Downsize MRI image using pydicom

This example shows how to downsize an MR image from to . The downsizing is performed by taking the central section instead of averagin the pixels. Finally, the image is store as a dicom image.

Note

This example requires the Numpy library to manipulate the pixel data.

The image has 64 x 64 voxels
The downsampled image has 8 x 8 voxels
The information of the data set after downsampling:

Dataset.file_meta -------------------------------
(0002,0000) File Meta Information Group Length  UL: 190
(0002,0001) File Meta Information Version       OB: b'\x00\x01'
(0002,0002) Media Storage SOP Class UID         UI: MR Image Storage
(0002,0003) Media Storage SOP Instance UID      UI: 1.3.6.1.4.1.5962.1.1.4.1.1.20040826185059.5457
(0002,0010) Transfer Syntax UID                 UI: Explicit VR Little Endian
(0002,0012) Implementation Class UID            UI: 1.3.6.1.4.1.5962.2
(0002,0013) Implementation Version Name         SH: 'DCTOOL100'
(0002,0016) Source Application Entity Title     AE: 'CLUNIE1'
-------------------------------------------------
(0008,0008) Image Type                          CS: ['DERIVED', 'SECONDARY', 'OTHER']
(0008,0012) Instance Creation Date              DA: '20040826'
(0008,0013) Instance Creation Time              TM: '185434'
(0008,0014) Instance Creator UID                UI: 1.3.6.1.4.1.5962.3
(0008,0016) SOP Class UID                       UI: MR Image Storage
(0008,0018) SOP Instance UID                    UI: 1.3.6.1.4.1.5962.1.1.4.1.1.20040826185059.5457
(0008,0020) Study Date                          DA: '20040826'
(0008,0021) Series Date                         DA: ''
(0008,0022) Acquisition Date                    DA: ''
(0008,0030) Study Time                          TM: '185059'
(0008,0031) Series Time                         TM: ''
(0008,0032) Acquisition Time                    TM: ''
(0008,0050) Accession Number                    SH: ''
(0008,0060) Modality                            CS: 'MR'
(0008,0070) Manufacturer                        LO: 'TOSHIBA_MEC'
(0008,0080) Institution Name                    LO: 'TOSHIBA'
(0008,0090) Referring Physician's Name          PN: ''
(0008,0201) Timezone Offset From UTC            SH: '-0400'
(0008,1010) Station Name                        SH: '000000000'
(0008,1060) Name of Physician(s) Reading Study  PN: '----'
(0008,1070) Operators' Name                     PN: '----'
(0008,1090) Manufacturer's Model Name           LO: 'MRT50H1'
(0010,0010) Patient's Name                      PN: 'CompressedSamples^MR1'
(0010,0020) Patient ID                          LO: '4MR1'
(0010,0030) Patient's Birth Date                DA: ''
(0010,0040) Patient's Sex                       CS: 'F'
(0010,1020) Patient's Size                      DS: None
(0010,1030) Patient's Weight                    DS: '80.0000'
(0018,0010) Contrast/Bolus Agent                LO: ''
(0018,0020) Scanning Sequence                   CS: 'SE'
(0018,0021) Sequence Variant                    CS: 'NONE'
(0018,0022) Scan Options                        CS: ''
(0018,0023) MR Acquisition Type                 CS: '3D'
(0018,0050) Slice Thickness                     DS: '0.8000'
(0018,0080) Repetition Time                     DS: '4000.0000'
(0018,0081) Echo Time                           DS: '240.0000'
(0018,0083) Number of Averages                  DS: '1.0000'
(0018,0084) Imaging Frequency                   DS: '63.92433900'
(0018,0085) Imaged Nucleus                      SH: 'H'
(0018,0086) Echo Number(s)                      IS: '1'
(0018,0091) Echo Train Length                   IS: None
(0018,1000) Device Serial Number                LO: '-0000200'
(0018,1020) Software Versions                   LO: 'V3.51*P25'
(0018,1314) Flip Angle                          DS: '90'
(0018,5100) Patient Position                    CS: 'HFS'
(0020,000D) Study Instance UID                  UI: 1.3.6.1.4.1.5962.1.2.4.20040826185059.5457
(0020,000E) Series Instance UID                 UI: 1.3.6.1.4.1.5962.1.3.4.1.20040826185059.5457
(0020,0010) Study ID                            SH: '4MR1'
(0020,0011) Series Number                       IS: '1'
(0020,0012) Acquisition Number                  IS: '0'
(0020,0013) Instance Number                     IS: '1'
(0020,0032) Image Position (Patient)            DS: [-83.9063, -91.2000, 6.6406]
(0020,0037) Image Orientation (Patient)         DS: [1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000]
(0020,0052) Frame of Reference UID              UI: 1.3.6.1.4.1.5962.1.4.4.1.20040826185059.5457
(0020,0060) Laterality                          CS: ''
(0020,1040) Position Reference Indicator        LO: ''
(0020,1041) Slice Location                      DS: '0.0000'
(0020,4000) Image Comments                      LT: 'Uncompressed'
(0028,0002) Samples per Pixel                   US: 1
(0028,0004) Photometric Interpretation          CS: 'MONOCHROME2'
(0028,0010) Rows                                US: 8
(0028,0011) Columns                             US: 8
(0028,0030) Pixel Spacing                       DS: [0.3125, 0.3125]
(0028,0100) Bits Allocated                      US: 16
(0028,0101) Bits Stored                         US: 16
(0028,0102) High Bit                            US: 15
(0028,0103) Pixel Representation                US: 1
(0028,0106) Smallest Image Pixel Value          SS: 0
(0028,0107) Largest Image Pixel Value           SS: 4000
(0028,1050) Window Center                       DS: '600'
(0028,1051) Window Width                        DS: '1600'
(7FE0,0010) Pixel Data                          OW: Array of 128 elements
(FFFC,FFFC) Data Set Trailing Padding           OB: Array of 126 elements

# authors : Guillaume Lemaitre <g.lemaitre58@gmail.com>
# license : MIT

from pydicom import examples

print(__doc__)

# FIXME: add a full-sized MR image in the testing data
ds = examples.mr

# get the pixel information into a numpy array
arr = ds.pixel_array
print(f"The image has {arr.shape[0]} x {arr.shape[1]} voxels")
arr_downsampled = arr[::8, ::8]
print(
    f"The downsampled image has {arr_downsampled.shape[0]} x {arr_downsampled.shape[1]} voxels"
)

# copy the data back to the original data set
ds.PixelData = arr_downsampled.tobytes()
# update the information regarding the shape of the data array
ds.Rows, ds.Columns = arr_downsampled.shape

# print the image information given in the dataset
print("The information of the data set after downsampling: \n")
print(ds)