  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.
