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()