BACKGROUND AND PURPOSE: Patients with type 2 diabetes mellitus have considerably higher risk of developing cognitive impairment and dementia. WM changes in these patients have been reported. Our aim was to demonstrate that gradual and continuous WM change and the associated cognitive decline in patients with type 2 diabetes mellitus can be captured by DTI parameters, which can be used to complement neuropsychological test scores in identifying patients with type 2 diabetes mellitus with and without mild cognitive impairment. MATERIALS AND METHODS: Forty-two patients with type 2 diabetes mellitus, divided into a group with mild cognitive impairment (n = 20) and a group with normal cognition (n = 22), were enrolled with age-, sex-, and education-matched healthy controls (n = 26). 3T DTI followed by Tract-Based Spatial Statistics analysis was used to investigate the differences in fractional anisotropy, mean diffusivity, axial diffusivity (λ₁), and radial diffusivity (λ₂₃) among the groups. A receiver operating characteristic analysis assessed the performance of DTI parameters for separating the 2 groups with type 2 diabetes mellitus. RESULTS: The whole-brain Tract-Based Spatial Statistics analysis revealed that 7.3% and 24.9% of the WM exhibited decreased fractional anisotropy and increased mean diffusivity (P < .05), respectively, between the diabetes mellitus with mild cognitive impairment and the diabetes mellitus with normal cognition groups, while considerably larger WM regions showed fractional anisotropy (36.6%) and mean diffusivity (58.8%) changes between the diabetes mellitus with mild cognitive impairment and the healthy control groups. These changes were caused primarily by an elevated radial diffusivity observed in the patients with diabetes mellitus with mild cognitive impairment. Radial diffusivity also exhibited subtle but statistically significant changes between the diabetes mellitus with normal cognition and the healthy control groups. Analyses on individual fiber tracts showed pronounced fractional anisotropy reduction and mean diffusivity elevation in regions related to cognitive functions. The receiver operating characteristic analysis on the right cingulum (hippocampus) showed that fractional anisotropy produced a larger area under the curve (0.832) than mean diffusivity (0.753) for separating mild cognitive impairment from normal cognition among patients with type 2 diabetes mellitus. When fractional anisotropy was combined with mean diffusivity, the area under the curve was further improved to 0.857. CONCLUSIONS: DTI parameters can show a substantial difference between patients with type 2 diabetes mellitus with and without mild cognitive impairment, suggesting their potential use as an imaging marker for detecting cognitive decline in patients with type 2 diabetes mellitus. More important, DTI parameters may capture gradual and continuous WM changes that can be associated with early stages of cognitive decline in patients with type 2 diabetes mellitus before they can be diagnosed clinically by using conventional neuropsychological tests.

BACKGROUND AND PURPOSE: Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D). MATERIALS AND METHODS: Fifty-four adult patients (18–70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0–4000 s/mm²) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, β, and μ) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation. RESULTS: Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ≤ .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ≥ .706). The receiver operating characteristic analysis showed that β (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or μ (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%. CONCLUSIONS: The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, β exhibited a better performance than the other 2 parameters, including ADC (or D).

BACKGROUND AND PURPOSE: The limitations inherent in the current methods of diagnosing mild cognitive impairment have constrained the use of early therapeutic interventions to delay the progression of mild cognitive impairment to dementia. This study evaluated whether quantifying enlarged perivascular spaces observed on MR imaging can help differentiate those with mild cognitive impairment from cognitively healthy controls and, thus, have an application in the diagnosis of mild cognitive impairment. MATERIALS AND METHODS: We automated the identification of enlarged perivascular spaces in brain MR Images using a custom quantitative program designed with Matlab. We then quantified the densities of enlarged perivascular spaces for patients with mild cognitive impairment (n = 14) and age-matched cognitively healthy controls (n = 15) and compared them to determine whether the density of enlarged perivascular spaces can serve as an imaging surrogate for mild cognitive impairment diagnosis. RESULTS: Quantified as a percentage of volume fraction (v/v%), densities of enlarged perivascular spaces were calculated to be 2.82 ± 0.40 v/v% for controls and 4.17 ± 0.57 v/v% for the mild cognitive impairment group in the subcortical brain (P < .001), and 2.74 ± 0.57 v/v% for the controls and 3.90 ± 0.62 v/v% for the mild cognitive impairment cohort in the basal ganglia (P < .001). Maximum intensity projections exhibited a visually conspicuous difference in the distributions of enlarged perivascular spaces for a patient with mild cognitive impairment and a control patient. By means of receiver operating characteristic curve analysis, we determined the sensitivity and specificity of using enlarged perivascular spaces as a differentiating biomarker between mild cognitive impairment and controls to be 92.86% and 93.33%, respectively. CONCLUSIONS: The density of enlarged perivascular spaces was found to be significantly higher in those with mild cognitive impairment compared with age-matched healthy control subjects. The density of enlarged perivascular spaces, therefore, may be a useful imaging biomarker for the diagnosis of mild cognitive impairment.