Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.
Background:The design of intensity modulated radiation therapy (IMRT) plans is difficult and time-consuming. The retrieval of similar IMRT plans from the IMRT plan dataset can effectively improve the quality and efficiency of IMRT plans and automate the design of IMRT planning. However, the large IMRT plans datasets will bring inefficient retrieval result. Materials and Methods: An intensity-modulated radiation therapy (IMRT) plan clustering method based on k-means algorithm and geometrical features is proposed to improve the retrieval efficiency from the IMRT plan dataset. The proposed method could benefit future automatic IMRT planning based on prior knowledge. In this study, a collection dataset including 100 cases of nasopharyngeal carcinoma IMRT plans was employed in the clustering experiment. The geometrical features of each cluster center were used to qualitatively predict the dosimetric characteristics of organs at risk (OARs) and compared with practical results. Results: Experimental results demonstrate that the tested dataset can be well clustered using the proposed method. The predicted dosimetric characteristics of OARs for each cluster agree well with their practical results, and the difficulty of IMRT planning for each cluster can be derived. Conclusion: The proposed IMRT plan clustering method can bring great benefit to the new cases of IMRT planning.
The large increase of distributed generation (DG) of photovoltaic (PV) systems in low voltage (LV) grids results in increasing voltage magnitudes and line loadings. Due to a lack of network observability in present LV grids, distribution system operators (DSOs) cannot detect and respond to any limit violations. Even with data collected by the expected rollout of smart meters, full network observability will not be achieved. Thus, the network state will not be completely known. LV-State Estimation (SE) in combination with pseudo-value generation can provide a way to determine the required network states with respect to voltage magnitudes and line loadings. This paper presents a linear three-phase LV-SE approach computed in phase sequences. The performance of the presented SE approach was investigated on a realistic grid model for different cases with and without pseudo-values. The performance is promising and the approach can be used to provide DSOs with input data for control actions to avoid limit violations and thus will contribute to the further integration of DG.
Cancer prevention and treatment are currently the focus of most research. Dose verification is an important step for reducing the improper dose deposition during radiotherapy. To mend the traditional gel dosimeters for 3D dose verification, a novel rare-earth nanoparticle-based composite gel was prepared, which has good radioluminescence property and reusability. It is a promising phantom material for the new 3D gel dosimeter. TEM, DLS, FT-IR, TGA, and spectrofluorometer were used to determine the chemical structure, micromorphology, and optical performance. Compared to the traditional gel dosimeters, the composite gel has a good linear relationship between the light intensity excited by X-ray and the tube current. Furthermore, it may measure the dose distribution immediately in situ, which reduces errors and saves time. This work provides a new idea for the research of 3D gel dosimeters and promotes the safe and effective use of radiotherapy.
Most of the jade on the market now comes from Myanmar, Guatemala, and a few from Russia. The gemological properties of jadeite from different producing areas are consistent. However, in the middle-end jade market, under the same quality, the prices of Guatemalan jade and Russian jade are generally lower than those of Myanmar jade, so some illegal merchants will use Guatemalan jade to impersonate Myanmar jade. Due to the continuous improvement of jade counterfeiting technology, traditional identification methods can no longer meet the demand. In order to protect the rights and interests of consumers need to establish a rapid and effective jade origin traceability method. In this paper, through the (LA-ICP-MS) trace element data set and the method based on weighted extreme learning machine, AdaBoost and incremental learning fusion, the jadeite discrimination model of different producing areas is established to realize the intelligent discrimination of jadeite producing areas. The recognition accuracy of integrated learning algorithm is more than 80 %. Compared with the basic extreme learning machine and weighted extreme learning machine, it can be found that the classification accuracy of integrated learning algorithm is higher and more stable.
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