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.