Gradient waveform design for tensor-valued encoding in diffusion MRI

J Neurosci Methods. 2021 Jan 15;348:109007. doi: 10.1016/j.jneumeth.2020.109007. Epub 2020 Nov 23.

Abstract

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.

Keywords: Diffusion magnetic resonance imaging; Gradient waveform design; Tensor-valued diffusion encoding.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Anisotropy
  • Artifacts
  • Diffusion
  • Diffusion Magnetic Resonance Imaging*
  • Diffusion Tensor Imaging*