MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer.

Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information.

Incremental learning for detection in X-ray luggage perspective images.

Convolutional neural networks have achieved remarkable results in the detection of X-ray luggage contraband. However, with an increase in contraband classes and substantial artificial transformation, the offline network training method has been unable to accurately detect the rapidly growing new classes of contraband. The current model cannot incrementally learn the newly appearing classes in real time without retraining the model.

Evaluation of an Analytic Reconstruction Method as a Platform for Spectral Cone-beam CT.

With the fast development of photon counting detection techniques, spectral CT with a photon counting detector has attracted considerable attention by increasing energy-resolution to identify and discriminate materials. The conventional analytic reconstruction algorithms can be directly applied to reconstruct spectral CT images for each spectrum or energy bin. However, a comprehensive evaluation of analytic reconstruction algorithms for spectral CT has not been reported yet.

[Multi-Spectrum CT Imaging Method Based on Spectrum Matching Priors].

In the process of X-ray computed tomography (CT) imaging, the traditional single-energy X-ray CT imaging technology is only applicable to structural analysis but can’t meet the needs of functioning for substance distinction and identification because of the multispectral hardening artifacts and inconsistency between the projection acquisition process and reconstruction assumption. A multispectral CT imaging method based on the spectrum matching priors is presented. First, energy spectrum filtering matching model is built and range spectrum parameters are set according to the material composition; then multi-spectrum projection sequence is acquired by filtering.

High-Dynamic-Range CT Reconstruction Based on Varying Tube-Voltage Imaging.

For complicated structural components characterized by wide X-ray attenuation ranges, the conventional computed tomography (CT) imaging using a single tube-voltage at each rotation angle cannot obtain all structural information. This limitation results in a shortage of CT information, because the effective thickness of the components along the direction of X-ray penetration exceeds the limitation of the dynamic range of the X-ray imaging system. To address this problem, high-dynamic-range CT (HDR-CT) reconstruction is proposed.

Lattice Boltzmann modeling of permeability in porous materials with partially percolating voxels.

A partial-bounce-back lattice Boltzmann model has been used to simulate flow on a lattice consisting of cubic voxels with a locally varying effective percolating fraction. The effective percolating fraction of a voxel is the total response to the partial-bounce-back techniques for porous media flow due to subvoxel fine structures. The model has been verified against known analytic solutions on two- and three-dimensional regular geometries, and has been applied to simulate flow and permeabilities of two real-world rock samples.

Voltage-Thickness-Gray imaging physical model in X-ray energy auto-modulation.

Energy auto-modulation is an important tool in X-ray imaging, as it can improve the quality and longevity of an x-ray imaging system. Because of the complex nature of imaged objects, X-ray energy auto-modulation may be difficult. If there is a physical model about imaging mechanism, one can forecast the best imaging parameters using a pre-scan that can be fed into this model.

Multi-energy image sequence fusion based on variable energy X-ray imaging.

For complicated structural components characterized by wide X-ray attenuation ranges, the conventional fixed-energy imaging mode cannot obtain all structural information using a single tube voltage. This limitation results in information shortage, because the effective thickness of components along the orientation of the X-ray penetration exceeds the limit of the dynamic range of the X-ray imaging system. To solve this problem, multi-energy image sequence fusion technology has been advanced.

[Research on X-ray multispectrum imaging based on variable energy].

In conventional X-ray imaging based on fixed energy, the ray image of the complicated structural component often appears overexposed or underexposed. This is because of the variations in the effective thickness of the component along the orientation of the X-ray penetration that exceed the limit of the dynamic range of the X-ray imaging system. Complete structural information cannot be obtained, and this lack will impact the quality of the X-ray image.

[Virtual design of X-ray multi-spectrum CT imaging based on variable voltage].

In the conventional X-ray imaging, the ray image of the complicated structural component (shape, structure complex and multi-materials) easily exhibits the overexposed and underexposed phenomenon. This is because of the bigger variations in the effective thickness in the orientation of X-ray penetration and the limit of the dynamic range of X-ray imaging system. The complete structure information can't be obtained, and it will impact the quality of X-ray CT image.

Variable weighted ordered subset image reconstruction algorithm.

We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART.