In cone-beam computed tomography （CBCT）, there are often cases where the size of the specimen is larger than the field of view （FOV） （referred to as over FOV-sized （OFS））. To acquire the complete projection da...In cone-beam computed tomography （CBCT）, there are often cases where the size of the specimen is larger than the field of view （FOV） （referred to as over FOV-sized （OFS））. To acquire the complete projection data for OFS objects, some scan modes have been developed for long objects and short but over-wide objects. However, these modes still cannot meet the requirements for both longitudinally long and transversely wide objects. In this paper, we propose a multiple helical scan mode and a corresponding reconstruction algorithm for both longitudinally long and transversely wide objects. The simulation results show that our model can deal with the problem and that the results are acceptable, while the OFS object is twice as long compared with the FOV in the same latitude.展开更多
As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in...As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's( DM) preference information.This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two,three,and four-objective knapsack problems. The results demonstrate the algorithm ' s ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition,the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point.展开更多
基金Project supported by the National Basic Research Program of China (Grant No. 2011CB707701)the National High Technology Research and Development Program of China (Grant No. 2009AA012200)the National Nature Science Foundation of China(Grant No. 30970722)
文摘In cone-beam computed tomography （CBCT）, there are often cases where the size of the specimen is larger than the field of view （FOV） （referred to as over FOV-sized （OFS））. To acquire the complete projection data for OFS objects, some scan modes have been developed for long objects and short but over-wide objects. However, these modes still cannot meet the requirements for both longitudinally long and transversely wide objects. In this paper, we propose a multiple helical scan mode and a corresponding reconstruction algorithm for both longitudinally long and transversely wide objects. The simulation results show that our model can deal with the problem and that the results are acceptable, while the OFS object is twice as long compared with the FOV in the same latitude.
文摘目的:主观评价和客观评估不同成像参数下CBCT的图像质量,分析图像质量的主观评价和客观评价间的关系。方法:分别采用6台不同品牌CBCT扫描仪〔3D Accuitomo(Morita)、i-CAT(Kavo)、5G(NewTom)、Smart3D(北京朗视)、DCT Pro(Vatech)、VGi(NewTom)〕,在各个品牌的典型曝光条件下(电压和电流强度不同)扫描空间分辨率模体和高仿真头模,7位医师对拍摄的CBCT图像进行主观评价打分,比较不同CBCT扫描仪的空间分辨率和对常见口腔解剖结构的可见性。客观评价指标采用各仪器所获的图像空间分辩率(LP/mm)。结果:7位医师的组内一致性和组间一致性均无显著性差异。主观评价New Tom 5G为2分,i-CAT为5分,其余4个品牌匀为4分,客观评价i-CAT的LP/mm为1.8,Smart3D为2.0,其余4个品牌为1.0~1.7。在相同管电流条件下,不同管电压的图像主观质量有显著性差异。在相同管电压条件下,不同管电流的图像主观质量有显著性差异。结论:图像质量的主客观评价具有一定的一致性,不同品牌之间的客观评价差异可能与电压、电流强度不同有关。
文摘As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's( DM) preference information.This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two,three,and four-objective knapsack problems. The results demonstrate the algorithm ' s ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition,the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point.
