Head CT Data Release

Description of the images shared:

The data consists of 35 different subject’s non-contrast CT scan, with 2
subjects having 2 scans but every other subject having one scan. One
subject additionally has 2 scans, but from the same time point, but with
different convolution kernels. Each scan has at least one reader’s
manual segmentation of the image to delineate the mask of the brain
areas (including cerebrospinal fluid (CSF)).

The data are from the Minimally-Invasive Surgery plus rt-PA for
Intracerebral hemorrhage Evacuation (MISTIE) Phases II and III clinical
trials the Clot Lysis: Evaluating Accelerated Resolution of
Intraventricular Hemorrhage (CLEAR) Phase III clinical trial. Each
subject’s scans and DICOM (Digital Imaging and Communications in
Medicine) header information are contained in a tarball (.tar.xz),
with the name of the tarball being the id of the subject (01.tar.gz
is for id 01).

Each Tarball

Image Data

Each tarball contains a series of NIfTI (Neuroimaging Informatics
Technology Initiative) files. These files can be read using common tools
such as ITK-SNAP (http://www.itksnap.org/pmwiki/pmwiki.php), Mango
(http://ric.uthscsa.edu/mango/), or Papaya
(http://ric.uthscsa.edu/mango/papaya.html). Each image has manual
segmentation from 2 different readers which is a binary image
delineating the brain and an automated estimate of the brain by the
method proposed in the paper below.

The NIfTI images are as follows:

1. BRAIN_1_Anonymized.nii.gz - CT image that has been
   de-identified/anonymized where the face and the ears have been
   removed from the image. These should have a range of -1024 to 3071
   and be in Hounsfield Units (HU).
2. BRAIN_1_Anonymized_Mask.nii.gz - a binary image denoting the
   brain areas using the method described in Muschelli et al. (2015).
   This method is also implemented in the function CT_Skull_Strip
   function in the ichseg R package
   (https://github.com/muschellij2/ichseg).
3. Manual_Mask_1_Reader_1.nii.gz - binary brain mask from Reader 1
4. Manual_Mask_1_Reader_2.nii.gz - binary brain mask from Reader 2

Reader 1 and reader 2 are consistent across subjects (e.g. reader 1 is
always reader 1).

Header information

In the tarballs, there are also CSV (Comma-separated) files, such as:

• BRAIN_1_header.csv
• Manual_Mask_1_Reader_1_header.csv
• Manual_Mask_1_Reader_2_header.csv

which contain information from the DICOM header. Mostly the only files
of interest are those with BRAIN/BONE in the name as the Manual mask
images are derived from those images. These are included for
completeness.

Specific subsets of the DICOM data/tags are given with the data:
0008-0070-MANUFACTURER 0018-0050-SLICETHICKNESS 0018-0060-KVP
0018-1120-GANTRYDETECTORTILT 0018-1151-XRAYTUBECURRENT
0018-1152-EXPOSURE 0018-1160-FILTERTYPE 0018-1170-GENERATORPOWER
0018-1210-CONVOLUTIONKERNEL 0018-5100-PATIENTPOSITION
0020-0032-IMAGEPOSITIONPATIENT 0020-1041-SLICELOCATION
0028-0030-PIXELSPACING 0008-0070-Manufacturer 0018-0050-SliceThickness
0020-0032-ImagePositionPatient 0020-1041-SliceLocation
0028-0030-PixelSpacing

Different files in some tarballs

Subject 13 has a BONE scan as well as a BRAIN scan. The BONE scan
is a non-contrast CT done with a different convolution kernel, which
gives different properties of the scan. The manual segmentations are in
the same space as both scans (i.e. no registration was done).

Subjects 06 and 16 have both a BRAIN_1 and BRAIN_2 scan. These were
images taken at 2 different time points. If the manual reader performed
segmentation on the scan, then a file such as
Manual_Mask_2_Reader_1.nii.gz would indicate the index for the scan
(e.g. Manual_Mask_2 corresponds to BRAIN_2).

Demographics

Demographic information is id column – which maps back to our original
de-identified ID, which has no information about patient other that site
and enrollment number, but the mapping is not provided or released.
Additional information such as age in years (range 40 – 78) rounded to
nearest year, sex, race (as measured in the trial), Hispanic indicator,
site ID (again maps back to the original data but no mapping is
provided) and diagnosis (dx). the diagnosis was made from the reading
center, indicating intracerebral hemorrhage (ICH) or ICH with
intraventricular hemorrhage (IVH), indicated as ICH with IVH in the dx
column.

The site ID is to allow researchers to see if there is a small site
effect (but not likely possible due to how few subjects).

The de-identification mechanism

A mask was determined outlined on a template of a mask and the ears of
the image. The face and ear masking of the data was done by registering
a template image to the individual images separately. The method is
similar to pydeface (https://github.com/poldracklab/pydeface) and
was implemented in the fslr (https://github.com/muschellij2/fslr)
package. The template data was adapted from pydeface and the template
used for registration was
https://github.com/muschellij2/pydeface/raw/master/pydeface/data/mean_reg2mean.nii.gz
and the template mask used was
https://github.com/muschellij2/pydeface/raw/master/pydeface/data/facemask_no_ears.nii.gz.

References

Muschelli, John, et al. “Validated automatic brain extraction of head CT
images.” Neuroimage 114 (2015): 379-385.
https://doi.org/10.1016/j.neuroimage.2015.03.074