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通过自由波形编码在健康大脑、水、油和液晶中获取的线性、平面和球形张量值扩散磁共振成像数据。

Linear, planar and spherical tensor-valued diffusion MRI data by free waveform encoding in healthy brain, water, oil and liquid crystals.

作者信息

Szczepankiewicz Filip, Hoge Scott, Westin Carl-Fredrik

机构信息

Radiology, Brigham and Women's Hospital, Boston, MA, USA.

Harvard Medical School, Boston, MA, USA.

出版信息

Data Brief. 2019 Jul 2;25:104208. doi: 10.1016/j.dib.2019.104208. eCollection 2019 Aug.

Abstract

Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.

摘要

最近,已经提出了几种基于张量值或多维扩散磁共振成像的微观结构成像的生物物理模型和信号表示方法。获取必要的数据需要非常规脉冲序列,因此更广泛的扩散磁共振成像社区无法获得这些数据。为了促进基于张量值扩散编码的分析技术的探索和发展,我们分享了一个在健康人脑中获取的综合数据集。该数据包括在多个b值和扩散编码方向上使用线性、平面和球形扩散张量编码的扩散加权图像。我们还提供了在几个模型中获取的数据,这些数据可能有助于验证。数据托管在GitHub上:https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/382a/6626882/251de82fdd3d/gr1.jpg

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