Reference-free calibration method for asynchronous rotation in robotic CT.

BACKGROUND: Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators. OBJECTIVE: We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model. METHODS: We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached. RESULTS: In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality. CONCLUSION: We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.

Sparse-view Spectral CT Reconstruction and Material Decomposition based on Multi-channel SGM.

In medical applications, the diffusion of contrast agents in tissue can reflect the physiological function of organisms, so it is valuable to quantify the distribution and content of contrast agents in the body over a period. Spectral CT has the advantages of multi-energy projection acquisition and material decomposition, which can quantify K-edge contrast agents. However, multiple repetitive spectral CT scans can cause excessive radiation doses. Sparse-view scanning is commonly used to reduce dose and scan time, but its reconstructed images are usually accompanied by streaking artifacts, which leads to inaccurate quantification of the contrast agents. To solve this problem, an unsupervised sparse-view spectral CT reconstruction and material decomposition algorithm based on the multi-channel score-based generative model (SGM) is proposed in this paper. First, multi-energy images and tissue images are used as multi-channel input data for SGM training. Secondly, the organism is multiply scanned in sparse views, and the trained SGM is utilized to generate multi-energy images and tissue images driven by sparse-view projections. After that, a material decomposition algorithm using tissue images generated by SGM as prior images for solving contrast agent images is established. Finally, the distribution and content of the contrast agents are obtained. The comparison and evaluation of this method are given in this paper, and a series of mouse scanning experiments are carried out to verify the effectiveness of the method.

A PCB image segmentation model based on rotational X-ray computed laminography imaging.

BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, with one integrating edge diffusion and l0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model.

Research on accuracy of material identification based on photon counting spectral CT.

BACKGROUND: Photon counting spectral CT is a significant direction in the development of CT technology and material identification is an important application of spectral CT. However, spectrum estimation in photon counting spectral CT is highly complex and may affect quantification accuracy of material identification. OBJECTIVE: To address the problem of energy spectrum estimation in photon-counting spectral CT, this study investigates empirical material decomposition algorithms to achieve accurate quantitative decomposition of the effective atomic number. METHODS: The spectrum is first calibrated using the empirical dual-energy calibration (EDEC) method and the effective atomic number is then quantitatively estimated based on the EDEC method. The accuracy of estimating the effective atomic number of materials under different calibration conditions is investigated by designing different calibration phantoms, and accurate quantitation is achieved using suitable calibration settings. Last, the validity of this method is verified through simulations and experimental studies. RESULTS: The results demonstrate that the error in estimating the effective atomic number is reduced to within 4% for low and medium Z materials, thereby enabling accurate material identification. CONCLUSION: The empirical dual-energy correction method can solve the problem of energy spectrum estimation in photon counting spectral CT. Accurate effective atomic number estimation can be achieved with suitable calibration.

Correction of motion artifact in CL based on MAFusNet.

Computed laminography (CL) is one of the best methods for nondestructive testing of plate-like objects. If the object and the detector move continually while the scanning is being done, the data acquisition efficiency of CL will be significantly increased. However, the projection images will contain motion artifact as a result. A multi-angle fusion network (MAFusNet) is presented in order to correct the motion artifact of CL projection images considering the properties of CL projection images. The multi-angle fusion module significantly increases the ability of MAFusNet to deblur by using data from nearby projection images, and the feature fusion module lessens information loss brought on by data flow between the encoders. In contrast to conventional deblurring networks, the MAFusNet network employs synthetic datasets for training and performed well on realistic data, proving the network's outstanding generalization. The multi-angle fusion-based network has a significant improvement in the correction effect of CL motion artifact through ablation study and comparison with existing classical deblurring networks, and the synthetic training dataset can also significantly lower the training cost, which can effectively improve the quality and efficiency of CL imaging in industrial nondestructive testing.

Detector shifting and deep learning based ring artifact correction method for low-dose CT.

BACKGROUND: In x-ray computed tomography (CT), the gain inconsistency of detector units leads to ring artifacts in the reconstructed images, seriously destroys the image structure, and is not conducive to image recognition. In addition, to reduce radiation dose and scanning time, especially photon counting CT, low-dose CT is required, so it is important to reduce the noise and suppress ring artifacts in low-dose CT images simultaneously. PURPOSE: Deep learning is an effective method to suppress ring artifacts, but there are still residual artifacts in corrected images. And the feature recognition ability of the network for ring artifacts decreases due to the effect of noise in the low-dose CT images. In this paper, a method is proposed to achieve noise reduction and ring artifact removal simultaneously. METHODS: To solve these problems, we propose a ring artifact correction method for low-dose CT based on detector shifting and deep learning in this paper. Firstly, at the CT scanning stage, the detector horizontally shifts randomly at each projection to alleviate the ring artifacts as front processing. Thus, the ring artifacts are transformed into dispersed noise in front processed images. Secondly, deep learning is used for dispersed noise and statistical noise reduction. RESULTS: Both simulation and real data experiments are conducted to evaluate the proposed method. Compared to other methods, the results show that the proposed method in this paper has better effect on removing ring artifacts in the low-dose CT images. Specifically, the RMSEs and SSIMs of the two sets of simulated and experiment data are better compared to the raw images significantly. CONCLUSIONS: The method proposed in this paper combines detector shifting and deep learning to remove ring artifacts and statistical noise simultaneously. The results show that the proposed method is able to get better performance.

A wavelet transform-based photon starvation artifacts suppression algorithm in CT imaging.

In computed tomography, high attenuation occurs when x-rays pass through a dense region or a long path in the scanning object. In this case, only limited photons reach the detector, which causes photon starvation artifacts. The artifacts usually appear as streaks along the directions with high attenuation. It might lower the discrimination of minor structures and lead to misdiagnosis. Applying a local filter to the projection data adaptively is a common solution, however, if the parameters of projection-based filter are not well selected, new artifacts and noise might appear in the final image. In this paper, a post image processing technique was developed to suppress the photon starvation streak artifacts. Based on the directional characteristics of streaks, a semi-adaptive anisotropic diffusion filter was applied to the high frequency sub-bands after wavelet transformation (WASA). Qualitative and quantitative experiments were performed on phantom data and clinical data to prove the effectiveness of this method for photon starvation artifact suppression.

Novel compact high-speed passive gamma-ray assay equipment for uranium enrichment measurement of fuel rods.

Uranium enrichment measurement is an essential quality inspection for fuel rods before delivery to users. Generally, compared with active neutron assay (ANA) equipment, passive gamma-ray assay (PGA) equipment is more economical and safer. However, the current PGA equipment based on photomultipliers is too slow (1 m/min) to meet the growing needs in China. Recently, we have developed a set of compact high-speed PGA equipment including four detection modules (128 units in total), a 128-channel data acquisition system (DAS), a power supply, special software, and an automatic loading and unloading mechanism. The detection unit is based on silicon photomultipliers in virtue of its compact size and good performance. The DAS processes signals of all units in parallel into a sequence of data packets carrying the energy information and the corresponding unit ID. The software integrates the data packets into a fluctuating count curve in a time-delay superposition method and identifies possible abnormal pellets. After calibrations, our equipment can locate abnormal pellets accurately at a speed of 6 m/min. In addition, it can directly measure the enrichment of fresh pellets not in secular equilibrium without waiting for two months. So far, the equipment has been successfully run for one year on the assembly line of China North Nuclear Fuel Co. and shows good potential to replace the traditional ANA equipment.

Review of CT image reconstruction open source toolkits.

Computed tomography (CT) has been widely applied in medical diagnosis, nondestructive evaluation, homeland security, and other science and engineering applications. Image reconstruction is one of the core CT imaging technologies. In this review paper, we systematically reviewed the currently publicly available CT image reconstruction open source toolkits in the aspects of their environments, object models, imaging geometries, and algorithms. In addition to analytic and iterative algorithms, deep learning reconstruction networks and open codes are also reviewed as the third category of reconstruction algorithms. This systematic summary of the publicly available software platforms will help facilitate CT research and development.

A micro-CL system and its applications.

The computed laminography (CL) method is preferable to computed tomography for the non-destructive testing of plate-like objects. A micro-CL system is developed for three-dimensional imaging of plate-like objects. The details of the micro-CL system are described, including the system architecture, scanning modes, and reconstruction algorithm. The experiment results of plate-like fossils, insulated gate bipolar translator module, ball grid array packaging, and printed circuit board are also presented to demonstrate micro-CL's ability for 3D imaging of flat specimens and universal applicability in various fields.

Depth discrimination method based on a multirow linear array detector for push-broom Compton scatter imaging.

A depth discrimination method is devised based on a multirow linear array detector for push-broom Compton scatter imaging. Two or more rows of detector modules are placed at different positions towards a sample. An improved parallel-hole collimator is fixed in front of the modules to restrict their fields of view. The depth information could be indicated by comparing the signal differences. In addition, an available detector and several related simulations using GEANT4 are given to support the method well.

[Three-dimensional digital dental model based on micro-CT].

OBJECTIVE: To establish a three-dimensional digital dental model through scanning dental impression directly with micro-CT. METHODS: The polyvinyl siloxane (PVS) impression of the plaster model was taken and scanned with micro-CT. VGStudio MAX and Imageware softwares were used to obtain the digital dental model. RESULTS: The three-dimensional digital model was established successfully. The scanning layer was 90 µm. CONCLUSIONS: A new way of establishing the digital dental models could be achieved with micro-CT.