Defect detection in CT scans of cast aluminum parts: A machine vision perspective

Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks

X-ray computed tomography scans can provide detailed information about the state of the material after manufacture and in service. X-ray computed tomography aided engineering (XAE) was recently introduced as an automated process to transfer 3D image data to finite element models. The implementation of a structure tensor code for material orientation analysis in combination with a newly developed integration point-wise fibre orientation mapping allows an easy applicable, computationally cheap, fast, and accurate model set-up. The robustness of the proposed approach is demonstrated on a non-crimp fabric glass fibre reinforced composite for a low resolution case with a voxel size of 64 μm corresponding to more than three times the fibre diameter. Even though 99.8% of the original image data is removed, the simulated elastic modulus of the considered non-crimp fabric composite is only underestimated by 4.7% compared to the simulation result based on the original high resolution scan. 

Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions

Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.

/pdf/sparse-angle-cbct-reconstruction-based-on-guided-image-1d8htkzt.pdf

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering

X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression.

/pdf/a-novel-reconstruction-of-the-sparse-view-cbct-algorithm-for-cx1znm6i.pdf

A Novel Reconstruction of the Sparse-View CBCT Algorithm for Correcting Artifacts and Reducing Noise

Ring artifacts seriously deteriorate the quality of CT images. Intensity-dependence of detector responses will result in intensity-dependent ring artifacts and time-dependence of CT hardware systems will result in time-dependent ring artifacts. However, only the intensity-dependent ring artifacts are taken into consideration in most post-processing methods. Therefore, the purpose of this study is to propose a general post-processing method, which has a significant removal effect on the intensity-dependent ring artifacts and the time-dependent ring artifacts. First in the proposed method, transform raw CT images into polar coordinate images, and the ring artifacts will manifest as stripe artifacts. Secondly, obtain structure images by smoothing the polar coordinate images and acquire texture images containing some details and stripe artifacts by subtracting the structure images from the polar coordinate images. Third, extract the stripe artifacts from the texture images using mean extraction and texture classification, and obtain the extracted ring artifacts by transforming the extracted stripe artifacts from polar coordinates into Cartesian coordinates. Finally, obtain corrected CT images by subtracting the extracted ring artifacts from the raw CT images, and iterate the corrected CT images in above steps until the ring artifacts extracted in the last iteration are weak enough. Simulation and real data show that the proposed method can remove the intensity-dependent ring artifacts and the time-dependent ring artifacts effectively while preserving image details and spatial resolution. In particular, real data prove that the method is suitable for new CT systems such as the photon counting CT.

Post-processing method for the removal of mixed ring artifacts in CT images

This study aims to address and test a new fusion reconstruction algorithm (FRA) applying to ill-posed projection to suppress noise artifacts and to improve the image quality, by combining iterative algorithm to the filtered backprojection (FBP) for computed tomography (CT). The reconstruction technique based on iterative reconstruction (IR) is used to approximate the value of the ill-posed pixel, then the forward projection operator is employed to obtain a new sinogram as a compensation item, and the superior one is selected to replace the inferior projection which is reconstructed from FBP. By utilizing the flexibility of the tradeoff coefficient in terms of sinogram updating, the new projection was constructed and the artifacts were eliminated/reduced. The study results show an effective suppression in noise artifacts of ill-posed projection. For simulation model # Blade and # Pi, the Normalized Mean Square Distance (NMSD), and Normal Average Absolute Distance (NAAD), with the proposed method were reduced by 70% above, Universal Quality Index (UQI) was increased by 60% above; It also indicates better uniformity of the results to the real projection of # Yueya, where the NMSDs and NAADs are well reduced by 28.82% and 29.34% for our proposed method, and UQIs are well improved about 65.96%. Based on the global noise suppression of the IR and the accuracy of the FBP algorithm, it is encouraged by the promising performance of the proposed algorithm to artifact reduction and detail preservation. Studies on simulation phantom and real projections were verified on the CT system. The results demonstrate the potential of our method for medical and industrial imaging.

Fusion reconstruction algorithm to ill-posed projection (FRAiPP) for artifacts suppression on X-ray computed tomography

Since the ideal image is difficult to be stripped from the actual sampling, this study aims to address and test a shared noise which as the knowledge and is included in the signal applying to the sampled projection to generate high qualified X-ray imaging by reducing the artifacts in computed tomography (CT). Combined with the randomness of the noise, the prereconstruction of the original projection is performed first, and then the forward projection which contains the equivalent noise in the image is obtained. Based on the slice updating, the forward and reconstruction processing is employed again. By means of threshold setting, multiple forward projections are accumulated, whereas the noise upon them will be reduced by averaging process. The noise is suppressed, and the expected information emerges, simultaneously. Study results show effective results, and the proposed method is practical and attractive as a preferred solution to CT artifacts suppression. It provides reliable guarantee for the CT inspection of internal and external dimensions.

High quality x-ray imaging based on cumulation of forward projection of computed tomography

Structural light measurement as a non-contact measurement method is commonly used in 3D shape detection, which can quickly acquire large-scale points cloud data of 3D surface with high precision. In the development of triangulation structural light sensors, the extraction of the light stripe centerline is the most important research point. Aiming at the problems of large error, high computational complexity and low data processing efficiency in the traditional maximum value based centerline extraction methods, a novel centerline extraction method based on actual light intensity distribution is proposed. Compared with the center line extraction method based on normal direction of light stripe, the discussed method is more suitable to describe the spatial characteristics of light stripe energy structure. It can greatly reduce the amount of calculation, improve processing speed and accuracy. The effectiveness of the proposed method is verified by a practical case of structural light sensor development.

A novel method for extracting structural light stripe centerline based on light intensity distribution direction

Streaking artifacts suppression for cone-beam computed tomography with the residual learning in neural network

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