We performed contrast-enhanced T2 fluid-attenuated inversion recovery(T2-FLAIR)and dynamic contrast enhanced MRI to illustrate the imaging characteristics of one case of hemangioblastoma.T2-FLAIR showed a large cyst l...We performed contrast-enhanced T2 fluid-attenuated inversion recovery(T2-FLAIR)and dynamic contrast enhanced MRI to illustrate the imaging characteristics of one case of hemangioblastoma.T2-FLAIR showed a large cyst located in the right cerebellum with mural nodule.The intensely enhancing cyst wall was observed on enhanced T2-FLAIR images acquired from 5.6 to 23 minutes after contrast administration,and quantitative dynamic contrast enhanced-MRI demonstrated that both the cyst wall and mural nodule presented high Ktrans,Kep and Ve values compared with the contralateral normal cerebellar tissues.The cyst showed gradual enhancement and reached the highest signal intensity at 67 minutes after contrast administration on enhanced T2-FLAIR images.In conclusion,early enhancement of cyst wall on T2-FLAIR might be the characteristic imaging findings for cystic hemangioblastoma,which may assist in the diagnosis of hemangioblastoma preoperatively.展开更多
Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenario...Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.展开更多
目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(...目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(T_(2)WI、ADC和DCE序列),并根据最新的BIRADS提取特征,使用ROI之间的类内系数(ICC)来评估读者间协议。结果研究人群分为:luminal A 89例(26%),luminal B HER2阳性39例(11.5%),luminal B HER2阴性168例(48.5%),三阴性(TNBC)41例(12%),HER2富集7例(2%)。Luminal内A肿瘤与特殊的组织学类型、最小的肿瘤大小和持续的动力学曲线相关(P均<0.05)。Luminal B HER2阴性肿瘤与最低ADC值相关(0.77×10^(-3)mm^(2)/s^(2)),其预测BC分子亚型的准确性为0.583。TNBC与不对称和中度/显著BPE,圆形/椭圆形肿块,边缘受限和边缘增强相关(P均<0.05)。HER2富集的BC与最大肿瘤大小相关(平均37.28mm,p值=0.02)。结论BC分子亚型与T_(2)WI、ADC和DCE MRI特征相关,ADC有助于预测luminal B HER2阴性病例。展开更多
文摘We performed contrast-enhanced T2 fluid-attenuated inversion recovery(T2-FLAIR)and dynamic contrast enhanced MRI to illustrate the imaging characteristics of one case of hemangioblastoma.T2-FLAIR showed a large cyst located in the right cerebellum with mural nodule.The intensely enhancing cyst wall was observed on enhanced T2-FLAIR images acquired from 5.6 to 23 minutes after contrast administration,and quantitative dynamic contrast enhanced-MRI demonstrated that both the cyst wall and mural nodule presented high Ktrans,Kep and Ve values compared with the contralateral normal cerebellar tissues.The cyst showed gradual enhancement and reached the highest signal intensity at 67 minutes after contrast administration on enhanced T2-FLAIR images.In conclusion,early enhancement of cyst wall on T2-FLAIR might be the characteristic imaging findings for cystic hemangioblastoma,which may assist in the diagnosis of hemangioblastoma preoperatively.
基金the Science and Technology Project of State Grid Corporation of China,Grant Number 5108-202304065A-1-1-ZN.
文摘Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.
文摘目的探讨乳腺肿瘤的多参数MRI特征(T_(2)-WI、ADC值和DCE)以及乳腺密度和背景实质增强(BPE)特征在不同乳腺癌(BC)分子亚型中的差异,以期为临床诊断提供重要参考。方法本研究为回顾性研究,纳入344例患者。所有患者均接受了多参数乳房MRI(T_(2)WI、ADC和DCE序列),并根据最新的BIRADS提取特征,使用ROI之间的类内系数(ICC)来评估读者间协议。结果研究人群分为:luminal A 89例(26%),luminal B HER2阳性39例(11.5%),luminal B HER2阴性168例(48.5%),三阴性(TNBC)41例(12%),HER2富集7例(2%)。Luminal内A肿瘤与特殊的组织学类型、最小的肿瘤大小和持续的动力学曲线相关(P均<0.05)。Luminal B HER2阴性肿瘤与最低ADC值相关(0.77×10^(-3)mm^(2)/s^(2)),其预测BC分子亚型的准确性为0.583。TNBC与不对称和中度/显著BPE,圆形/椭圆形肿块,边缘受限和边缘增强相关(P均<0.05)。HER2富集的BC与最大肿瘤大小相关(平均37.28mm,p值=0.02)。结论BC分子亚型与T_(2)WI、ADC和DCE MRI特征相关,ADC有助于预测luminal B HER2阴性病例。
