.png){kind=icon}
返回
Quantifying the brain's sheet structure with normalized convolution
.png){kind=icon}
OA
AI
DOI：10.1016/j.media.2017.03.007.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as pages of a book has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of path neighborhoods reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Diffusion MRI
Brain sheet structure
Normalized convolution
Lie bracket
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.8
论文数:
3.8K
被引数:
2.4W
机构
引用论文
Probabilistic Fiber Tracking Using the Residual Bootstrap with Constrained Spherical Deconvolution使用残差自举和约束球面反卷积的概率光纤跟踪.png)
HUMAN BRAIN MAPPINGOAAI
HUMAN BRAIN MAPPINGIF3.3
OAAI分享收藏分享收藏Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm通过隐马尔可夫随机场模型和期望最大化算法分割脑部MR图像
OAAI
OAAI分享收藏A Ranking of Diffusion MRI Compartment Models with In Vivo Human Brain Data具有体内人脑数据的扩散MRI隔室模型的排名OAAI
OAAI分享收藏Min-max vs. max-min flow control algorithms for optimal computer network capacity assignment优化计算机网络容量分配的最小-最大vs.最大-最小流量控制算法OAAI
OAAI分享收藏
查看更多内容