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 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 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 fused image with segmented ROI in red

The following example will let the user segment an image (using get_roi() method). A fused image where the segmented area is enhanced with red color is produced by fuse_segment().

Finally, the color image is displayed.

from imagedata import Series

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

T2rgb = T2.fuse_mask(segment)

T2rgb.show()

Draw a time curve when mask is moved

import numpy as np
import matplotlib.pyplot as plt
from imagedata.viewer import grid_from_roi

def plot_aif(idx, tag, vertices):
    # Called from Viewer when vertices are modified
    # Clear any previous axis plot
    plt.sca(ax[1])
    plt.cla()
    if vertices is None:
        return
    mask = grid_from_roi(si, {idx: vertices})
    curve = np.sum(si, axis=(1, 2, 3), where=mask == 1) / np.count_nonzero(mask)
    ax[1].plot(curve, label='AIF')
    ax[1].legend()
    ax[1].figure.canvas.draw()

si = Series('data', 'time')
fig, ax = plt.subplots(1, 2)
mask = si.get_roi(ax=ax[0], onselect=plot_aif)

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 import Series
import nipype.interfaces.fsl as fsl


def mcflirt(moving, fixed):
    """Register dynamic series using FSL MCFLIRT
    Args:
        moving: moving (Series)
        fixed:  reference volume, either
            index into moving (Series), or
            separate volume (int)
    Returns:
        registered Series
    """

    if issubclass(type(fixed), Series):
        if fixed.ndim == 3:
            ref = fixed
            ref_volume = fixed
        else:
            raise ValueError('Fixed volume should be 3D (is {})'.format(fixed.ndim))
    else:
        assert fixed >= 0 and fixed < len(moving), "Wrong fixed index {}".format(fixed)
        ref = fixed
        ref_volume = moving[ref]
    print('\nPreparing for MCFLIRT ...')
    with tempfile.TemporaryDirectory() as tmp:
        p = Path(tmp)
        fixed_file = p / 'fixed.nii.gz'
        moving_file = p / 'moving.nii.gz'
        tmp_out = p / 'out.nii.gz'
        moving.write(moving_file, formats=['nifti'])

        print('MCFLIRT running ...')

        mcflt = fsl.MCFLIRT()
        mcflt.inputs.in_file = str(moving_file)
        if issubclass(type(ref), Series):
            ref.write(fixed_file, formats=['nifti'])
            mcflt.inputs.ref_file = str(fixed_file)
        else:
            mcflt.inputs.ref_vol = ref
        mcflt.inputs.out_file = str(tmp_out)
        mcflt.inputs.cost = "corratio"  # "normcorr"
        # mcflt.inputs.cost     = "normcorr"
        print('{}'.format(mcflt.cmdline))
        result = mcflt.run()
        print('Result code: {}'.format(result.runtime.returncode))

        try:
            out = Series(tmp_out, input_order=moving.input_order, template=moving, geometry=ref_volume)
        except Exception as e:
            print(e)
        out.tags = moving.tags
        out.axes = moving.axes
        out.seriesDescription = 'MCFLIRT {}'.format(mcflt.inputs.cost)
    print('MCFLIRT ended.\n')
    return out