期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 348, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2020.109007
关键词
Diffusion magnetic resonance imaging; Tensor-valued diffusion encoding; Gradient waveform design
资金
- Swedish Research Council, Sweden [2016-03443]
- NIH, United States [R01MH074794, P41EB015902]
- Swedish Research Council [2016-03443] Funding Source: Swedish Research Council
- Vinnova [2016-03443] Funding Source: Vinnova
Tensor-valued diffusion encoding allows for controlling sensitivity to microscopic diffusion anisotropy through modulating the shape of the b-tensor, which serves as a new encoding dimension. When designing gradient waveforms, considerations include hardware and physiological limitations, potential confounding effects not captured by the b-tensor, and artifacts related to diffusion encoding.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据