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Medicine (Baltimore) ; 95(35): e4726, 2016 Aug.
Article in English | MEDLINE | ID: mdl-27583912

ABSTRACT

The aim of the study was to evaluate the performance of parameters obtained from diffusion-weighted imaging (DWI) with multiple b values in the detection of chronic brain damage in patients with type 2 diabetes.We enrolled 30 patients with or without abnormalities on brain magnetic resonance imaging (lacunar infarction, leukoaraiosis, and/or brain atrophy) and 15 nondiabetic controls; obtained DWI parameters that included apparent diffusion coefficient (ADC), fast ADC (ADCfast), slow ADC (ADCslow), fraction of fast ADC (f), distributed diffusion coefficient (DDC), and stretched exponential (α); and performed receiver operating characteristic (ROC) analysis to evaluate the performance of parameters for the detection of chronic brain damage.The parameters ADC, ADCslow, f, and DDC were increased, whereas parameters ADCfast and α were decreased in type 2 diabetes patients compared with controls without diabetes. The centrum semiovale showed the most significant change in the evaluated parameters, and the changes in parameters ADCslow, f, and DDC were greater than the changes in other parameters. There was no significance between parameters of the biexponential model (ADCfast, ADCslow, f) and parameters of the stretched model (DDC, α), but parameters of both these models were superior to the parameter of monoexponential model (ADC). Moreover, ROC analysis showed that ADCslow of the centrum semiovale supplied by the anterior cerebral artery had the highest performance for detection of chronic brain damage (area under the ROC curve of 0.987, 93.3% sensitivity, and 100% specificity).Our study shows that DWI with multiple b values can quantitatively access chronic brain damage and may be used for detection and monitoring in type 2 diabetes patients.


Subject(s)
Brain Damage, Chronic/diagnostic imaging , Diabetes Mellitus, Type 2/complications , Diffusion Magnetic Resonance Imaging , Aged , Diabetes Mellitus, Type 2/diagnostic imaging , Female , Humans , Image Interpretation, Computer-Assisted , Male , Middle Aged , ROC Curve
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