scholarly journals Using fiber tractography and diffusion kurtosis imaging to evaluate neuroimaging changes in patients with cerebrotendinous xanthomatosis after stopping chenodeoxycholic acid treatment for three years

2021 ◽  
Author(s):  
Jun-Jun Lee ◽  
Chiung-Chih Chang ◽  
Wen-Neng Chang
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhijun Geng ◽  
Yunfei Zhang ◽  
Shaohan Yin ◽  
Shanshan Lian ◽  
Haoqiang He ◽  
...  

Purpose. To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods. A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results. For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05 ). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and Kapp (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001 ). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion. The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.


2017 ◽  
Vol 28 (8) ◽  
pp. 3141-3150 ◽  
Author(s):  
Tristan Barrett ◽  
Mary McLean ◽  
Andrew N. Priest ◽  
Edward M. Lawrence ◽  
Andrew J. Patterson ◽  
...  

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