Note

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# Downsize MRI image using pydicom

This example shows how to downsize an MR image from 512 imes 512 to 64 imes 64. 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)

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