Abstract #3041

Novel Diffusion-Kurtosis-Informed Template to Reduce Partial Volume Effects in the Atlas-Based Analysis

Farida Grinberg^1,2, Xiang Gao¹, Ezequiel Farrher¹, and N. Jon Shah^1,2

¹Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany, ²Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

Between-group comparisons of diffusion tensor and diffusion kurtosis imaging metrics are important for elucidation of differences between pathology and healthy state. However, thus far, the methodology of such comparisons has not been well established. Frequently used methods, such as region-of-interest analysis or atlas-based analysis are subject to errors due to partial volume effects, whereas the track-based spatial statistics reduces consideration to a small amount of voxels along the skeleton, thus diminishing useful information. In this work we represent a simple, robust diffusion-kurtosis-informed template effectively reducing partial volume effects in the atlas-based analysis using less restrictive approach.

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