ROI QuantificationΒΆ

Region of Interest Quantification.

roiquant uses atlas provided in design file to compute the regional derivative values especially for reho and alff outputs. The users can also use custom atlases to generate region values from the output. Beware that your directories need to be mounted in the container (see xcpEngine containers (Extra Info))

The customed atlas and the input image such as rehoZ must have the same dimension and orientation. This can be done with ${XCPEDIR}/utils/quantifyAtlas.:

docker run --rm -it --entrypoint /xcpEngine/utils/quantifyAtlas   \
    pennbbl/xcpengine:latest \
 -v  inputfile  \  # this is input image 3D
 -s  mean \ # the statistics, the defualt is the mean of each roi in atlas
 -a  atlas \ # the atlas in 3D
 -n  atlas_name \ # atlas name : option
 -p  id1,id2 \ # subject idenfiers  : option
 -r  region_names \ # name of regions in atlas : option
 -o  output_path.txt

The output will consist of header with ids and region names or numbers with the corresponding values atlas rois as show below:

id1,id2, reho_mean_region1,reho_mean_region2,...
ses-01,sub-1, 0.3456,0.7894,...

Similarly, users can extract time series from BOLD image with the customized atlas. This is similar to the output in fcon module. It is called:

docker run --rm -it --entrypoint /xcpEngine/utils/roi2ts.R   \
   pennbbl/xcpengine:latest \
   -i   $input    \  # the 4D bold image
   -r   $atlas       \ # the atlas in the same orientation as bold
   -l   $atlas_label \ # atlas region label  but not compulsory
   >>    $output.txt   # output file