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