datman

A python project containing useful functions for managing imaging data from collection through analysis.

• Introduction
• Quality Control
• CAMH XNAT Documentation

For instructions on how to use the CAMH XNAT server, our file naming convetions, etc., please see CAMH XNAT Documentation. DATMAN relies on these conventions being followed.

Introduction

definitions

• Exam/Scan: A series of MRI acquisitions completed in one sitting.
• Series/Acquisition: A single image of a subject in one modality
• Exam archive: The raw data taken during an exam. For MRI, this could be a folder structure of DICOM images, or a tarball/zipfile of that same data,
• Type tag: A short, keyword that distinguishes a kind of acquisition from other kinds in a study. For instance, T1, DTI, REST. These type tags are used in the file naming and follow-on processing scripts to identify acquisitions without having to parse their headers/description/etc...

dependencies

• NiNet for some NIFTI-handling functionsi and analysis.
• SciPy, NumPy, Matplotlib for general analysis.
• [NiBabel](For NIFTI reading/writing.)

setup

For interfacing with xnat, datman requires that each project's Define Prearchive Settings under Manage be set to 'All image data will be placed into the archive automatically and will overwrite existing files. Data which doesn't match a pre-existing project will be placed in an 'Unassigned' project.'

Your environment needs to be set up as so:

• Add datman to your PYTHONPATH.
• Add datman/bin to your PATH.
• Add datman/assets to your PATH, PYTHONPATH, & MATLABPATH.
• Set DATMAN_ASSETS to point to datman/assets.

modules

utils

General file-handling utilities.

web

An interface between our data and gh-pages to create online data reports.

module

A set of commands for interacting with GNU module.

img

A set of commands for handling imaging data.

behav

A set of commands for handling behavioural data.

Quality Control

We've built a number of quality control pipelines to help track quality across sites and image modalities. The outputs are .PDF files typically containing many plots. Below is a brief description on the outputs of each.

T1-contrast, BOLD contrast, B0 contrast

A set of axial slices designed to give an overview of the sequence. This is useful for identifying geometric distortions in the image, severe inhomogeneities, or orientation errors.

Head Motion

For functional scans. Uses the motion-correction realignment parameters to calculate the framewise displacement (in mm/TR) of the head. This calculation currently assumes a head radius of 50mm to convert degrees of rotation into millimeters of displacement. The red line denotes the cut-off for TR scrubbing suggested in [1] resting-state scans. Subjects with a lot of TRs above this line may need to be removed from downstream analysis.

[1] Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Jonathan D. Power et al. 2011. Neuroimage 59:3.

SFNR

Shows voxel-wise signal-to-fluctuation noise ratio, a measure from the fBIRN QC pipeline.

Slice/TR Abnormalities

Shows the average and standard deviation of all values within each acquisition slice across all TRs. This allows us to visualize large deviations from the average mean value on a slice wise basis. This can happen on only some slices, and can help detect the presense of localized artifacts that occour during acquisition or reconstruction. The slice-dependent effects are particularly obvious in the DTI sequences, where particular gradient directions may be more susceptible to spike-noise.

DTI

A compliment to the slice/TR abnormalities plot for DTI sequences, this shows a single coronal slice through the center of the acquisition for each TR. This can also help us identify artifactual spatial patterns that might not be obvious in the slice/TR plot.

Phantom Scatterplots: ADNI

This tracks the T1 weighted value across the 5 primary ROIs in the ADNI phantom, and the T1 ratios between each of the higher ones with the lowest one. For more information, please see http://www.phantomlab.com/library/pdf/magphan_adni_manual.pdf.

Phantom Scatterplots: fBIRN fMRI

This uses the fBIRN pipeline to define % signal fluctuation, linear drift, signal to noise ratio, signal-to-fluctuation noise ratio, and radius of decorrelation. For more information, please see [1], http://www.ncbi.nlm.nih.gov/pubmed/16649196.

[2] Report on a multicenter fMRI quality assurance protocol. Friedman L et al. 2006. J Magn Reson Imaging 23(6).

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