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

Paquette, Michael; Tax, Chantal M.W.; Eichner, Cornelius; Anwander, Alfred

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

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Paquette, Michael
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Eichner, Cornelius
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander, Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Paquette, M., Tax, C. M., Eichner, C., & Anwander, A. (2020). Impact of gradient non-linearities on B-tensor diffusion encoding. In Proceedings of the 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

Cite as: https://hdl.handle.net/21.11116/0000-0006-D4AF-6

Abstract

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 mT/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.