Visual reports

Demo: anatomical reports

Demo: functional reports

How reports work

In order to ease the screening process of individual images, MRIQC generates individual reports with mosaic views of a number of cutting planes and supporting information (for example, segmentation contours). The most straightforward use-case is the visualization of those images flagged as low-quality by the classifier.

After the extraction of IQMs in all the images of our sample, a group report is generated. The group report shows a scatter plot for each of the IQMs, so it is particularly easy to identify the cases that are outliers for each metric. The plots are interactive, such that clicking on any particular sample opens the corresponding individual report of that case. Examples of group and individual reports for the ABIDE dataset are available online at mriqc.org.

mriqc.reports package

Submodules

Encapsulates report generation functions

mriqc.reports.group.gen_html(csv_file, mod, csv_failed=None, out_file=None)[source]

Encapsulates report generation functions

mriqc.reports.individual.individual_html(in_iqms, in_plots=None, exclude_index=0, wf_details=None)[source]

Helpers in report generation

mriqc.reports.utils.anat_flags(iqms_dict)[source]

Anatomical flags

mriqc.reports.utils.iqms2html(indict, table_id)[source]

Converts a dictionary into an HTML table

mriqc.reports.utils.read_report_snippet(in_file)[source]

Add a snippet into the report

mriqc.reports.utils.unfold_columns(indict, prefix=None)[source]

Converts an input dict with flattened keys to an array of columns