QSIprep输出结果解读

共分为四类:

  1. QA报告:一个html文件
  2. 预处理图像数据
  3. 后续分析可能用到的数据
  4. 量化的QA

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QA报告

位置在<output dir>/qsiprep/sub-<subject_label>.html

预处理数据

位置在<output dir>/qsiprep/sub-<subject_label>/

T1w相关的数据和fmriprep的差不多,在anat文件夹下:

  1. brain mask
  2. CSF/GM/WM的分割结果
  3. FAST的Tissue class map
  4. ANTs的N4 Bias校正后的T1w文件
  5. MNI和T1w的变形场文件
  • *_brainmask.nii.gz Brain mask derived using ANTs’ antsBrainExtraction.sh.
  • *_class-CSF_probtissue.nii.gz
  • *_class-GM_probtissue.nii.gz
  • *_class-WM_probtissue.nii.gz tissue-probability maps.
  • *_dtissue.nii.gz Tissue class map derived using FAST.
  • *_preproc.nii.gz Bias field corrected T1w file, using ANTS’ N4BiasFieldCorrection
  • *_space-MNI152NLin2009cAsym_brainmask.nii.gz Same as above, but in MNI space.
    *_space-MNI152NLin2009cAsym_class-CSF_probtissue.nii.gz
    *_space-MNI152NLin2009cAsym_class-GM_probtissue.nii.gz
    *_space-MNI152NLin2009cAsym_class-WM_probtissue.nii.gz 
    *_space-MNI152NLin2009cAsym_dtissue.nii.gz 
    *_space-MNI152NLin2009cAsym_preproc.nii.gz 
  • *_space-MNI152NLin2009cAsym_target-T1w_warp.h5 MNI to T1变形场文件?Composite (warp and affine) transform to map from MNI to T1 space
    *_target-MNI152NLin2009cAsym_warp.h5 T1 to MNI变形场文件?

DWI的数据在dwi下,实际可能藏在ses-1下:

  1. confunds文件
  2. brain mask
  3. b0 template
  4. fsl的.bval .bvec和MRTrix的.b文件
  5. 所有b0的重采样DWI
  6. eddy_cnr
  7. 根据空间扭曲调整的梯度表
  • *_confounds.tsv one column per calculated confound and one row per timepoint/volume
  • *dwiref.nii.gz The b0 template
  • *desc-brain_mask.nii.gz The generous brain mask that should be reduced probably
  • *desc-preproc_dwi.nii.gz Resampled DWI series including all b0 images.
  • *desc-preproc_dwi.bval*desc-preproc_dwi.bvec FSL-style bvals and bvecs files. These will be incorrectly interpreted by MRTrix, but will work with DSI Studio and Dipy. Use the .b file for MRTrix.
  • desc-preproc_dwi.b The gradient table to import data into MRTrix. This and the _dwi.nii.gz can be converted directly to a .mif file using the mrconvert -grad _dwi.b command.
  • *bvecs.nii.gz Each voxel contains a gradient table that has been adjusted for local rotations introduced by spatial warping.
  • *eddy_cnr.nii.gz Each voxel contains a contrast-to-noise model defined as the variance of the signal model divided by the variance of the error of the signal model.

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