Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI.

PURPOSE: To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity. METHODS: Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters. RESULTS: MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001). CONCLUSION: MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.

A novel method for evaluating brain function and microstructural changes in Parkinson's disease.

In this study, microstructural brain damage in Parkinson's disease patients was examined using diffusion tensor imaging and tract-based spatial statistics. The analyses revealed the presence of neuronal damage in the substantia nigra and putamen in the Parkinson's disease patients. Moreover, disease symptoms worsened with increasing damage to the substantia nigra, confirming that the substantia nigra and basal ganglia are the main structures affected in Parkinson's disease. We also found that microstructural damage to the putamen, caudate nucleus and frontal lobe positively correlated with depression. Based on the tract-based spatial statistics, various white matter tracts appeared to have microstructural damage, and this correlated with cognitive disorder and depression. Taken together, our results suggest that diffusion tensor imaging and tract-based spatial statistics can be used to effectively study brain function and microstructural changes in patients with Parkinson's disease. Our novel findings should contribute to our understanding of the histopathological basis of cognitive dysfunction and depression in Parkinson's disease.