Gradient waveform design for tensor-valued encoding in diffusion MRI

TL;DR

This paper reviews the design of gradient waveforms for tensor-valued diffusion MRI, emphasizing how waveform optimization influences sensitivity, accuracy, and artifact management in advanced diffusion imaging applications.

Contribution

It provides a comprehensive overview of the objectives, considerations, and tradeoffs in designing gradient waveforms for tensor-valued diffusion MRI, addressing hardware, physiological, and artifact-related constraints.

Findings

Identifies key design objectives for gradient waveforms.

Discusses tradeoffs between sensitivity and artifacts.

Highlights importance of waveform optimization for accurate data.

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.