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Abstract #4412

Impact of gradient non-linearities on B-tensor diffusion encoding

Michael Paquette1, Chantal M.W. Tax2, Cornelius Eichner1, and Alfred Anwander1
1Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2CUBRIC, School of Physics, Cardiff University, Cardiff, United Kingdom

We investigate the effect of gradient non-linearities (GNL) on free gradient waveform used for B-tensor diffusion encoding. We show the magnitude of the GNL-bias for strong gradients of $$$300 m\text{T}/\text{m}$$$. We derive a closed-form formula of the voxelwise B-tensor under GNL, independent of the choice of gradient waveform used to encode the B-tensor.

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