morpho_report

Morphologist processing report

Description

This process runs at the end of the Morphologist pipeline. It generates both a .json report and a PDF document where the main steps results are displayed, and the global brain measurements are summarized. It may make use of normative stats to asses if the processing is within the norm, and if not, some processing failures may be detected from it.

Parameters

t1mri: Raw T1 MRI ( input )

left_grey_white: Left Grey White Mask ( optional, input )

right_grey_white: Right Grey White Mask ( optional, input )

left_gm_mesh: Hemisphere Mesh ( optional, input )

right_gm_mesh: Hemisphere Mesh ( optional, input )

left_wm_mesh: Hemisphere White Mesh ( optional, input )

right_wm_mesh: Hemisphere White Mesh ( optional, input )

left_labelled_graph: Labelled Cortical folds graph ( optional, input )

right_labelled_graph: Labelled Cortical folds graph ( optional, input )

talairach_transform: Transform Raw T1 MRI to Talairach-AC/PC-Anatomist ( optional, input )

brain_volumes_file: Brain volumetry measurements ( optional, input )

normative_brain_stats: Normative brain volumes stats ( optional, input )

Optional normative data. These normative stats may be built from a database (or several) using one of the processes: normative_brainvolumes_stats, or normative_brainvolumes_stats_mult.
Some normative data may be stratified by sex and age, which makes the comparison much more meaningfil. In this situation, the user has to provide sex and age covariables for the current subject. They may be input "by hand" using the covariables parameter, or using a .csv or .tsv table, which may be the BIDS participants.tsv table, if the columns sex and age are present there.

report: Morphologist report ( output )

report_json: Morphologist JSON report ( optional, output )

inter_subject_qc_table: QC table ( optional, output )

subject: String ( input )

bids: String ( input )

covariables_file: Participants file ( optional, input )

This optional file is meant to provide sex and age information for the subject, in order to use matched stratified normative data. So it is useful only when using such stratified normative data. This file is an alternative to the covariables parameter, and may contain several subjects, as long as the subject ID is found in the first table column. sex and age columns are needed in this table. The table may be the BIDS participants.tsv file, if the expected columns are present there.

covariables: String ( optional, input )

When using stratified normative data to compare global brain morphometric measurements, the subject covariables, sex and age are needed. This parameter allows to provide them on a single subject basis (see the covariables_file parameter for an alternative). The format here is a JSON dictionary: for instance:

{"sex": "M", "age": 35}

covariables_specs: String ( optional, input )

This parameter may be used optionally to replace covariables_file and covariables in the context of stratified normative data to compare global brain morphometric measurements. It allows to use covariables (normally age and sex) which may be scattered in multiple files, and add data filters and values transformations. The string here is a JSON dictionary secifying, for each covariable, where and how to find it.
Example (inspired by the ABCD cohort phenotypes files):

{"age": {"filename": "/data/abcd/phenotype/ab_g_dyn.tsv", "var_in_file": "ab_g_dyn__visit_age", "filter": {"session_id": "ses-00A"}}, "sex": {"filename": "/data/abcd/phenotype/ab_g_stc.tsv", "var_in_file": "ab_g_stc__cohort_sex", "interpret": {"1": "M", "2": "F"}}}

This means that age and sex variables are found in different files (ab_g_dyn.tsv and ab_g_stc.tsv respectrively) under column names ab_g_dyn__visit_age and ab_g_stc__cohort_sex, that the subject age table has to be filtered using only session_id column value being ses-00A (the baseline of a longitudinal study where the same subject may have multiple acquisitioins at different ages), and the sex value is not ("M", "F") as expected, but numerical values (1, 2) which need to be translated.
The interpret field may be a dict as in this example to simply translate values, or a string naming a translation function. To date, only the value months is understood to translate age in months into years.

Technical information

Toolbox : Morphologist

User level : 0

Identifier : morpho_report

File name : brainvisa/toolboxes/morphologist/processes/morphometry/morpho_report.py

Supported file formats :

t1mri :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

left_grey_white :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

right_grey_white :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

left_gm_mesh :

GIFTI file, GIFTI file, MESH mesh, MNI OBJ mesh, PLY mesh, TRI mesh

right_gm_mesh :

GIFTI file, GIFTI file, MESH mesh, MNI OBJ mesh, PLY mesh, TRI mesh

left_wm_mesh :

GIFTI file, GIFTI file, MESH mesh, MNI OBJ mesh, PLY mesh, TRI mesh

right_wm_mesh :

GIFTI file, GIFTI file, MESH mesh, MNI OBJ mesh, PLY mesh, TRI mesh

left_labelled_graph :

Graph and data, Graph and data

right_labelled_graph :

Graph and data, Graph and data

talairach_transform :

Transformation matrix, Transformation matrix

brain_volumes_file :

CSV file, CSV file

normative_brain_stats :

JSON file, JSON file

report :

PDF File, PDF File

report_json :

JSON file, JSON file

inter_subject_qc_table :

TSV file, TSV file

covariables_file :

CSV file, CSV file, TSV file