# 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>