Purpose To demonstrate that a recently proposed continuous-time-random walk (CTRW) diffusion model can be adapted to human brain imaging, and to illustrate that the CTRW model can improve the diagnostic accuracy of differentiating low-grade and high-grade pediatric brain tumors. Theory and Methods Fifty-four children with histopathologically confirmed brain tumors (24 low-grade, 30 high-grade) underwent MRI scans at 3T. Diffusion-weighted images, acquired with 12 b-values (0 to 4000 s/mm2), were fit to a simplified CTRW diffusion model to extract three parameters: anomalous diffusion coefficient, Dm, and temporal and spatial heterogeneity parameters, α and β, respectively. The two latter parameters have been linked to intra-voxel tissue heterogeneity. With histopathology results as a reference, a k-means clustering algorithm and a receiver operating characteristic (ROC) analysis were employed to determine the sensitivity, specificity, and diagnostic accuracy of using the CTRW parameters for differentiating the tumor grades. Results The CTRW diffusion model was successfully applied to data obtained from the living human brain using a 3T MRI scanner with clinically achievable b-values up to 4000 s/mm2. Significant differences between the two tumor groups were observed in all three individual CTRW parameters with p-values<0.001. The k-means analysis showed that the combination of the three CTRW parameters produced higher diagnostic accuracy (85% vs. 75%) and specificity (83% vs. 54%) than the apparent diffusion coefficient (ADC) from a mono-exponential model in differentiating the grades of pediatric brain tumors. In addition, the ROC analysis revealed that any combination of the CTRW parameters gave a larger area under the curve (0.90–0.96) than using ADC alone (0.80). Conclusion With its sensitivity to intra-voxel heterogeneity, the simplified CTRW diffusion model has provided a set of new parameters that are useful for non-invasive brain tumor grading for pediatric patients. The proposed technique is particularly valuable in situations where surgical biopsy is not feasible due to the tumor location.

).q RSNA, 2015 Purpose:To demonstrate that a new set of parameters (D, b, and m) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low-and high-grade pediatric brain tumors. Materials and Methods:The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter b (which correlates with tissue heterogeneity), and a microstructural quantity m were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, b, and m values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the lowand high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis. Results:None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P  .24), but all showed a significant difference (P , . Conclusion:The FROC parameters can be used to differentiate between low-and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors.q RSNA, 2015

Background Dilated brain perivascular spaces (PVSs) are found to be associated with many conditions, including aging, dementia, and Alzheimer's disease (AD). Conventionally, PVS assessment is mainly based on subjective observations of the number, size and shape of PVSs in MR images collected at clinical field strengths (≤ 3T). This study tests the feasibility of imaging and quantifying brain PVS with an ultrahigh 7T whole-body MRI scanner. New Method 3D high resolution T2-weighted brain images from healthy subjects (n=3) and AD patients (n=5) were acquired on a 7T whole-body MRI scanner. To automatically segment the small hyperintensive fluid-filling PVS structures, we also developed a quantitative program based on algorithms for spatial gradient, component connectivity, edge-detection, k-means clustering, etc, producing quantitative results of white matter PVS volume densities. Results The 3D maps of automatically segmented PVS show an apparent increase in PVS density in AD patients compared to age-matched healthy controls due to the PVS dilation (8.0 ± 2.1 v/v% in AD vs. 4.9 ± 1.3% in controls, p<0.05). Comparison with Existing Method We demonstrated that 7T provides sufficient SNR and resolution for quantitatively measuring PVSs in deep white matter that is challenging with clinical MRI systems (≤ 3T). Compared to the conventional visual counting and rating for the PVS assessment, the quantitation method we developed is automatic and objective. Conclusions Quantitative PVS MRI at 7T may serve as a non-invasive and endogenous imaging biomarker for diseases with PVS dilation.

Background and Purpose To demonstrate that gradual and continuous WM change and the associated cognitive decline in type 2 diabetes mellitus (T2DM) patients can be captured by DTI parameters and the DTI parameters can be used to complement neuropsychological test scores in identifying T2DM patients with and without mild cognitive impairment (MCI). Materials and Methods Forty-two T2DM patients, divided into a group with MCI (DM-MCI; n = 20) and a group with normal cognition (DM-NC; n = 22) based on cognitive assessments, were enrolled together with age-, gender-, and education-matched healthy controls (HC; n = 26). DTI was performed at 3 Tesla, followed by an analysis using tract-based spatial statistics (TBSS) to investigate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1), and radial diffusivity (λ23) among the groups. A receiver operating characteristic (ROC) analysis was used to assess the performance of using DTI parameters for separating the two T2DM groups. Results The whole-brain TBSS analysis revealed that 7.3% and 24.9% of the WM exhibited decreased FA and increased MD (p < 0.05), respectively, between the DM-MCI and the DM-NC groups, while considerably larger WM regions showed FA (36.6%) and MD (58.8%) changes between the DM-MCI and the HC groups. These changes were caused primarily by an elevated radial diffusivity observed in the DM-MCI patients. Radial diffusivity also exhibited subtle but statistically significant changes between the DM-NC and the HC group. Analyses on individual fiber tracts showed pronounced FA reduction and MD elevation in regions related to cognitive functions. The ROC analysis on the right cingulum (hippocampus) showed that FA produced a larger area under the curve (AUC = 0.832) than MD (0.753) for separating MCI from normal cognition among T2DM patients. When FA was combined with MD, the AUC was further improved to 0.857. Conclusion This study demonstrates that the DTI parameters can show a significant difference between the T2DM patients with and without MCI, suggesting their potential use as an imaging marker for detecting cognitive decline in T2DM patients. More importantly, this study also suggests that the DTI parameters may capture gradual and continuous WM changes that can be associated with early stages of cognitive decline in T2DM patients before they can be diagnosed clinically using the conventional neuropsychological tests.