Abstract
To obtain a better insight in tissue microstructures using diffusion MRI, a high resolution and dense sampling of q-space is required. In clinical settings, however, this can often not be achieved due to limited acquisition time. Reduced field-of-view (FOV) approaches counteract this limitation but may pose a challenge for the post-processing steps such as motion and artifact correction. We present an evaluation of the potential problems that arise with reduced FOV data during the standard post-processing. The acquisition with reduced FOV is extracted from a full FOV dataset. We select three different registration tools to perform the standard data post-processing pipeline. We first evaluate the spatial error and then measure its impact on the tensor reconstruction as well as on the derived fractional anisotropy (FA). With reduced FOV images, the multi-scale registration methods showed high sensitivity to parameter selection and produced up to 30 % outliers. With an optimized parameter set, all registration methods yielded spatial errors of 1 mm (±0.572). The spatial error resulted in a mean error of 0.03 (±0.013) in the estimated FA values, and was thus of the same magnitude as group differences as they are typically reported in DTI studies. Regions with large FA differences were located especially in the corpus callosum. The evaluation indicates that diffusion-weighted MR acquisitions with reduced FOV require careful selection of registration parameters and also cautious interpretation when quantifying derived indices.
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Acknowledgements
Dr. Maier-Hein (né Fritzsche) and Jan Hering received support from the German Research Foundation (DFG), grant# ME 833/15-1 and WO 1218/3-1 respectively.
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Hering, J., Wolf, I., Meinzer, HP., Stieltjes, B., Maier-Hein, K.H. (2014). A Quantitative Evaluation of Errors Induced by Reduced Field-of-View in Diffusion Tensor Imaging. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_4
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