DICOM is the primary format for medical images. Like the *.mhd, DICOM splits an image into metadata and raw data. Contrary to *.mhd files however, the raw data and the header are in the same file. Almost all vendors of 3D medical imaging hardware use a version of this format. As a consequence, the headers in DICOM files form different manufacturers can be very different. The Python package pydicom can be used to load DICOM files and access the header parameters.
DICOM files are usually 2D image files. Volumes are stored as folders of 2D DICOM files. A Python library called pydicom can be used to read and write DICOM files:
import pydicom
dicom_image = pydicom.dcmread('/path/to/dicom/file')The dicom_image now contains a FileDataSet object, which is basically a dictionary with parameters. Here, we have load a slice from a 3D CT image:
print(dicom_image)(0008, 0005) Specific Character Set CS: 'ISO_IR 100'
(0008, 0008) Image Type CS: ['ORIGINAL', 'PRIMARY', 'AXIAL']
(0008, 0016) SOP Class UID UI: CT Image Storage
(0008, 0018) SOP Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.143451261327128179989900675595
(0008, 0020) Study Date DA: '20000101'
(0008, 0021) Series Date DA: '20000101'
(0008, 0022) Acquisition Date DA: '20000101'
(0008, 0023) Content Date DA: '20000101'
(0008, 0024) Overlay Date DA: '20000101'
(0008, 0025) Curve Date DA: '20000101'
(0008, 002a) Acquisition DateTime DT: '20000101'
(0008, 0030) Study Time TM: ''
(0008, 0032) Acquisition Time TM: ''
(0008, 0033) Content Time TM: ''
(0008, 0050) Accession Number SH: u'2819497684894126'
(0008, 0060) Modality CS: 'CT'
(0008, 0070) Manufacturer LO: u'GE MEDICAL SYSTEMS'
(0008, 0090) Referring Physician's Name PN: u''
(0008, 1090) Manufacturer's Model Name LO: u'LightSpeed Plus'
(0008, 1155) Referenced SOP Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.675906998158803995297223798692
(0010, 0010) Patient's Name PN: u''
(0010, 0020) Patient ID LO: u'LIDC-IDRI-0001'
(0010, 0030) Patient's Birth Date DA: ''
(0010, 0040) Patient's Sex CS: ''
(0010, 1010) Patient's Age AS: ''
(0010, 21d0) Last Menstrual Date DA: '20000101'
(0012, 0062) Patient Identity Removed CS: 'YES'
(0012, 0063) De-identification Method LO: u'DCM:113100/113105/113107/113108/113109/113111'
(0013, 0010) Private Creator LO: u'CTP'
(0013, 1010) Private tag data LO: u'LIDC-IDRI'
(0013, 1013) Private tag data LO: u'62796001'
(0018, 0010) Contrast/Bolus Agent LO: u'IV'
(0018, 0015) Body Part Examined CS: 'CHEST'
(0018, 0022) Scan Options CS: 'HELICAL MODE'
(0018, 0050) Slice Thickness DS: "2.500000"
(0018, 0060) KVP DS: "120"
(0018, 0090) Data Collection Diameter DS: "500.000000"
(0018, 1020) Software Version(s) LO: u'LightSpeedApps2.4.2_H2.4M5'
(0018, 1100) Reconstruction Diameter DS: "360.000000"
(0018, 1110) Distance Source to Detector DS: "949.075012"
(0018, 1111) Distance Source to Patient DS: "541.000000"
(0018, 1120) Gantry/Detector Tilt DS: "0.000000"
(0018, 1130) Table Height DS: "144.399994"
(0018, 1140) Rotation Direction CS: 'CW'
(0018, 1150) Exposure Time IS: "570"
(0018, 1151) X-Ray Tube Current IS: "400"
(0018, 1152) Exposure IS: "4684"
(0018, 1160) Filter Type SH: u'BODY FILTER'
(0018, 1170) Generator Power IS: "48000"
(0018, 1190) Focal Spot(s) DS: "1.200000"
(0018, 1210) Convolution Kernel SH: u'STANDARD'
(0018, 5100) Patient Position CS: 'FFS'
(0020, 000d) Study Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630178
(0020, 000e) Series Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192
(0020, 0010) Study ID SH: u''
(0020, 0011) Series Number IS: "3000566"
(0020, 0013) Instance Number IS: "80"
(0020, 0032) Image Position (Patient) DS: ['-166.000000', '-171.699997', '-207.500000']
(0020, 0037) Image Orientation (Patient) DS: ['1.000000', '0.000000', '0.000000', '0.000000', '1.000000', '0.000000']
(0020, 0052) Frame of Reference UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.229925374658226729607867499499
(0020, 1040) Position Reference Indicator LO: u'SN'
(0020, 1041) Slice Location DS: "-207.500000"
(0028, 0002) Samples per Pixel US: 1
(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'
(0028, 0010) Rows US: 512
(0028, 0011) Columns US: 512
(0028, 0030) Pixel Spacing DS: ['0.703125', '0.703125']
(0028, 0100) Bits Allocated US: 16
(0028, 0101) Bits Stored US: 16
(0028, 0102) High Bit US: 15
(0028, 0103) Pixel Representation US: 1
(0028, 0120) Pixel Padding Value US: 63536
(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'
(0028, 1050) Window Center DS: "-600"
(0028, 1051) Window Width DS: "1600"
(0028, 1052) Rescale Intercept DS: "-1024"
(0028, 1053) Rescale Slope DS: "1"
(0038, 0020) Admitting Date DA: '20000101'
(0040, 0002) Scheduled Procedure Step Start Date DA: '20000101'
(0040, 0004) Scheduled Procedure Step End Date DA: '20000101'
(0040, 0244) Performed Procedure Step Start Date DA: '20000101'
(0040, 2016) Placer Order Number / Imaging Servi LO: u''
(0040, 2017) Filler Order Number / Imaging Servi LO: u''
(0040, a075) Verifying Observer Name PN: u'Removed by CTP'
(0040, a123) Person Name PN: u'Removed by CTP'
(0040, a124) UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.335419887712224178340067932923
(0070, 0084) Content Creator's Name PN: u''
(0088, 0140) Storage Media File-set UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.211790042620307056609660772296
(7fe0, 0010) Pixel Data OW: Array of 524288 bytes
As you can see, each parameter has two hexadecimal numbers associated with it. These are the DICOM tags. You can use these tags to get to specific information. For example, if you want to know the manufacturer of the scanner, you need the (0080, 0070) tag, or 0x080070 in hexadecimal:
dicom_image[0x080070]You can however also just type
dicom_image.Manufacturerwhich is a lot nicer. As you can see there is a plethora of data in a DICOM file. Important parameters are the PixelSpacing and SliceThickness, which tell you the physical dimensions of the data. Furthermore, it is nice to know where this slice was located in axial direction, which is shown by SliceLocation. Because DICOM filenames are not necessarily in the right order, you can use this slice location to order the slices if you load a 3D volume. The PixelData field contains the actual pixel data in binary format. It is a flat vector, however. Using the pixel_array attribute of the FileDataSet object, you get a much nicer Numpy array of the slice's data:
image = dicom_image.pixel_arrayThis image can be plotted with Matplotlib's imshow() function.
Because 3D DICOM data is distributed over multiple files, one for each slice, you first need to obtain all the filenames.
You can use the built-in Python module os to list the filenames in the DICOM directory:
import os
import pydicom
path = '/folder/to/a/3d/volume'
dicom_filenames = os.listdir(path)Then, it is a matter of loading each of those files into a list:
list_of_slices = []
for filename in dicom_filenames:
list_of_slices.append(pydicom.dcmread(filename))list_of_slices now contains the FileDataSet objects, each containing one slice of the volume. To order these, we write a small utility function. We use this function as the sorting key.
def order_by_slice_location(slice):
return float(slice.SliceLocation)
list_of_slices.sort(key=order_by_slice_location, reverse=True)Now, the list_of_slices is ordered from superior to inferior along the axial direction. Note that we need to set the reverse flag to True to get this ordering, as the z-coordinate decreases in this direction, and the list method sort() sorts the list in ascending order without it.
Now, we only need to get the array of pixels in each slice to get the full volume:
volume_list = []
for slice in list_of_slices:
volume_list.append(slice.pixel_array)
image = np.array(volume_list)image now contains the 3D volume, which can be plotted or used for further analysis.