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3,385 results on '"Wang, Lixia"'

106. Six‐dimensional quantitative DCE MR Multitasking of the entire abdomen: Method and application to pancreatic ductal adenocarcinoma

Author

Wang, Nan, Gaddam, Srinivas, Wang, Lixia, Xie, Yibin, Fan, Zhaoyang, Yang, Wensha, Tuli, Richard, Lo, Simon, Hendifar, Andrew, Pandol, Stephen, Christodoulou, Anthony G, and Li, Debiao

Subjects
Engineering, Biomedical Engineering, Cancer, Digestive Diseases, Clinical Research, Pancreatic Cancer, Rare Diseases, Biomedical Imaging, Abdomen, Adenocarcinoma, Contrast Media, Humans, Magnetic Resonance Imaging, Pancreatic Neoplasms, DCE MRI, MR Multitasking, pancreatic ductal adenocarcinoma, quantitative imaging, respiratory motion resolved imaging, Nuclear Medicine & Medical Imaging, Biomedical engineering
Abstract

PurposeTo develop a quantitative DCE MRI technique enabling entire-abdomen coverage, free-breathing acquisition, 1-second temporal resolution, and T1 -based quantification of contrast agent concentration and kinetic modeling for the characterization of pancreatic ductal adenocarcinoma (PDAC).MethodsSegmented FLASH readouts following saturation-recovery preparation with randomized 3D Cartesian undersampling was used for incoherent data acquisition. MR Multitasking was used to reconstruct 6-dimensional images with 3 spatial dimensions, 1 T1 recovery dimension for dynamic T1 quantification, 1 respiratory dimension to resolve respiratory motion, and 1 DCE time dimension to capture the contrast kinetics. Sixteen healthy subjects and 14 patients with pathologically confirmed PDAC were recruited for the in vivo studies, and kinetic parameters vp , Ktrans , ve , and Kep were evaluated for each subject. Intersession repeatability of Multitasking DCE was assessed in 8 repeat healthy subjects. One-way unbalanced analysis of variance was performed between control and patient groups.ResultsIn vivo studies demonstrated that vp , Ktrans , and Kep of PDAC were significantly lower compared with nontumoral regions in the patient group (P = .002, .003, .004, respectively) and normal pancreas in the control group (P = .011,

Published
2020

107. Multiparametric Mapping Magnetic Resonance Imaging of Pancreatic Disease.

Author

Wang, Lixia, Gaddam, Srinivas, Wang, Nan, Xie, Yibin, Deng, Zixin, Zhou, Zhengwei, Fan, Zhaoyang, Jiang, Tao, Christodoulou, Anthony G, Han, Fei, Lo, Simon K, Wachsman, Ashley M, Hendifar, Andrew Eugene, Pandol, Stephen J, and Li, Debiao

Subjects
ADC, T1, T2, chronic pancreatitis, magnetic resonance imaging, pancreatic ductal adenocarcinoma, parametric mapping, Pancreatic Cancer, Digestive Diseases, Biomedical Imaging, Clinical Research, Rare Diseases, Cancer, Physiology, Medical Physiology, Psychology
Abstract

BackgroundCurrent magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated.PurposeTo evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis.Materials and methodsPancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively.ResultsT1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001).ConclusionMultiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

Published
2020

108. Prevalences and characteristics of Trichuris spp. infection in sheep in pastoral areas of the Tianshan, Xinjiang, China

Author

Wang Lixia, Zhang Guowu, Fu Yuhang, Ning Chengcheng, Li Zhiyuan, Wang Huisheng, Zhang Jinsheng, Shang Yunxia, Sun Yaoqiang, Huang Xiaoxing, Cai Xuepeng, Xia Xianzhu, Meng Qingling, and Qiao Jun

Subjects
sheep, trichuris spp., prevalence and characteristics, phylogenetic analysis, Veterinary medicine, SF600-1100
Abstract

Nematodes of the Trichuris genus are commonly reported parasites that can cause trichuriasis in many animals, which leads to inflammation, intestinal bleeding and reductions of productivity in livestock. Knowledge of the prevalence of Trichuris infestation in the Tianshan ovine population and of the nematode species parasitising the population is not exhaustive, and this study aimed to expand the knowledge.

Published
2022
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133. The association between lymphocyte-to-monocyte ratio and all-cause mortality in obese hypertensive patients with diabetes and without diabetes: results from the cohort study of NHANES 2001–2018

Author

Wang, Lixia, primary, Gao, Jie, additional, Liu, Bing, additional, Fu, Youliang, additional, Yao, Zhihui, additional, Guo, Shanshan, additional, Song, Ziwei, additional, Zhang, Zhaoyuan, additional, He, Jiaojiao, additional, Wang, Congxia, additional, Ma, Weidong, additional, and Wu, Feng, additional

Published
2024
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