Quality Check Stream
The above image depicts a very simplified version of quality checks. The quality checks to be added will be the "bad" slice detection and "bad" scan detection. The output of the "bad" slice detection will be a PS in each run directory, a .mat file in each run directory that stores all the output to create the figures in the PS, and one text that lists all "bad" detected slices. The user can check the "bad" slices in an image viewer and decide what to do with them. Do you think any user would view the PS?
Metrics used for bad slice detection:
- z-score of mean slice values - default threshold 4
- MSE between slice scans - supplementary for the user
The output of the "bad" scan detection will be a .txt (or .csv?) file in the run directory if and only if a "bad" scan is detected in a run. The text file will contain regressors for each "bad" scan in the run. The user decides whether to use the regressors at the first level stage.
Metrics used for bad scan detection:
- differnce between z-scored scan means - default threshold TBD
- maybe just used art toolbox for this which also does motion detection. It may be cumbersome to use because it requires an SPM.mat and is easiest to use with SPM motion parameter text files. Our preprocessing text files can be used, but we would have to edit some of the code.
Why
The above image depicts a decision making tree when using the semi-automated quality stream.
The output of the "bad scan/slice" detection will be a PS, a .mat, and a compiled text file. The PS will contain figure. The .mat file will contain all the variables used to generate the PS. The text file will only be created if there were corrupted scans detected. It will contain the regressor for the GLM.
The unprocessed data check stream is used to detect any scans that are beyond repair. It is also used to detect single slices corrupted by artifacts.
The post-processed data check is intended to check for scans that continue to be abnormal after realignment and normalization. This is to confirm that the abnormality is not due to movement. The variability in these scans is accounted by including a regressor in the first-level GLM. The data check can also detect single slices corrupted by image artifacts not due to movement. The slices can be corrected by methods of interpolation.
Figures plotted in PS:
- SNR as defined by Tor Wager histogram (may be useful later)
- z-score of mean slice values
- MSE between slice scans
- Whole scan means vs whole scan STD
- MSE between whole volumes
