Examples

Split MRI diffusion series into separate series for each b value

Using python:

Write each b value in turn by looping over first index of 4D DWI dataset. Each new series will have a unique Series Instance UID, such that a PACS will handle each b value as separate series.

from imagedata.series import Series

dwi = Series('10_ep2d_diff_b50_400_800/', 'b')

# dwi.tags[0] has the b values, e.g. [50, 400, 800]

for i, b in enumerate(dwi.tags[0]):
    # Output to folders b50/, b400/ and b800/
    dwi[i].write('b{}'.format(b))

More elaborate example in python:

To set a separate series number and description for each series, each volume of b values must be separate objects:

from imagedata.series import Series

dwi = Series('10_ep2d_diff_b50_400_800/', 'b')

for i, b in enumerate(dwi.tags[0]):
    # Output to folders b50/, b400/ and b800/
    dwi[i].write('b{}'.format(b))
    for i, b in enumerate(dwi.tags[0]):
        s = dwi[i]
        s.seriesNumber = 100 + i
        s.seriesDescription = 'b {}'.format(b)
        s.write('b{}'.format(b))

Using command line:

Split b values using `–odir multi’ parameter. Each b value will be written to folder tmp/b0, tmp/b1, etc. However, all folders will share the Series Instance UID.

To make unique Series Instance UIDs, run image_data on each created folder.

In the following example, the b values are first split to folders tmp/b0, tmp/b1, etc. Next, each tmp/b* series is copied again, producing separate Series Instance UIDs. Notice how each series is given a separate series number and series description.

image_data --order b --odir multi tmp 10_ep2d_diff_b50_400_800

image_data --sernum 100 --serdes "b0" out/b0 tmp/b0
image_data --sernum 101 --serdes "b50" out/b50 tmp/b1
image_data --sernum 102 --serdes "b100" out/b100 tmp/b2

rm -r tmp

Segment an image, display image with segmented ROI in red

The following example will let the user segment an image (using get_roi() method). An RGB version of the original image is produced by the get_roi() method, where each of the RGB components are set to the original gray scale value.

`segment_indices’ address the selected area, and is used to set the green (1) and blue components (2) to zero. Hence, the [1:] slicing of the color components RGB.

Finally, the color image is display with the segmented area in red.

from imagedata.series import Series

T2 = Series('801_Obl T2 TSE HR SENSE/')
segment = T2.get_roi()

T2rgb = T2.to_rgb()
segment_indices = segment == 1

# Clear green and blue components inside segmentation,
# leaving the red component
T2rgb[segment_indices,1:] = 0

T2rgb.show()

Motion correction using FSL MCFLIRT

Motion correction using image registration is a process where different images of a patient are transformed to a common reference frame. This example uses the FSL MCFLIRT program for this task. MCFLIRT takes NIfTI input and output. Hence, this example will write a Series instance to a temporary NIfTI file, call MCFLIRT, then read back the resulting NIfTI file using the original Series instance as a template for DICOM header information.

import tempfile
from pathlib import Path
from imagedata.series import Series
import nipype.interfaces.fsl as fsl

def mcflirt(dce, fx):
    """Register dynamic series using FSL MCFLIRT
    Args:
        dce: dynamic series [t, slice, row, column]
        fx: index of fixed volume in dce (int)
    Returns:
        registered Series
    """

    assert fx >= 0 and fx < len(dce), "Wrong fixed index {}".format(fx)
    print('\nPreparing for MCFLIRT ...')
    with tempfile.TemporaryDirectory() as tmp:
        p = Path(tmp)
        tmp_fixed = p / 'fixed'
        dce[fx].write(tmp_fixed, formats=['nifti'])
        fixed = list(tmp_fixed.glob('*'))[0]
        tmp_moving = p / 'moving'
        dce.write(tmp_moving, formats=['nifti'])
        moving = list(tmp_moving.glob('*'))[0]

        print('MCFLIRT running ...')
        tmp_out = p / 'out.nii.gz'

        mcflt = fsl.MCFLIRT()
        mcflt.inputs.in_file = str(moving)
        # mcflt.inputs.ref_file = str(fixed)
        mcflt.inputs.ref_vol = fx
        mcflt.inputs.out_file = str(tmp_out)
        mcflt.inputs.cost = "corratio"
        # mcflt.inputs.cost     = "normcorr"
        print('{}'.format(mcflt.cmdline))
        result = mcflt.run()

        dce2 = Series(tmp_out, input_order=dce.input_order, template=dce, geometry=dce)
        dce2.tags = dce.tags
        dce2.axes = dce.axes
        dce2.seriesDescription = 'MCFLIRT {}'.format(mcflt.inputs.cost)
    print('MCFLIRT ended.\n')
    return dce2