Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

Although sparse-view computed tomography (CT) has significantly reduced radiation dose, it also introduces severe artifacts which degrade the image quality. In recent years, deep learning-based methods for inverse problems have made remarkable progress and have become increasingly popular in CT reconstruction. However, most of these methods suffer several limitations: dependence on high-quality training data, weak interpretability, etc. In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations. DRP introduces a neural network as an implicit prior into the iterative method, thereby realizing cross-domain gradient feedback. During the reconstruction process, the neural network is progressively optimized in multiple stages to narrow the solution space in radon domain for the under-constrained imaging protocol, and the convergence of the proposed method has been discussed in this work. Compared with the popular pre-trained method, the proposed framework requires no dataset and exhibits superior interpretability and generalization ability. The experimental results demonstrate that the proposed method can generate detailed images while effectively suppressing image artifacts.Meanwhile, DRP achieves comparable or better performance than the supervised methods.Comment: 11 pages, 12 figures, Journal pape

3D Nondestructive Visualization and Evaluation of TRISO Particles Distribution in HTGR Fuel Pebbles Using Cone-Beam Computed Tomography

A nonuniform distribution of tristructural isotropic (TRISO) particles within a high-temperature gas-cooled reactor (HTGR) pebble may lead to excessive thermal gradients and nonuniform thermal expansion during operation. If the particles are closely clustered, local hotspots may form, leading to excessive stresses on particle layers and an increased probability of particle failure. Although X-ray digital radiography (DR) is currently used to evaluate the TRISO distributions in pebbles, X-ray DR projection images are two-dimensional in nature, which would potentially miss some details for 3D evaluation. This paper proposes a method of 3D visualization and evaluation of the TRISO distribution in HTGR pebbles using cone-beam computed tomography (CBCT): first, a pebble is scanned on our high-resolution CBCT, and 2D cross-sectional images are reconstructed; secondly, all cross-sectional images are restructured to form the 3D model of the pebble; then, volume rendering is applied to segment and display the TRISO particles in 3D for visualization and distribution evaluation. For method validation, several pebbles were scanned and the 3D distributions of the TRISO particles within the pebbles were produced. Experiment results show that the proposed method provides more 3D than DR, which will facilitate pebble fabrication research and production quality control

Stress analysis of aspherical TRISO-coated particle with X-ray computed tomography

The failure probability of TRISO-coated particle is directly dependent on the asphericity and the layer thickness. Local asphericity of the SiC layer will contribute to the concentrated stress region, increasing failure probability of the particle. In this paper, we utilized micro X-ray computed tomography (CT) to obtain the 3D volume rendering of the SiC layer with the real geometric shape before irradiation. The stress distribution of the aspherical reconstructed SiC was then simulated with finite element method (FEM) based on the pressure vessel model. The maximum and mean principle stress were compared between the analytical methods and FEM simulation. The maximum deviation between the SiC principle stress with the real shape and ideal shape is 64.56 % for the inner gas pressure 17 MPa. The preliminary failure probability using the aforementioned stress was calculated and compared with the analytical solution. There is obvious increment with the maximum principle stress. The local stress concentration of the acquired aspherical model is 1.86. The stress discrepancy between the FEM simulation and the theoretical calculation increases with the inner gas pressure. The SiC asphericity measured with X-ray CT will contribute to a higher failure probability under irradiation

Three-Dimensional Measurement of TRISO Coated Particle Using Micro Computed Tomography

The fuel safety and performance of high-temperature gas-cooled reactor (HTGR) are dependent on the integrity and geometric parameter of Tri-structural Isotropic (TRISO) coated particle. Micro X-ray computed tomography (CT) was used for nondestructive testing and three-dimensional measurement of the particle components which are composed of kernel, buffer layer, inner pyrolytic carbon layer (IPyC), silicon carbide (SiC) layer, and outer pyrolytic carbon (OPyC) layer. The thickness distribution and volume of kernel and coating layers are obtained by constructing 3D volume rendering of TRISO particle. Mean thickness of each layer is calculated for comparison with design value. A comparison between two-dimensional and three-dimensional measurement results is also made. It is found that the thickness distribution of all layers approximately obeys Gaussian distribution. Deviation of the thickness of kernel and coating layers between 3D measurement result and design value is 7.88%, -25.63%, -45.50%, 13.87%, and 14.73%, respectively. The deviation will affect the failure probability of TRISO particle. Obvious difference of the OPyC mean thickness between 3D measurement and 2D measurement is found, which proves that the proposed 3D measurement provides comprehensive information of the particle. However, 2D and 3D measured thickness of the kernel and IPyC layer tend to be similar

Dose-Area Product Determination and Beam Monitor Calibration for the Fixed Beam of the Shanghai Advanced Proton Therapy Facility

Research conducted to-date, makes use of the IBA-Lynx scintillating screen and radiochromic film to analyze the proton field uniformity for dose-area product (DAP) determination. In this paper, the machine log file based reconstruction is proposed to calculate the field uniformity to simplify the measurement. In order to calculate the field uniformity, the dose distribution is reconstructed based on the machine log file with matRad (an open source software for analytical dose calculation in MATLAB). After acquisition of the dose distribution, the field flatness and symmetry are calculated automatically for different proton energies. A comprehensive comparison of DAP determined with Bragg peak chamber (BPC) and Markus chamber (MC) is presented. The actual delivered dose is reconstructed with the log file to analyze the lateral dose distribution of the scanned field. DAP of different energies are calculated ranging from 70.6 MeV to 235 MeV. The percentage difference is calculated, illustrating the DAP discrepancy between the MC and BPC to the mean value. The percentage difference ranges from −0.19% to 1.26%. The variation between DAP measured with the BPC and MC peaks at −2.5%. The log file based reconstruction to calculate field uniformity can be an alternative for DAP determination. The direct method using a large-area Bragg peak chamber is investigated. The two methods to determine DAP and calibrate beam monitor illustrate consistent results

3D Proton Bragg Peak Visualization and Spot Shape Measurement with Polymer Gel Dosimeters

Proton pencil beam scanning is a dynamic beam delivery technique with excellent conformability to the tumor volume. The accuracy of spot size and scanning positions will have a significant effect on the delivered dose distribution. We employed polymer gel dosimeters to measure the spot size and the scanning positions for the Shanghai Advanced Proton Therapy facility (SAPT). Polymer gel dosimeters (MAGAT-f and PAGAT) were utilized to measure the full width at half maximum (FWHM) of the beam spot at various depths on the basis of their MRI readouts. The correlation between the spot FWHM and standard deviation (σ) was analyzed at different depths. The measured Bragg peak range was compared with the Monte Carlo (MC) simulation. Three-dimensional volume rendering of the Bragg peak was reconstructed for the 3D visualization to measure the spot size three-dimensionally. The R2 dose–response curve was investigated with polymer gel dosimeters. The deviations of the Bragg peak ranging between measurement and simulation were 0.13% and −0.53% for MAGAT-f and PAGAT, respectively. Our results ascertain the feasibility of a polymer gel dosimeter to measure the spot size and positions of a proton pencil beam

OMICS Applications for Medicinal Plants in Gastrointestinal Cancers: Current Advancements and Future Perspectives

Gastrointestinal cancers refer to a group of deadly malignancies of the gastrointestinal tract and organs of the digestive system. Over the past decades, considerable amounts of medicinal plants have exhibited potent anticancer effects on different types of gastrointestinal cancers. OMICS, systems biology approaches covering genomics, transcriptomics, proteomics and metabolomics, are broadly applied to comprehensively reflect the molecular profiles in mechanistic studies of medicinal plants. Single- and multi-OMICS approaches facilitate the unravelling of signalling interaction networks and key molecular targets of medicinal plants with anti-gastrointestinal cancer potential. Hence, this review summarizes the applications of various OMICS and advanced bioinformatics approaches in examining therapeutic targets, signalling pathways, and the tumour microenvironment in response to anticancer medicinal plants. Advances and prospects in this field are also discussed

Deep Learning-Based Denoising in Brain Tumor CHO PET: Comparison with Traditional Approaches

18F-choline (CHO) PET image remains noisy despite minimum physiological activity in the normal brain, and this study developed a deep learning-based denoising algorithm for brain tumor CHO PET. Thirty-nine presurgical CHO PET/CT data were retrospectively collected for patients with pathological confirmed primary diffuse glioma. Two conventional denoising methods, namely, block-matching and 3D filtering (BM3D) and non-local means (NLM), and two deep learning-based approaches, namely, Noise2Noise (N2N) and Noise2Void (N2V), were established for imaging denoising, and the methods were developed without paired data. All algorithms improved the image quality to a certain extent, with the N2N demonstrating the best contrast-to-noise ratio (CNR) (4.05 ± 3.45), CNR improvement ratio (13.60% ± 2.05%) and the lowest entropy (1.68 ± 0.17), compared with other approaches. Little changes were identified in traditional tumor PET features including maximum standard uptake value (SUVmax), SUVmean and total lesion activity (TLA), while the tumor-to-normal (T/N ratio) increased thanks to smaller noise. These results suggested that the N2N algorithm can acquire sufficient denoising performance while preserving the original features of tumors, and may be generalized for abundant brain tumor PET images