
yeatmanlab/AFQ: Automated Fiber Quantification - GitHub
AFQ was designed to generate Tract Profiles of tissue properties for major fiber tracts in healthy and diseased brains. Online documentation can be found at: . …
BRAIN:AFQ(纤维束自动量化)技术预测颞叶癫痫术后疗效
纤维束自动定量法(afq)是一种dti纤维束成像技术,它可以全面分析沿白质束长度方向的组织特征。 该方法考虑了局部病变内组织的特点,提供了一种比全束追踪更敏感的测量白质束精细解剖改变的方法。
AFQ.models.dti — AFQ 1.3.2 documentation - GitHub Pages
AFQ.models.dti. fit_dti (data_files, bval_files, bvec_files, mask = None, out_dir = None, file_prefix = None, b0_threshold = 50) [source] # Fit the DTI model using default settings, save files with derived maps. Parameters data_files str or list. Files containing DWI data. If this is a str, that’s the full path to a single file.
GitHub - YongLiuLab/AI4AD_AFQ: AI Challenge for AD based on AFQ …
Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter degeneration in AD through diffusion properties. It is confirmed that WM integrity measures are effective in classifying AD using machine learning.
Automated Fiber Quantification in Python (pyAFQ) — AFQ 2.0 …
It implements a complete and automated data processing pipeline for tractometry, from raw DTI data to white matter tract identification, as well as quantification of tissue properties along the length of the major long-range brain white matter connections.
Automated Fiber Quantification | University of Utah - Bookdown
Automated Fiber Quantification version 1.2 (https://github.com/yeatmanlab/AFQ) identifies twenty major fiber tracts that include the corticospinal tract, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, anterior thalamic radiation, cingulum cingulate gyrus and hippocampal bundles, superior ...
自动化纤维束定量AFQ概述1 - CSDN博客
博客介绍基于Matlab软件的扩散加权成像预处理,将T1加权图像对齐到AC - PC空间,扩散加权图像用FSL的FDT预处理。 还阐述了自动纤维量化(AFQ)软件的处理步骤,其在MATLAB中实现,可产生“束轮廓”测量组织特性。 2.4.3. 扩散加权. 成像 预处理:将每个参与者的T1 加权图 像对齐到AC-PC空间中,为大脑可视化和牵引术提供共同的方向。 这种对齐涉及手动定义T1图像中的几个解剖标志:AC(前连合),PC(后连合)和矢状面中。 扩散加权图像在FMRIB的软件 …
AFQ.models.dti — AFQ 1.3.2 documentation - yeatmanlab.github.io
def fit_dti (data_files, bval_files, bvec_files, mask = None, out_dir = None, file_prefix = None, b0_threshold = 50): """ Fit the DTI model using default settings, save files with derived maps. Parameters ---------- data_files : str or list Files containing DWI data.
How to use Free water DTI — AFQ 1.3.2 documentation - GitHub …
How to use Free water DTI# The free-water DTI model [1, 2]_ fits a two compartment model to dMRI data with more than one non-zero shell. One compartment is a spherical compartment with the diffusivity of water, which accounts for free water in the tissue.
慧脑云|自动化纤维定量分析(AFQ)介绍 - 知乎 - 知乎专栏
2020年5月7日 · 本期由慧脑云科学家团队的许博岩博士介绍“自动化纤维 定量分析 ”。 这是一种能够精细分析纤维束的脑影像 数据分析方法,已经在慧脑云平台上实现自动化处理。 希望本期视频能够对大家的科研工作有所帮助。 谢谢! 慧脑云开启每周2次的视频发布,欢迎脑科学领域的科研、临床研究人员关注! 本期由慧脑云科学家团队的许博岩博士介绍“自动化纤维定量分析”。 这是一种能够精细分析纤维束的脑影像数据分析方法,已经在慧脑云平台上…