Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context

Abnormalities of resting state functional connectivity are related to sustained attention deficits in MS.

ObjectivesResting state (RS) functional MRI recently identified default network abnormalities related to cognitive impairment in MS. fMRI can also be used to map functional connectivity (FC) while the brain is at rest and not adhered to a specific task. Given the importance of the anterior cingulate cortex (ACC) for higher executive functioning in MS, we here used the ACC as seed-point to test for differences and similarities in RS-FC related to sustained attention between MS patients and controls.DesignBlock-design rest phases of 3 Tesla fMRI data were analyzed to assess RS-FC in 31 patients (10 clinically isolated syndromes, 16 relapsing-remitting, 5 secondary progressive MS) and 31 age- and gender matched healthy controls (HC). Participants underwent extensive cognitive testing.ObservationsIn both groups, signal changes in several brain areas demonstrated significant correlation with RS-activity in the ACC. These comprised the posterior cingulate cortex (PCC), insular cortices, the right caudate, right middle temporal gyrus, angular gyri, the right hippocampus, and the cerebellum. Compared to HC, patients showed increased FC between the ACC and the left angular gyrus, left PCC, and right postcentral gyrus. Better cognitive performance in the patients was associated with increased FC to the cerebellum, middle temporal gyrus, occipital pole, and the angular gyrus.ConclusionWe provide evidence for adaptive changes in RS-FC in MS patients compared to HC in a sustained attention network. These results extend and partly mirror findings of task-related fMRI, suggesting FC may increase our understanding of cognitive dysfunction in MS

and .show the standard deviation of the noise for two of six noise tensor elements and that serve as input for Eq. [13].

<p>The spatially varying regularization parameter was updated at each iteration step. This is shown for two regions of interest (ROI 1, ROI 2) for the regularization tensor elements and .</p

Evaluated tractography parameters Mean Length (ML), Track Count (TC), Volume (V), and Voxel Count (VC) for the noise-free phantom, the phantom with overlaid noise from ten measurements (mean ± standard deviation) and the noisy phantom with regularization (mean ± standard deviation).

<p>Evaluated tractography parameters Mean Length (ML), Track Count (TC), Volume (V), and Voxel Count (VC) for the noise-free phantom, the phantom with overlaid noise from ten measurements (mean ± standard deviation) and the noisy phantom with regularization (mean ± standard deviation).</p

FA map from data obtained using one measurement (first out of three averages) (a), FA map from the denoised set of DWI data () (b) showing only marginal differences compared with (a), FA map from data obtained using three averages (c), FA map from data obtained using one measurement (first out of three averages) with regularization (first row) (d).

<p>The second row shows magnified views from the region marked in the first image.</p

Tractography from the software phantom without noise (a), from the phantom with overlaid noise (b) and from the noisy, regularized phantom (c) demonstrating the improvement of fiber homogeneity due to regularization.

<p>Tractography from the software phantom without noise (a), from the phantom with overlaid noise (b) and from the noisy, regularized phantom (c) demonstrating the improvement of fiber homogeneity due to regularization.</p

Diffusion tensor elements for one average (first row) and for one average regularized (second row).

<p>Rows three and four show a magnified view from the region marked in the first image.</p

Schematic overview of the adaptive spatially varying regularization algorithm.

<p> denotes the diffusion-weighted images, IC the independent components decomposed by ICA, are the denoised diffusion-weighted images with the corresponding noise images is the diffusion tensor with the corresponding noise tensor , evaluated using the Stejskal-Tanner equation (ST). The noise variance tensor was estimated by averaging the variance in a 5×5 pixel moving window (SD). denotes the regularization tensor to estimate the sought regularized diffusion tensor .</p

Comparison of FA maps from conventional ss-EPI data (a) with a resolution of 2.5×2.5×2.5 mm³ with high resolution rs-EPI data (b) with a resolution of 1×1×2.5 mm³.

<p>Small structures such as the fornix (marked by the arrow) or branches in peripheral regions can hardly be seen in conventional DTI scans with limited resolution but can be clearly identified in high resolution rs-EPI.</p