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

Reishofer, G., Koschutnig, K., Langkammer, C., Porter, D., Jehna, M., Enzinger, C., Keeling, S. and Ebner, F. (2013) Time-optimized high-resolution readout-segmented diffusion tensor imaging. PLoS ONE, 8(9), e74156. (doi: 10.1371/journal.pone.0074156) (PMID:24019951) (PMCID:PMC3760803)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porter, Professor David
• Authors: Reishofer, G., Koschutnig, K., Langkammer, C., Porter, D., Jehna, M., Enzinger, C., Keeling, S., and Ebner, F.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: PLoS ONE
• Publisher: Public Library of Science
• ISSN: 1932-6203
• ISSN (Online): 1932-6203
• Copyright Holders: Copyright © 2013 Reishofer et al.
• First Published: First published in PLoS ONE 8(9):e74156
• Publisher Policy: Reproduced under a Creative Commons License