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Graessle, T.

Publications and source records attributed to Graessle, T..

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High Angular Resolution Susceptibility Imaging and Estimation of Fiber Orientation Distribution Functions in Primate Brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution. HighlightsO_LIIntroduction of High Angular Resolution Susceptibility Imaging (HARSI) for advancing Quantitative Susceptibility Mapping (QSM). C_LIO_LIHARSI-derived fiber orientation distributions in fixed chimpanzee brain. C_LIO_LIHARSI-based visualization of complex fiber configurations. C_LIO_LIComparisons between HARSI and High Angular Resolution Diffusion Imaging. C_LIO_LIPotential for high-resolution post-mortem imaging of fiber architecture. C_LI

neuroscience↗

B1+-correction of MT saturation maps optimized for 7T postmortem MRI of the brain

PurposeMagnetization transfer saturation (MTsat) is a useful marker to probe tissue macromolecular content and myelination in the brain. The increased [Formula] -inhomogeneity at [≥] 7T and significantly larger saturation pulse flip angles which are often used for postmortem studies exceed the limits where previous MTsat [Formula] correction methods are applicable. Here, we develop a calibration-based correction model and procedure, and validate and evaluate it in postmortem 7T data of whole chimpanzee brains. TheoryThe [Formula] dependence of MTsat was investigated by varying the off-resonance saturation pulse flip angle. For the range of saturation pulse flip angles applied in typical experiments on postmortem tissue, the dependence was close to linear. A linear model with a single calibration constant C is proposed to correct bias in MTsat by mapping it to the reference value of the saturation pulse flip angle. MethodsC was estimated voxel-wise in five postmortem chimpanzee brains. "Individual-based global parameters" were obtained by calculating the mean C within individual specimen brains and "group-based global parameters" by calculating the means of the individual-based global parameters across the five brains. ResultsThe linear calibration model described the data well, though C was not entirely independent of the underlying tissue and [Formula]. Individual-based and group-based global correction parameters (C = 1.2) led to visible, quantifiable reductions of [Formula]-biases in high resolution MTsat maps. ConclusionThe presented model and calibration approach effectively corrects for [Formula] in-homogeneities in postmortem 7T data.

neuroscience↗