This study tested the hypothesis that diffusion tensor imaging (DTI) can detect alteration in microscopic integrity of white matter (WM) and basal ganglia (BG) regions known to be involved in Parkinson's disease (PD) pathology. It was also hypothesized that there is an association between the DTI abnormality and PD severity and subtype. DTI at 4 Tesla was obtained in 12 PD and 20 control subjects. The DTI measures of fractional anisotropy (FA) and mean diffusivity (MD) were evaluated using both region of interest (ROI) and voxel-based methods. Movement deficits in PD subjects were assessed using Motor Subscale (Part III) of the Unified Parkinson's Disease Rating Scale (UPDRS). Subtype determination of movement deficits was derived based upon results of subjects’ UPDRS ratings. Reduced FA (p<0.05, corrected) was found in PD subjects in a number of regions, including the precentral gyrus, substantia nigra, putamen, posterior striatum, frontal WM, and in regions related to the supplementary motor areas. Reduced FA in the substantia nigra correlated (p<0.05, corrected) with increased UPDRS motor scores. Significant spatial correlations between FA alterations in putamen and other PD-affected regions were also found in the context of PD subtypes index analysis. Our data suggest that microstructural alterations detected with DTI might serve as a potential biomarker for PD.

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio (SNR) represent significant hurdles for many in vivo applications. This paper presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for Gaussian noise modeling and theoretical characterizations of the resolution and SNR of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling, q-space modeling, etc.). The joint reconstruction method can provide significant improvements in SNR relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this SNR-enhancing joint reconstruction approach for a range of different diffusion imaging experiments.

Most MRI studies of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases. Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4 Tesla MRI. GM loss and GM hypoperfusion were measured using high-resolution T1 and arterial spin labeling MRI (ASL-MRI). WM degradation was measured with diffusion tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD, structural and functional damages were similar. Joint assessments of multimodal MRI have potential value to provide new imaging markers for improved differential diagnoses between FTD and AD.