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Medical-image Analysis and Statistical Interpretation Lab

Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

Posted by on Monday, April 30, 2007 in Diffusion Tensor Imaging.

B. A. Landman, J. A. Farrell, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. “Effects of Diffusion Weighting Schemes on the Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T”, NeuroImage. 36(4): 1123-1138. July 2007. PMID17532649

Full text: https://www.ncbi.nlm.nih.gov/pubmed/17532649

Abstract

Diffusion tensor imaging (DTI) is used to study tissue composition and architecture in vivo. To increase the signal to noise ratio (SNR) of DTI contrasts, studies typically use more than the minimum of 6 diffusion weighting (DW) directions or acquire repeated observations of the same set of DW directions. Simulation-based studies have sought to optimize DTI acquisitions and suggest that increasing the directional resolution of a DTI dataset (i.e., the number of distinct directions) is preferable to repeating observations, in an equal scan time comparison. However, it is not always clear how to translate these recommendations into practice when considering physiological noise and scanner stability. Furthermore, the effect of different DW schemes on in vivo DTI findings is not fully understood. This study characterizes how the makeup of a DW scheme, in terms of the number of directions, impacts the precision and accuracy of in vivo fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) findings. Orientation dependence of DTI reliability is demonstrated in vivo and a principled theoretical framework is provided to support and interpret findings with simulation results. As long as sampling orientations are well balanced, differences in DTI contrasts due to different DW schemes are shown to be small relative to intra-session variability. These differences are accentuated at low SNR, while minimized at high SNR. This result suggests that typical clinical studies, which use similar protocols but different well-balanced DW schemes, are readily comparable within the experimental precision.

Minimum potential energy (PE) partitions of the Jones30 DW scheme. The optimal PE partitions (left) are evenly distributed as indicated by the shading which is proportional to the area of the spherical Voronoi tessellations of the DW directions. The realized directions are distinct (right) from the specified ones (left) because the gradient tables are corrected for subject motion. The right panel shows 30 clusters, where each cluster represents a specified DW direction and consists of three sub-clusters which represent realized DW directions from each session. The separation of the sub-clusters shows the inter-session effects, while the distribution of the symbols shows the intra-session effects. Large ovals indicate the subset of the Jones30 that was used to construct the PE6 partition.
Minimum potential energy (PE) partitions of the Jones30 DW scheme. The optimal PE partitions (left) are evenly distributed as indicated by the shading which is proportional to the area of the spherical Voronoi tessellations of the DW directions. The realized directions are distinct (right) from the specified ones (left) because the gradient tables are corrected for subject motion. The right panel shows 30 clusters, where each cluster represents a specified DW direction and consists of three sub-clusters which represent realized DW directions from each session. The separation of the sub-clusters shows the inter-session effects, while the distribution of the symbols shows the intra-session effects. Large ovals indicate the subset of the Jones30 that was used to construct the PE6 partition.

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