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.

每次信号采集沿多个空间方向的扩散编码可以用 b-张量来描述。张量值扩散编码的好处是它将“b-张量的形状”解锁为新的编码维度。通过调节 b-张量形状，我们可以控制对微观扩散各向异性的敏感性，可用作对比机制；传统扩散编码无法访问的特征。由于基于张量值扩散编码的成像方法正在寻找越来越多的应用，我们被提示强调为任何给定应用设计最佳梯度波形的挑战。在这篇综述中，我们首先建立了为张量值扩散 MRI 创建场梯度波形的基本设计目标。我们还调查了与硬件和生理学所施加的限制、b-张量无法捕获的潜在混杂效应以及与扩散编码波形相关的伪影相关的其他设计考虑因素。在整个过程中，我们讨论了预期的折衷和权衡，旨在更全面地了解梯度波形设计及其对准确测量和数据解释的影响。