NIH R01MH074794 (Diffusion MRI in Schizophrenia)

Knutsson H, Westin C-F. From expected propagator distribution to optimal q-space sample metric. Med Image Comput Comput Assist Interv. 2014;17 (Pt 3) :217-24.Abstract
We present a novel approach to determine a local q-space metric that is optimal from an information theoreticperspective with respect to the expected signal statistics. It should be noted that the approach does not attempt to optimize the quality of a pre-defined mathematical representation, the estimator. In contrast, our suggestion aims at obtaining the maximum amount of information without enforcing a particular feature representation. Results for three significantly different average propagator distributions are presented. The results show that the optimal q-space metric has a strong dependence on the assumed distribution in the targeted tissue. In many practical cases educated guesses can be made regarding the average propagator distribution present. In such cases the presented analysis can produce a metric that is optimal with respect to this distribution. The metric will be different at different q-space locations and is defined by the amount of additional information that is obtained when adding a second sample at a given offset from a first sample. The intention is to use the obtained metric as a guide for the generation of specific efficient q-space sample distributions for the targeted tissue.
O'Donnell LJ, Pasternak O. Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls. Schizophr Res. 2015;161 (1) :133-41.Abstract
One key pitfall in diffusion magnetic resonance imaging (dMRI) clinical neuroimaging research is the challenge of understanding and interpreting the results of a complex analysis pipeline. The sophisticated algorithms employed by the analysis software, combined with the relatively non-specific nature of many diffusion measurements, lead to challenges in interpretation of the results. This paper is aimed at an intended audience of clinical researchers who are learning about dMRI or trying to interpret dMRI results, and who may be wondering "Does dMRI tell us anything about the white matter?" We present a critical review of dMRI methods and measures used in clinical neuroimaging research, focusing on the most commonly used analysis methods and the most commonly reported measures. We describe important pitfalls in every section, and provide extensive references for the reader interested in more detail.
Szczepankiewicz F, Lasič S, van Westen D, Sundgren PC, Englund E, Westin C-F, Ståhlberg F, Lätt J, Topgaard D, Nilsson M. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage. 2015;104 :241-52.Abstract
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
Gordon  Kindlmann

Dr. Gordon Kindlmann

Assistant Professor, Computer Science and the Computation Institute, University of Chicago

I research image analysis and data visualization to improve the computation of imaging-based science.

Science pairs measurement tools that...

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Mailing address: Gordon Kindlmann UChicago Computer Science 1100 East 58th St Chicago, IL 60637

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