSON] et al. [62] noted that the scope of CT assessments is predominantly confined by the extent to which X-rays can penetrate various materials. They advocate for enhancements in the precision of CT by fine-tuning its resolution and the scope of its measurements. In [63], they further explore how to determine the optimal measurement angle for samples under a rotation axis tilt angle of 10-35\({}^{\circ}\) by simulating the penetration pathway of X-rays and spatial analysis to minimize discrepancies within dimensional measurement. With the introduction of spiral CT scanning technology [64], the traditional CT scanning method has been replaced by this new technology. Furthermore, the improvement in CT imaging resolution is pivotal for the ongoing evolution of CT technology. [PERSON] et al. [65] underscored the importance of image clarity in the identification and delineation of the pore space structure, a process that necessitates a higher resolution, albeit with the potential introduction of increased noise. The potency of the X-ray beams significantly influences the signal-to-noise ratio (SNR), and the fine-tuning of this ratio is imperative for mitigating the adverse effects of X-ray scatter on the integrity of the image [66; 67]. Conventional reconstruction methods, exemplified by filtered back projection (FBP), are adept at generating high-fidelity images; however, their effectiveness is predicated on certain idealized conditions, such as uniform X-ray photon energy and unvaried photon trajectories, which are not always attainable in practical scenarios and may lead to reduced accuracy in the stages of image segmentation and object recognition [68]. In recent years, deep learning reconstruction (DLR) algorithms [69] have been introduced to correct noise issues in CT images. Especially in dual-energy CT (DECT) [70], image reconstruction technology based on the total variable (SART-TV) [71] has shown excellent performance in suppressing artifacts and resolving the issue of sinogram loss. [PERSON] et al. [72] made technical improvements to reduce the impact of the non-ideal focal spot size of the X-ray source on image quality. Reducing the number of scans step by step (e.g., high-speed CT scans [73]) can effectively avoid motion artifacts [74]. The post-scanning image summation technique, known as fused CT, diminishes image noise [75], and the incorporation of contrast agents significantly boosts the contrast and overall image quality of the examined region [76; 77]. After reviewing the application and development of CT technology in the non-destructive testing of porous materials (such as geotechnical materials), the importance of obtaining high-quality CT images becomes evident. However, these images alone are inadequate for conducting a quantitative assessment; techniques for image analysis are also required to extract and interpret useful information. Techniques for imaging analysis still pose challenges in determining pore boundaries or analyzing errors and distortions caused by CT reconstruction [78].