DICOM_Using_PyDicom/Introducing_PyDicom,_its_Classes,_Methods,_and_Attributes.ipynb

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      "source": [
        "# **PyDicom… the best choice for Pythonistas**\n",
        "\n",
        "> **If you are a Pythonista** and you're working with Medical Data, especially DICOM data, you've probably heard of the great python package PyDicom. In this post, you'll learn the basics of this package. We'll discuss PyDicom classes (DataSet, DataElement, Sequence) and some useful methods and attributes that you can apply to PyDicom classes.\n",
        "\n",
        "> **PyDicom** is a pure Python package for working with DICOM files such as medical images, reports, and radiotherapy objects. PyDicom makes it easy to read and write these complex files into natural pythonic structures for easy manipulation.\n",
        "\n",
        "> **DICOM**, Digital Imaging and Communications in Medicine, is the standard language of Medical Imaging Systems. When you hear the word \"DICOM\", you probably think of it as just medical images. But **Healthcare Data Scientists/Analysts** may consider it as treasury as any other medical Dataset. Because it goes beyond just data of pixels. A DICOM file contains many useful data related to the patient, the imaging modality, and others.\n",
        "\n",
        "> Dealing with DICOM metadata and accessing specific attributes and data is very helpful for anyone, data scientist/analyst, working with DICOM files. Luckily, PyDicom provides many useful functions, methods, and attributes on DICOM files."
      ],
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      "source": [
        "# **Let's Begin Coding**\n",
        "When I want to discover a new Python package, I usually begin with a general examples using this package and then dive into it more and more gradually. My main purpose is to learn how to extract the metadata of many DICOM files and convert it to a Dataset that I can use for analyzing exploration.\n",
        "\n",
        "So… let's begin our journey with common example of PyDicom.\n",
        "\n",
        "##We will discuss the following:\n",
        "> **Example of reading a DICOM File**\n",
        "\n",
        "> **Core Elements in PyDicom**\n",
        "\n",
        "> **PyDICOM DataSet**\n",
        "\n",
        "> **Methods for PyDicom DataSet**\n",
        "- **.keys()** Method\n",
        "- **.values()** Method\n",
        "- **.elements()** Method\n",
        "- **.group_dataset()** Method\n",
        "- **.dir() Method**\n",
        "\n",
        "> **Attributes for PyDicom DataSet**\n",
        "- **pixel_array** Attribute\n",
        "\n",
        "> **PyDicom DataElement**\n",
        "\n",
        "> **PyDicom Sequence**"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "# **Example of reading a DICOM File**"
      ],
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      "cell_type": "markdown",
      "source": [
        "To read a DICOM file using pydicom, you can use the following steps:\n",
        "- Install pydicom: To use pydicom, you will need to install it using pip."
      ],
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pydicom\n",
            "  Downloading pydicom-2.3.1-py3-none-any.whl (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 5.4 MB/s \n",
            "\u001b[?25hInstalling collected packages: pydicom\n",
            "Successfully installed pydicom-2.3.1\n"
          ]
        }
      ],
      "source": [
        "# Install PyDicom Package\n",
        "!pip install pydicom"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Import the pydicom library: In your Python script, you will need to import the pydicom library in order to use it."
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Import the pacakage\n",
        "import pydicom"
      ],
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      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "> There are a lot of DICOM samples on the internet. And you can use your own DICOM files. For this post, I chose a [dataset](https://www.kaggle.com/datasets/dmisky/dlwptvolumetricdicomlung) of 99 slices of chest-CT scan for one patient. You can find it on Kaggle. I saved the dataset on my [Drive](https://drive.google.com/drive/folders/1dbJpXVUBn0mSUxgJIqKZj4d_0thSeTW3) so I can easily have access through GoogleColab.\n",
        "\n",
        "\n",
        "- Read the DICOM file: To read a DICOM file, you can use the dcmread function, which returns a Dataset object containing the data from the DICOM file.\n",
        "\n",
        "> Note: Sometimes, the output of some code lines will be too long and bulky. Sorry in advance about that!!! But this a great chance for you to get a taste of how DICOM files look like in real life. So, I hope you bear with me for these long outputs :)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Reading a DICOM file from a specific path\n",
        "dcm_data = pydicom.dcmread('/content/drive/MyDrive/Datasets/Kaggle/DICOM/dicom_lung/000000.dcm')\n",
        "dcm_data"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset.file_meta -------------------------------\n",
              "(0002, 0000) File Meta Information Group Length  UL: 196\n",
              "(0002, 0001) File Meta Information Version       OB: b'\\x00\\x01'\n",
              "(0002, 0002) Media Storage SOP Class UID         UI: CT Image Storage\n",
              "(0002, 0003) Media Storage SOP Instance UID      UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.214140401149739061368142942055\n",
              "(0002, 0010) Transfer Syntax UID                 UI: Explicit VR Little Endian\n",
              "(0002, 0012) Implementation Class UID            UI: 1.2.40.0.13.1.1.1\n",
              "(0002, 0013) Implementation Version Name         SH: 'dcm4che-1.4.35'\n",
              "-------------------------------------------------\n",
              "(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'\n",
              "(0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'AXIAL', 'CT_SOM5 SPI']\n",
              "(0008, 0016) SOP Class UID                       UI: CT Image Storage\n",
              "(0008, 0018) SOP Instance UID                    UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.214140401149739061368142942055\n",
              "(0008, 0020) Study Date                          DA: '20100227'\n",
              "(0008, 0021) Series Date                         DA: '20100227'\n",
              "(0008, 0022) Acquisition Date                    DA: '20100227'\n",
              "(0008, 0023) Content Date                        DA: '20100227'\n",
              "(0008, 0030) Study Time                          TM: '161937.171'\n",
              "(0008, 0031) Series Time                         TM: '162536.14'\n",
              "(0008, 0032) Acquisition Time                    TM: '162203.028699'\n",
              "(0008, 0033) Content Time                        TM: '162203.028699'\n",
              "(0008, 0050) Accession Number                    SH: '1598252606449858'\n",
              "(0008, 0060) Modality                            CS: 'CT'\n",
              "(0008, 0070) Manufacturer                        LO: 'SIEMENS'\n",
              "(0008, 0090) Referring Physician's Name          PN: ''\n",
              "(0008, 1030) Study Description                   LO: 'CT CHEST W IV CONTRAST'\n",
              "(0008, 103e) Series Description                  LO: 'LUNG 3.0  B70f'\n",
              "(0008, 1090) Manufacturer's Model Name           LO: 'Sensation 16'\n",
              "(0008, 1110)  Referenced Study Sequence  1 item(s) ---- \n",
              "   (0008, 1150) Referenced SOP Class UID            UI: Detached Study Management SOP Class\n",
              "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.369429505691935722415265978954\n",
              "   ---------\n",
              "(0008, 1140)  Referenced Image Sequence  1 item(s) ---- \n",
              "   (0008, 1150) Referenced SOP Class UID            UI: CT Image Storage\n",
              "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.205235983215257790411718232367\n",
              "   ---------\n",
              "(0008, 2112)  Source Image Sequence  1 item(s) ---- \n",
              "   (0008, 1150) Referenced SOP Class UID            UI: 1.3.12.2.1107.5.9.1\n",
              "   (0008, 1155) Referenced SOP Instance UID         UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.311297831929978695651149773236\n",
              "   ---------\n",
              "(0009, 0010) Private Creator                     LO: 'SIEMENS CT VA1 DUMMY'\n",
              "(0010, 0010) Patient's Name                      PN: 'C3N-00247'\n",
              "(0010, 0020) Patient ID                          LO: 'C3N-00247'\n",
              "(0010, 0030) Patient's Birth Date                DA: ''\n",
              "(0010, 0040) Patient's Sex                       CS: 'F'\n",
              "(0010, 1010) Patient's Age                       AS: '077Y'\n",
              "(0010, 2160) Ethnic Group                        SH: '8'\n",
              "(0012, 0050) Clinical Trial Time Point ID        LO: '97'\n",
              "(0012, 0051) Clinical Trial Time Point Descripti ST: 'Days offset from diagnosis'\n",
              "(0012, 0062) Patient Identity Removed            CS: 'YES'\n",
              "(0012, 0063) De-identification Method            LO: 'Per DICOM PS 3.15 AnnexE. Details in 0012,0064'\n",
              "(0012, 0064)  De-identification Method Code Sequence  8 item(s) ---- \n",
              "   (0008, 0100) Code Value                          SH: '113100'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Basic Application Confidentiality Profile'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113101'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Clean Pixel Data Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113104'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Clean Structured Content Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113105'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Clean Descriptors Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113107'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Retain Longitudinal Temporal Information Modified Dates Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113108'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Retain Patient Characteristics Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113109'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Retain Device Identity Option'\n",
              "   ---------\n",
              "   (0008, 0100) Code Value                          SH: '113111'\n",
              "   (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              "   (0008, 0104) Code Meaning                        LO: 'Retain Safe Private Option'\n",
              "   ---------\n",
              "(0013, 0010) Private Creator                     LO: 'CTP'\n",
              "(0013, 1010) Private tag data                    LO: 'CPTAC-LSCC'\n",
              "(0013, 1013) Private tag data                    LO: '70852626'\n",
              "(0018, 0015) Body Part Examined                  CS: 'CHEST'\n",
              "(0018, 0050) Slice Thickness                     DS: '3.0'\n",
              "(0018, 0060) KVP                                 DS: '100.0'\n",
              "(0018, 0090) Data Collection Diameter            DS: '500.0'\n",
              "(0018, 1020) Software Versions                   LO: 'syngo CT 2014A'\n",
              "(0018, 1030) Protocol Name                       LO: 'CHEST_ROUTINE_SMALL'\n",
              "(0018, 1100) Reconstruction Diameter             DS: '289.0'\n",
              "(0018, 1110) Distance Source to Detector         DS: '1040.0'\n",
              "(0018, 1111) Distance Source to Patient          DS: '570.0'\n",
              "(0018, 1120) Gantry/Detector Tilt                DS: '0.0'\n",
              "(0018, 1130) Table Height                        DS: '137.0'\n",
              "(0018, 1140) Rotation Direction                  CS: 'CW'\n",
              "(0018, 1150) Exposure Time                       IS: '500'\n",
              "(0018, 1151) X-Ray Tube Current                  IS: '237'\n",
              "(0018, 1152) Exposure                            IS: '131'\n",
              "(0018, 1160) Filter Type                         SH: '0'\n",
              "(0018, 1170) Generator Power                     IS: '35'\n",
              "(0018, 1190) Focal Spot(s)                       DS: '1.2'\n",
              "(0018, 1200) Date of Last Calibration            DA: '20100227'\n",
              "(0018, 1201) Time of Last Calibration            TM: '061404'\n",
              "(0018, 1210) Convolution Kernel                  SH: 'B70f'\n",
              "(0018, 5100) Patient Position                    CS: 'HFS'\n",
              "(0018, 9306) Single Collimation Width            FD: 1.5\n",
              "(0018, 9307) Total Collimation Width             FD: 24.0\n",
              "(0018, 9309) Table Speed                         FD: 43.0\n",
              "(0018, 9310) Table Feed per Rotation             FD: 21.6\n",
              "(0018, 9311) Spiral Pitch Factor                 FD: 0.9\n",
              "(0018, 9323) Exposure Modulation Type            CS: 'XYZ_EC'\n",
              "(0018, 9324) Estimated Dose Saving               FD: 29.4\n",
              "(0018, 9345) CTDIvol                             FD: 5.924999999999999\n",
              "(0019, 0010) Private Creator                     LO: 'SIEMENS CT VA0  COAD'\n",
              "(0019, 10b0) [Feed per Rotation]                 DS: '21.6'\n",
              "(0020, 000d) Study Instance UID                  UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.258626612405225511766549337110\n",
              "(0020, 000e) Series Instance UID                 UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.242193282649561899185427104083\n",
              "(0020, 0010) Study ID                            SH: ''\n",
              "(0020, 0011) Series Number                       IS: '2'\n",
              "(0020, 0012) Acquisition Number                  IS: '2'\n",
              "(0020, 0013) Instance Number                     IS: '75'\n",
              "(0020, 0032) Image Position (Patient)            DS: [-143.2177734375, -287.2177734375, -216]\n",
              "(0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 1, 0]\n",
              "(0020, 0052) Frame of Reference UID              UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.118309119521421997707953128381\n",
              "(0020, 1040) Position Reference Indicator        LO: ''\n",
              "(0020, 1041) Slice Location                      DS: '-216.0'\n",
              "(0020, 4000) Image Comments                      LT: '\\nPOST CONTRAST'\n",
              "(0021, 0010) Private Creator                     LO: 'SIEMENS MED'\n",
              "(0021, 1011) [Target]                            DS: [1, -6]\n",
              "(0028, 0002) Samples per Pixel                   US: 1\n",
              "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
              "(0028, 0010) Rows                                US: 512\n",
              "(0028, 0011) Columns                             US: 512\n",
              "(0028, 0030) Pixel Spacing                       DS: [0.564453125, 0.564453125]\n",
              "(0028, 0100) Bits Allocated                      US: 16\n",
              "(0028, 0101) Bits Stored                         US: 12\n",
              "(0028, 0102) High Bit                            US: 11\n",
              "(0028, 0103) Pixel Representation                US: 0\n",
              "(0028, 0106) Smallest Image Pixel Value          US: 0\n",
              "(0028, 0107) Largest Image Pixel Value           US: 4095\n",
              "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n",
              "(0028, 1050) Window Center                       DS: [-500, 40]\n",
              "(0028, 1051) Window Width                        DS: [1500, 400]\n",
              "(0028, 1052) Rescale Intercept                   DS: '-1024.0'\n",
              "(0028, 1053) Rescale Slope                       DS: '1.0'\n",
              "(0028, 1055) Window Center & Width Explanation   LO: ['WINDOW1', 'WINDOW2']\n",
              "(0029, 0010) Private Creator                     LO: 'SIEMENS CSA HEADER'\n",
              "(0029, 0011) Private Creator                     LO: 'SIEMENS MEDCOM HEADER'\n",
              "(0029, 0012) Private Creator                     LO: 'MITRA PRESENTATION 1.0'\n",
              "(0031, 0010) Private Creator                     LO: 'AGFA PACS Archive Mirroring 1.0'\n",
              "(0032, 000a) Study Status ID                     CS: 'READ'\n",
              "(0032, 000c) Study Priority ID                   CS: 'LOW'\n",
              "(0032, 1030) Reason for Study                    LO: 'Malignant neoplasm of lower lobe, right bronchus or lung, RLL bi'\n",
              "(0032, 1060) Requested Procedure Description     LO: 'CT CHEST W IV CONTRAST'\n",
              "(0032, 4000) Study Comments                      LT: ''\n",
              "(0033, 0010) Private Creator                     LO: 'MITRA OBJECT UTF8 ATTRIBUTES 1.0'\n",
              "(0040, 0244) Performed Procedure Step Start Date DA: '20100227'\n",
              "(0040, 0245) Performed Procedure Step Start Time TM: '161937.171'\n",
              "(7fe0, 0010) Pixel Data                          OW: Array of 524288 elements"
            ]
          },
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          "execution_count": 3
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Clearly, there are a lot of DICOM metadata other than pixel data. But we can see a pattern of the output, whereas the DICOM attributes are written row by row. Each row has a unique tag and other core elements of the DICOM attribute. Let's discuss them in more detail."
      ],
      "metadata": {
        "id": "HFgOAPP0KtCH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Core Elements in PyDicom**"
      ],
      "metadata": {
        "id": "HmGcOyNU78Dy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying .dcmread() wraps a DataSet, which is a dictionary data structure {}. This DataSet contains keys and values represented like the following:\n",
        "- ***Keys:*** contains the DICOM Tags of the attributes specified in the DICOM file you are reading. Examples of the keys like:\n",
        " - (0x0010, 0x0010) PatientName attribute.\n",
        " - (0x0028, 0x0010) Rows attribute.\n",
        " - (0x7fe0, 0x0010) PixelData attribute.\n",
        "     \n",
        "  The tags' numbers consist of two hexadecimal, the first refers to the group, and the second refers to a specific element. So, you might find many attributes that have the same first number of tags.\n",
        "\n",
        "- ***Values:*** the values of this dictionary generally contain the following:\n",
        " - **Tag:** the element's tag like (0028, 0030), for example.\n",
        " - **Keyword:** describes what the attribute refers to. The keyword of the tag (0028, 0030) is \"Pixel Spacing\".\n",
        " - **VR:** it's only two characters that refer to the Value Representation of the element, which describes the data type and format of the attribute value. The VR of the tag (0028, 0030) is \"DS\". You can see the VR of any tag and how it is represented using Python structures following the link.\n",
        " - **Value:** the actual value of the element. It could be an integer, a string, a list, or even a Sequence, which is a dataset of attributes. The value of the tag (0028, 0030) is a list of two floats that represent the physical distance along the rows and columns, respectively, in mm. An example of this list is [0.564453125, 0.564453125]."
      ],
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        "id": "vvM04GgSEzX1"
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      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "8HyqP4MYeqXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we understand reading a DICOM file using PyDicom requires working directly with the main class, which is DataSet. Let's Discuss it in more depth and see what are the contents of a DataSet."
      ],
      "metadata": {
        "id": "mdop4bkTLXfV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**PyDICOM DataSet**\n",
        "A DICOM DataSet is a mutable mapping of DICOM Data Elements. Each DataElement, a value of the dictionary, in the DICOM DataSet has a unique tag, a key of the dictionary, that identifies it. For example, the \"PatientName\" attribute corresponds to the tag (0x0010, 0x0010) in the DICOM standard, which identifies the patient's name data element. You can access a specific DICOM attribute using its name, PatientName for example."
      ],
      "metadata": {
        "id": "_R8LzlrK06ca"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![pydicom 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)"
      ],
      "metadata": {
        "id": "y_KaaLD1ykxm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can access specific DICOM attributes in many different ways like the following:"
      ],
      "metadata": {
        "id": "pjIS6LhP4NEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the patient's name.\n",
        "patient_name = dcm_data.PatientName\n",
        "patient_name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hgjjFhrSak2",
        "outputId": "992c7249-4031-464d-f8de-72140bbcdddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C3N-00247'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or you can access the same attribute, PatientName, using its unique tag. For example, the tag of the PatientName attribute is (0010, 0010). Note that the numbers are in Hexadecimals as it is in the DICOM Standard."
      ],
      "metadata": {
        "id": "p2SDOIOYV1uM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the patient's name using its unique DICOM tag (0010, 0010)\n",
        "dcm_data[0x0010, 0x0010]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7aWIjrXhkG-",
        "outputId": "7bac22da-f4ba-441c-8d59-e87638df482b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0010, 0010) Patient's Name                      PN: 'C3N-00247'"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Notice** that using the tag, which is the key of the dataset dictionary, yields the whole value of the dictionary and not only the actual value of the attribute. To get only the actual value, you should use the .value attribute."
      ],
      "metadata": {
        "id": "aZzAh766hwTg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the patient's name using its unique DICOM tag (0010, 0010)\n",
        "dcm_data[0x0010, 0x0010].value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IhAmq2qWQec",
        "outputId": "790b1ac3-8939-464c-cbaa-45e1743c464e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C3N-00247'"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's up to you to decide whether you extract the specific attribute using its unique tag or using the keyword. Now let's see some of the useful methods and attributes we can use with DataSet class."
      ],
      "metadata": {
        "id": "63OebdbGMZ3r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Methods for PyDicom DataSet**"
      ],
      "metadata": {
        "id": "nlPoZ4FY1EDT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **.keys() Method**\n",
        "Using .keys() returns the list of the keys of the DataSet dictionary. This method could be helpful when joining metadata from multiple sources of DICOM files that have common DICOM attributes."
      ],
      "metadata": {
        "id": "pMQDRe4Z8ofM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the keys, the DICOM tags, that are in a DICOM file\n",
        "dcm_data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qbz97S20xhn7",
        "outputId": "687a5c92-9886-4be0-8ee5-a90273a48873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([(0008, 0005), (0008, 0008), (0008, 0016), (0008, 0018), (0008, 0020), (0008, 0021), (0008, 0022), (0008, 0023), (0008, 0030), (0008, 0031), (0008, 0032), (0008, 0033), (0008, 0050), (0008, 0060), (0008, 0070), (0008, 0090), (0008, 1030), (0008, 103e), (0008, 1090), (0008, 1110), (0008, 1140), (0008, 2112), (0009, 0010), (0010, 0010), (0010, 0020), (0010, 0030), (0010, 0040), (0010, 1010), (0010, 2160), (0012, 0050), (0012, 0051), (0012, 0062), (0012, 0063), (0012, 0064), (0013, 0010), (0013, 1010), (0013, 1013), (0018, 0015), (0018, 0050), (0018, 0060), (0018, 0090), (0018, 1020), (0018, 1030), (0018, 1100), (0018, 1110), (0018, 1111), (0018, 1120), (0018, 1130), (0018, 1140), (0018, 1150), (0018, 1151), (0018, 1152), (0018, 1160), (0018, 1170), (0018, 1190), (0018, 1200), (0018, 1201), (0018, 1210), (0018, 5100), (0018, 9306), (0018, 9307), (0018, 9309), (0018, 9310), (0018, 9311), (0018, 9323), (0018, 9324), (0018, 9345), (0019, 0010), (0019, 10b0), (0020, 000d), (0020, 000e), (0020, 0010), (0020, 0011), (0020, 0012), (0020, 0013), (0020, 0032), (0020, 0037), (0020, 0052), (0020, 1040), (0020, 1041), (0020, 4000), (0021, 0010), (0021, 1011), (0028, 0002), (0028, 0004), (0028, 0010), (0028, 0011), (0028, 0030), (0028, 0100), (0028, 0101), (0028, 0102), (0028, 0103), (0028, 0106), (0028, 0107), (0028, 0303), (0028, 1050), (0028, 1051), (0028, 1052), (0028, 1053), (0028, 1055), (0029, 0010), (0029, 0011), (0029, 0012), (0031, 0010), (0032, 000a), (0032, 000c), (0032, 1030), (0032, 1060), (0032, 4000), (0033, 0010), (0040, 0244), (0040, 0245), (7fe0, 0010)])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **.values() Method**\n",
        "This method returns a list of the values of the DataSet dictionary. It's kinda bulky and not preferred to read in this form. But this method could be useful for iterating over a list of values in some cases."
      ],
      "metadata": {
        "id": "f1hU6Oi18wKh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the values, the DICOM attributes, that are in a DICOM file\n",
        "dcm_data.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_lGyVGoxzUE",
        "outputId": "b3cfa5ba-1b53-4352-d972-2310d394cbab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([(0008, 0005) Specific Character Set              CS: 'ISO_IR 100', (0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'AXIAL', 'CT_SOM5 SPI'], (0008, 0016) SOP Class UID                       UI: CT Image Storage, (0008, 0018) SOP Instance UID                    UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.214140401149739061368142942055, (0008, 0020) Study Date                          DA: '20100227', (0008, 0021) Series Date                         DA: '20100227', (0008, 0022) Acquisition Date                    DA: '20100227', (0008, 0023) Content Date                        DA: '20100227', (0008, 0030) Study Time                          TM: '161937.171', (0008, 0031) Series Time                         TM: '162536.14', (0008, 0032) Acquisition Time                    TM: '162203.028699', (0008, 0033) Content Time                        TM: '162203.028699', (0008, 0050) Accession Number                    SH: '1598252606449858', (0008, 0060) Modality                            CS: 'CT', (0008, 0070) Manufacturer                        LO: 'SIEMENS', (0008, 0090) Referring Physician's Name          PN: '', (0008, 1030) Study Description                   LO: 'CT CHEST W IV CONTRAST', (0008, 103e) Series Description                  LO: 'LUNG 3.0  B70f', (0008, 1090) Manufacturer's Model Name           LO: 'Sensation 16', <Sequence, length 1>, <Sequence, length 1>, <Sequence, length 1>, (0009, 0010) Private Creator                     LO: 'SIEMENS CT VA1 DUMMY', (0010, 0010) Patient's Name                      PN: 'C3N-00247', (0010, 0020) Patient ID                          LO: 'C3N-00247', (0010, 0030) Patient's Birth Date                DA: '', (0010, 0040) Patient's Sex                       CS: 'F', (0010, 1010) Patient's Age                       AS: '077Y', (0010, 2160) Ethnic Group                        SH: '8', (0012, 0050) Clinical Trial Time Point ID        LO: '97', (0012, 0051) Clinical Trial Time Point Descripti ST: 'Days offset from diagnosis', (0012, 0062) Patient Identity Removed            CS: 'YES', (0012, 0063) De-identification Method            LO: 'Per DICOM PS 3.15 AnnexE. Details in 0012,0064', <Sequence, length 8>, (0013, 0010) Private Creator                     LO: 'CTP', (0013, 1010) Private tag data                    LO: 'CPTAC-LSCC', (0013, 1013) Private tag data                    LO: '70852626', (0018, 0015) Body Part Examined                  CS: 'CHEST', (0018, 0050) Slice Thickness                     DS: '3.0', (0018, 0060) KVP                                 DS: '100.0', (0018, 0090) Data Collection Diameter            DS: '500.0', (0018, 1020) Software Versions                   LO: 'syngo CT 2014A', (0018, 1030) Protocol Name                       LO: 'CHEST_ROUTINE_SMALL', (0018, 1100) Reconstruction Diameter             DS: '289.0', (0018, 1110) Distance Source to Detector         DS: '1040.0', (0018, 1111) Distance Source to Patient          DS: '570.0', (0018, 1120) Gantry/Detector Tilt                DS: '0.0', (0018, 1130) Table Height                        DS: '137.0', (0018, 1140) Rotation Direction                  CS: 'CW', (0018, 1150) Exposure Time                       IS: '500', (0018, 1151) X-Ray Tube Current                  IS: '237', (0018, 1152) Exposure                            IS: '131', (0018, 1160) Filter Type                         SH: '0', (0018, 1170) Generator Power                     IS: '35', (0018, 1190) Focal Spot(s)                       DS: '1.2', (0018, 1200) Date of Last Calibration            DA: '20100227', (0018, 1201) Time of Last Calibration            TM: '061404', (0018, 1210) Convolution Kernel                  SH: 'B70f', (0018, 5100) Patient Position                    CS: 'HFS', (0018, 9306) Single Collimation Width            FD: 1.5, (0018, 9307) Total Collimation Width             FD: 24.0, (0018, 9309) Table Speed                         FD: 43.0, (0018, 9310) Table Feed per Rotation             FD: 21.6, (0018, 9311) Spiral Pitch Factor                 FD: 0.9, (0018, 9323) Exposure Modulation Type            CS: 'XYZ_EC', (0018, 9324) Estimated Dose Saving               FD: 29.4, (0018, 9345) CTDIvol                             FD: 5.924999999999999, (0019, 0010) Private Creator                     LO: 'SIEMENS CT VA0  COAD', (0019, 10b0) [Feed per Rotation]                 DS: '21.6', (0020, 000d) Study Instance UID                  UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.258626612405225511766549337110, (0020, 000e) Series Instance UID                 UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.242193282649561899185427104083, (0020, 0010) Study ID                            SH: '', (0020, 0011) Series Number                       IS: '2', (0020, 0012) Acquisition Number                  IS: '2', (0020, 0013) Instance Number                     IS: '75', (0020, 0032) Image Position (Patient)            DS: [-143.2177734375, -287.2177734375, -216], (0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 1, 0], (0020, 0052) Frame of Reference UID              UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.118309119521421997707953128381, (0020, 1040) Position Reference Indicator        LO: '', (0020, 1041) Slice Location                      DS: '-216.0', (0020, 4000) Image Comments                      LT: '\\nPOST CONTRAST', (0021, 0010) Private Creator                     LO: 'SIEMENS MED', (0021, 1011) [Target]                            DS: [1, -6], (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: [0.564453125, 0.564453125], (0028, 0100) Bits Allocated                      US: 16, (0028, 0101) Bits Stored                         US: 12, (0028, 0102) High Bit                            US: 11, (0028, 0103) Pixel Representation                US: 0, (0028, 0106) Smallest Image Pixel Value          US: 0, (0028, 0107) Largest Image Pixel Value           US: 4095, (0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED', (0028, 1050) Window Center                       DS: [-500, 40], (0028, 1051) Window Width                        DS: [1500, 400], (0028, 1052) Rescale Intercept                   DS: '-1024.0', (0028, 1053) Rescale Slope                       DS: '1.0', (0028, 1055) Window Center & Width Explanation   LO: ['WINDOW1', 'WINDOW2'], (0029, 0010) Private Creator                     LO: 'SIEMENS CSA HEADER', (0029, 0011) Private Creator                     LO: 'SIEMENS MEDCOM HEADER', (0029, 0012) Private Creator                     LO: 'MITRA PRESENTATION 1.0', (0031, 0010) Private Creator                     LO: 'AGFA PACS Archive Mirroring 1.0', (0032, 000a) Study Status ID                     CS: 'READ', (0032, 000c) Study Priority ID                   CS: 'LOW', (0032, 1030) Reason for Study                    LO: 'Malignant neoplasm of lower lobe, right bronchus or lung, RLL bi', (0032, 1060) Requested Procedure Description     LO: 'CT CHEST W IV CONTRAST', (0032, 4000) Study Comments                      LT: '', (0033, 0010) Private Creator                     LO: 'MITRA OBJECT UTF8 ATTRIBUTES 1.0', (0040, 0244) Performed Procedure Step Start Date DA: '20100227', (0040, 0245) Performed Procedure Step Start Time TM: '161937.171', (7fe0, 0010) Pixel Data                          OW: Array of 524288 elements])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **.elements() Method**\n",
        "Using .elements() yields the top-level elements of the Dataset. This method will be useful when you don't need the DICOM attributes in any of the Sequences that you might find in the DICOM files you're working with. Notice how the Sequences are represented when using the .elements() method in the output below."
      ],
      "metadata": {
        "id": "qmQnELFs1J84"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the top-level elements of the Dataset Class\n",
        "[*dcm_data.elements()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYvI7aoAlly5",
        "outputId": "388cc475-0250-4130-914c-e970990ad49b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0008, 0005) Specific Character Set              CS: 'ISO_IR 100',\n",
              " (0008, 0008) Image Type                          CS: ['ORIGINAL', 'PRIMARY', 'AXIAL', 'CT_SOM5 SPI'],\n",
              " (0008, 0016) SOP Class UID                       UI: CT Image Storage,\n",
              " (0008, 0018) SOP Instance UID                    UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.214140401149739061368142942055,\n",
              " (0008, 0020) Study Date                          DA: '20100227',\n",
              " (0008, 0021) Series Date                         DA: '20100227',\n",
              " (0008, 0022) Acquisition Date                    DA: '20100227',\n",
              " (0008, 0023) Content Date                        DA: '20100227',\n",
              " (0008, 0030) Study Time                          TM: '161937.171',\n",
              " (0008, 0031) Series Time                         TM: '162536.14',\n",
              " (0008, 0032) Acquisition Time                    TM: '162203.028699',\n",
              " (0008, 0033) Content Time                        TM: '162203.028699',\n",
              " (0008, 0050) Accession Number                    SH: '1598252606449858',\n",
              " (0008, 0060) Modality                            CS: 'CT',\n",
              " (0008, 0070) Manufacturer                        LO: 'SIEMENS',\n",
              " (0008, 0090) Referring Physician's Name          PN: '',\n",
              " (0008, 1030) Study Description                   LO: 'CT CHEST W IV CONTRAST',\n",
              " (0008, 103e) Series Description                  LO: 'LUNG 3.0  B70f',\n",
              " (0008, 1090) Manufacturer's Model Name           LO: 'Sensation 16',\n",
              " <Sequence, length 1>,\n",
              " <Sequence, length 1>,\n",
              " <Sequence, length 1>,\n",
              " (0009, 0010) Private Creator                     LO: 'SIEMENS CT VA1 DUMMY',\n",
              " (0010, 0010) Patient's Name                      PN: 'C3N-00247',\n",
              " (0010, 0020) Patient ID                          LO: 'C3N-00247',\n",
              " (0010, 0030) Patient's Birth Date                DA: '',\n",
              " (0010, 0040) Patient's Sex                       CS: 'F',\n",
              " (0010, 1010) Patient's Age                       AS: '077Y',\n",
              " (0010, 2160) Ethnic Group                        SH: '8',\n",
              " (0012, 0050) Clinical Trial Time Point ID        LO: '97',\n",
              " (0012, 0051) Clinical Trial Time Point Descripti ST: 'Days offset from diagnosis',\n",
              " (0012, 0062) Patient Identity Removed            CS: 'YES',\n",
              " (0012, 0063) De-identification Method            LO: 'Per DICOM PS 3.15 AnnexE. Details in 0012,0064',\n",
              " <Sequence, length 8>,\n",
              " (0013, 0010) Private Creator                     LO: 'CTP',\n",
              " (0013, 1010) Private tag data                    LO: 'CPTAC-LSCC',\n",
              " (0013, 1013) Private tag data                    LO: '70852626',\n",
              " (0018, 0015) Body Part Examined                  CS: 'CHEST',\n",
              " (0018, 0050) Slice Thickness                     DS: '3.0',\n",
              " (0018, 0060) KVP                                 DS: '100.0',\n",
              " (0018, 0090) Data Collection Diameter            DS: '500.0',\n",
              " (0018, 1020) Software Versions                   LO: 'syngo CT 2014A',\n",
              " (0018, 1030) Protocol Name                       LO: 'CHEST_ROUTINE_SMALL',\n",
              " (0018, 1100) Reconstruction Diameter             DS: '289.0',\n",
              " (0018, 1110) Distance Source to Detector         DS: '1040.0',\n",
              " (0018, 1111) Distance Source to Patient          DS: '570.0',\n",
              " (0018, 1120) Gantry/Detector Tilt                DS: '0.0',\n",
              " (0018, 1130) Table Height                        DS: '137.0',\n",
              " (0018, 1140) Rotation Direction                  CS: 'CW',\n",
              " (0018, 1150) Exposure Time                       IS: '500',\n",
              " (0018, 1151) X-Ray Tube Current                  IS: '237',\n",
              " (0018, 1152) Exposure                            IS: '131',\n",
              " (0018, 1160) Filter Type                         SH: '0',\n",
              " (0018, 1170) Generator Power                     IS: '35',\n",
              " (0018, 1190) Focal Spot(s)                       DS: '1.2',\n",
              " (0018, 1200) Date of Last Calibration            DA: '20100227',\n",
              " (0018, 1201) Time of Last Calibration            TM: '061404',\n",
              " (0018, 1210) Convolution Kernel                  SH: 'B70f',\n",
              " (0018, 5100) Patient Position                    CS: 'HFS',\n",
              " (0018, 9306) Single Collimation Width            FD: 1.5,\n",
              " (0018, 9307) Total Collimation Width             FD: 24.0,\n",
              " (0018, 9309) Table Speed                         FD: 43.0,\n",
              " (0018, 9310) Table Feed per Rotation             FD: 21.6,\n",
              " (0018, 9311) Spiral Pitch Factor                 FD: 0.9,\n",
              " (0018, 9323) Exposure Modulation Type            CS: 'XYZ_EC',\n",
              " (0018, 9324) Estimated Dose Saving               FD: 29.4,\n",
              " (0018, 9345) CTDIvol                             FD: 5.924999999999999,\n",
              " (0019, 0010) Private Creator                     LO: 'SIEMENS CT VA0  COAD',\n",
              " (0019, 10b0) [Feed per Rotation]                 DS: '21.6',\n",
              " (0020, 000d) Study Instance UID                  UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.258626612405225511766549337110,\n",
              " (0020, 000e) Series Instance UID                 UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.242193282649561899185427104083,\n",
              " (0020, 0010) Study ID                            SH: '',\n",
              " (0020, 0011) Series Number                       IS: '2',\n",
              " (0020, 0012) Acquisition Number                  IS: '2',\n",
              " (0020, 0013) Instance Number                     IS: '75',\n",
              " (0020, 0032) Image Position (Patient)            DS: [-143.2177734375, -287.2177734375, -216],\n",
              " (0020, 0037) Image Orientation (Patient)         DS: [1, 0, 0, 0, 1, 0],\n",
              " (0020, 0052) Frame of Reference UID              UI: 1.3.6.1.4.1.14519.5.2.1.7085.2626.118309119521421997707953128381,\n",
              " (0020, 1040) Position Reference Indicator        LO: '',\n",
              " (0020, 1041) Slice Location                      DS: '-216.0',\n",
              " (0020, 4000) Image Comments                      LT: '\\nPOST CONTRAST',\n",
              " (0021, 0010) Private Creator                     LO: 'SIEMENS MED',\n",
              " (0021, 1011) [Target]                            DS: [1, -6],\n",
              " (0028, 0002) Samples per Pixel                   US: 1,\n",
              " (0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2',\n",
              " (0028, 0010) Rows                                US: 512,\n",
              " (0028, 0011) Columns                             US: 512,\n",
              " (0028, 0030) Pixel Spacing                       DS: [0.564453125, 0.564453125],\n",
              " (0028, 0100) Bits Allocated                      US: 16,\n",
              " (0028, 0101) Bits Stored                         US: 12,\n",
              " (0028, 0102) High Bit                            US: 11,\n",
              " (0028, 0103) Pixel Representation                US: 0,\n",
              " (0028, 0106) Smallest Image Pixel Value          US: 0,\n",
              " (0028, 0107) Largest Image Pixel Value           US: 4095,\n",
              " (0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED',\n",
              " (0028, 1050) Window Center                       DS: [-500, 40],\n",
              " (0028, 1051) Window Width                        DS: [1500, 400],\n",
              " (0028, 1052) Rescale Intercept                   DS: '-1024.0',\n",
              " (0028, 1053) Rescale Slope                       DS: '1.0',\n",
              " (0028, 1055) Window Center & Width Explanation   LO: ['WINDOW1', 'WINDOW2'],\n",
              " (0029, 0010) Private Creator                     LO: 'SIEMENS CSA HEADER',\n",
              " (0029, 0011) Private Creator                     LO: 'SIEMENS MEDCOM HEADER',\n",
              " (0029, 0012) Private Creator                     LO: 'MITRA PRESENTATION 1.0',\n",
              " (0031, 0010) Private Creator                     LO: 'AGFA PACS Archive Mirroring 1.0',\n",
              " (0032, 000a) Study Status ID                     CS: 'READ',\n",
              " (0032, 000c) Study Priority ID                   CS: 'LOW',\n",
              " (0032, 1030) Reason for Study                    LO: 'Malignant neoplasm of lower lobe, right bronchus or lung, RLL bi',\n",
              " (0032, 1060) Requested Procedure Description     LO: 'CT CHEST W IV CONTRAST',\n",
              " (0032, 4000) Study Comments                      LT: '',\n",
              " (0033, 0010) Private Creator                     LO: 'MITRA OBJECT UTF8 ATTRIBUTES 1.0',\n",
              " (0040, 0244) Performed Procedure Step Start Date DA: '20100227',\n",
              " (0040, 0245) Performed Procedure Step Start Time TM: '161937.171',\n",
              " (7fe0, 0010) Pixel Data                          OW: Array of 524288 elements]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **.group_dataset() Method**\n",
        "As we've mentioned above, you might find many attributes that have the same first number. These tags, the ones with a common first tag, usually describe a common parameter. For example, the attributes with the common first tag (0x0010) usually refer to Patient-related attributes. Attributes with the first tag (0x0028) describe Image Pixel attributes. Sometimes it's helpful to see all the attributes that are related to a specific parameter. Using the method (.group_dataset) returns a Dataset containing only elements of a certain group."
      ],
      "metadata": {
        "id": "0vX01ZdT374_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the attributes related to 0x0028 tag, these are related to ImagePixel\n",
        "dcm_data.group_dataset(0x0028)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zOvSQD7YnvQf",
        "outputId": "f1ad0c5f-5908-466b-a211-46aef1b4f59c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0028, 0002) Samples per Pixel                   US: 1\n",
              "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
              "(0028, 0010) Rows                                US: 512\n",
              "(0028, 0011) Columns                             US: 512\n",
              "(0028, 0030) Pixel Spacing                       DS: [0.564453125, 0.564453125]\n",
              "(0028, 0100) Bits Allocated                      US: 16\n",
              "(0028, 0101) Bits Stored                         US: 12\n",
              "(0028, 0102) High Bit                            US: 11\n",
              "(0028, 0103) Pixel Representation                US: 0\n",
              "(0028, 0106) Smallest Image Pixel Value          US: 0\n",
              "(0028, 0107) Largest Image Pixel Value           US: 4095\n",
              "(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'\n",
              "(0028, 1050) Window Center                       DS: [-500, 40]\n",
              "(0028, 1051) Window Width                        DS: [1500, 400]\n",
              "(0028, 1052) Rescale Intercept                   DS: '-1024.0'\n",
              "(0028, 1053) Rescale Slope                       DS: '1.0'\n",
              "(0028, 1055) Window Center & Width Explanation   LO: ['WINDOW1', 'WINDOW2']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **.dir() Method**\n",
        "Return an alphabetical list of element keywords in the Dataset. This is a great choice to make the first insight into the metadata that you're dealing with."
      ],
      "metadata": {
        "id": "NCBvCa0U4Usl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# An alphabetical list of the element keywords in the DICOM DataSet.\n",
        "dcm_data.dir()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g95B1PobOf0j",
        "outputId": "4350e124-f973-473b-ff47-5b0ca6017fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AccessionNumber',\n",
              " 'AcquisitionDate',\n",
              " 'AcquisitionNumber',\n",
              " 'AcquisitionTime',\n",
              " 'BitsAllocated',\n",
              " 'BitsStored',\n",
              " 'BodyPartExamined',\n",
              " 'CTDIvol',\n",
              " 'ClinicalTrialTimePointDescription',\n",
              " 'ClinicalTrialTimePointID',\n",
              " 'Columns',\n",
              " 'ContentDate',\n",
              " 'ContentTime',\n",
              " 'ConvolutionKernel',\n",
              " 'DataCollectionDiameter',\n",
              " 'DateOfLastCalibration',\n",
              " 'DeidentificationMethod',\n",
              " 'DeidentificationMethodCodeSequence',\n",
              " 'DistanceSourceToDetector',\n",
              " 'DistanceSourceToPatient',\n",
              " 'EstimatedDoseSaving',\n",
              " 'EthnicGroup',\n",
              " 'Exposure',\n",
              " 'ExposureModulationType',\n",
              " 'ExposureTime',\n",
              " 'FilterType',\n",
              " 'FocalSpots',\n",
              " 'FrameOfReferenceUID',\n",
              " 'GantryDetectorTilt',\n",
              " 'GeneratorPower',\n",
              " 'HighBit',\n",
              " 'ImageComments',\n",
              " 'ImageOrientationPatient',\n",
              " 'ImagePositionPatient',\n",
              " 'ImageType',\n",
              " 'InstanceNumber',\n",
              " 'KVP',\n",
              " 'LargestImagePixelValue',\n",
              " 'LongitudinalTemporalInformationModified',\n",
              " 'Manufacturer',\n",
              " 'ManufacturerModelName',\n",
              " 'Modality',\n",
              " 'PatientAge',\n",
              " 'PatientBirthDate',\n",
              " 'PatientID',\n",
              " 'PatientIdentityRemoved',\n",
              " 'PatientName',\n",
              " 'PatientPosition',\n",
              " 'PatientSex',\n",
              " 'PerformedProcedureStepStartDate',\n",
              " 'PerformedProcedureStepStartTime',\n",
              " 'PhotometricInterpretation',\n",
              " 'PixelData',\n",
              " 'PixelRepresentation',\n",
              " 'PixelSpacing',\n",
              " 'PositionReferenceIndicator',\n",
              " 'ProtocolName',\n",
              " 'ReasonForStudy',\n",
              " 'ReconstructionDiameter',\n",
              " 'ReferencedImageSequence',\n",
              " 'ReferencedStudySequence',\n",
              " 'ReferringPhysicianName',\n",
              " 'RequestedProcedureDescription',\n",
              " 'RescaleIntercept',\n",
              " 'RescaleSlope',\n",
              " 'RotationDirection',\n",
              " 'Rows',\n",
              " 'SOPClassUID',\n",
              " 'SOPInstanceUID',\n",
              " 'SamplesPerPixel',\n",
              " 'SeriesDate',\n",
              " 'SeriesDescription',\n",
              " 'SeriesInstanceUID',\n",
              " 'SeriesNumber',\n",
              " 'SeriesTime',\n",
              " 'SingleCollimationWidth',\n",
              " 'SliceLocation',\n",
              " 'SliceThickness',\n",
              " 'SmallestImagePixelValue',\n",
              " 'SoftwareVersions',\n",
              " 'SourceImageSequence',\n",
              " 'SpecificCharacterSet',\n",
              " 'SpiralPitchFactor',\n",
              " 'StudyComments',\n",
              " 'StudyDate',\n",
              " 'StudyDescription',\n",
              " 'StudyID',\n",
              " 'StudyInstanceUID',\n",
              " 'StudyPriorityID',\n",
              " 'StudyStatusID',\n",
              " 'StudyTime',\n",
              " 'TableFeedPerRotation',\n",
              " 'TableHeight',\n",
              " 'TableSpeed',\n",
              " 'TimeOfLastCalibration',\n",
              " 'TotalCollimationWidth',\n",
              " 'WindowCenter',\n",
              " 'WindowCenterWidthExplanation',\n",
              " 'WindowWidth',\n",
              " 'XRayTubeCurrent']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A filter of a specific set of attributes can be implemented as an argument to the .dir() method."
      ],
      "metadata": {
        "id": "jyiOs45E3h1W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract all the attributes that have \"Pixel\" in its keywords\n",
        "dcm_data.dir('Pixel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8GPQLfM3xJ_",
        "outputId": "c9218b0b-b5ed-476a-9842-fed0211d861d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LargestImagePixelValue',\n",
              " 'PixelData',\n",
              " 'PixelRepresentation',\n",
              " 'PixelSpacing',\n",
              " 'SamplesPerPixel',\n",
              " 'SmallestImagePixelValue']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Attributes for PyDicom DataSet**\n",
        "There are some attributes that you can use with the DataSet class. But I will point out only one, the most important and commonly used attribute, pixel_array."
      ],
      "metadata": {
        "id": "lMtphwhyIMLE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **pixel_array Attribute**\n",
        "This attribute returns the pixel data as a numpy.ndarray"
      ],
      "metadata": {
        "id": "McxgVP4OK1qo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the image pixels\n",
        "dcm_data.pixel_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DYKYX6bLFBs",
        "outputId": "a3c0413f-7033-4491-c3bc-1cf66c2e3dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[356, 244, 201, ..., 190, 224,  76],\n",
              "       [309, 387, 370, ..., 122,  17,   6],\n",
              "       [334, 476, 486, ...,  29,  46,  47],\n",
              "       ...,\n",
              "       [ 98, 188, 186, ..., 156,  86, 125],\n",
              "       [ 66, 138, 139, ..., 221,  74,  81],\n",
              "       [168, 173, 100, ..., 188, 135, 147]], dtype=uint16)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Image representation\n",
        "im = dcm_data.pixel_array\n",
        "\n",
        "plt.imshow(im, cmap='gray')\n",
        "plt.axis('off')\n",
        "plt.title('Axial Slice of a Chest-CT')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "ICv0Oolkb950",
        "outputId": "afa14942-9091-491a-dcb5-0d5802ceb04a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **PyDicom DataElement**\n",
        "As we've mentioned above, a DataSet is a PyDicom class that contains a set of DICOM tags as keys and values as DICOM DataElements. DataElements are the values of the attributes or the metadata that you might find during reading DICOM files. It's useful to be able to access specific information that might interest you in your work or research without searching about it in such pretty large text-like data like above. You can access specific info inside a specific DataElement using attributes."
      ],
      "metadata": {
        "id": "PgchREQNfciQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Pydicom DataElements 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)"
      ],
      "metadata": {
        "id": "cgZkV8U1QAgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the element's keyword (if known) as str\n",
        "kwrds = dcm_data[0x0010, 0x0010].keyword\n",
        "\n",
        "# Return the DICOM dictionary name for the element as str\n",
        "name = dcm_data[0x0010, 0x0010].name\n",
        "\n",
        "# Return the element's Value Representation\n",
        "vr = dcm_data[0x0010, 0x0010].VR\n",
        "\n",
        "# Return the element's value.\n",
        "value = dcm_data[0x0010, 0x0010].value\n",
        "\n",
        "print(\"The element's Keyword              : \", kwrds)\n",
        "print(\"The element's name                 : \", name)\n",
        "print(\"The element's Value Representation : \", vr)\n",
        "print(\"The element's value                : \", value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODhPpvz_0UJd",
        "outputId": "48bcfdce-c097-43c1-a4cd-cea394e65f38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The element's Keyword              :  PatientName\n",
            "The element's name                 :  Patient's Name\n",
            "The element's Value Representation :  PN\n",
            "The element's value                :  C3N-00247\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **PyDicom Sequence**\n",
        "The DataElement inside a PyDicom DataSet could contain values that are strings, integers, lists, or even a Sequence of data. The sequence is derived from Python's list. But it can even be a DataSet. Let's see how to access specific parameters in a specific Sequence. Since there's bulky information in the file we're working with, let's make use of the method .dir(*filter). With a proper filter implemented as an argument, we can extract the names of the \"Sequence\"s."
      ],
      "metadata": {
        "id": "Igtt3zGXG0HY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract all the attributes that have \"Sequence\" in its keywords\n",
        "dcm_data.dir('Sequence')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCUoqGIXVFjr",
        "outputId": "8ecba3a8-71c4-4920-e1c7-5cddd3d7675d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DeidentificationMethodCodeSequence',\n",
              " 'ReferencedImageSequence',\n",
              " 'ReferencedStudySequence',\n",
              " 'SourceImageSequence']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try to work with De-identification Method Code Sequence."
      ],
      "metadata": {
        "id": "c0eWmPzUjSyl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Return a specific Sequence, \"DeidentificationMethodCodeSequence\"\n",
        "dcm_data.DeidentificationMethodCodeSequence"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTsJrqiRWdHK",
        "outputId": "a27ae7cb-5536-4c95-d998-34e07ffc731d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Sequence, length 8>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To print out all the elements in the Sequence, we can use the indices."
      ],
      "metadata": {
        "id": "FVWu-2Q-j6kc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the contents of a Sequence\n",
        "dcm_data.DeidentificationMethodCodeSequence[:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSvFzg7FXoMS",
        "outputId": "6b97f55c-fbc5-4ea6-a9de-923c703121d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0008, 0100) Code Value                          SH: '113100'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Basic Application Confidentiality Profile',\n",
              " (0008, 0100) Code Value                          SH: '113101'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Clean Pixel Data Option',\n",
              " (0008, 0100) Code Value                          SH: '113104'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Clean Structured Content Option',\n",
              " (0008, 0100) Code Value                          SH: '113105'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Clean Descriptors Option',\n",
              " (0008, 0100) Code Value                          SH: '113107'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Retain Longitudinal Temporal Information Modified Dates Option',\n",
              " (0008, 0100) Code Value                          SH: '113108'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Retain Patient Characteristics Option',\n",
              " (0008, 0100) Code Value                          SH: '113109'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Retain Device Identity Option',\n",
              " (0008, 0100) Code Value                          SH: '113111'\n",
              " (0008, 0102) Coding Scheme Designator            SH: 'DCM'\n",
              " (0008, 0104) Code Meaning                        LO: 'Retain Safe Private Option']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To access specific info inside the Sequence, we can use its index."
      ],
      "metadata": {
        "id": "kIQR9pGSkiew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Return the info of specific line in a Sequence\n",
        "dcm_data.DeidentificationMethodCodeSequence[0].CodeMeaning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "oGMMwyvikM5d",
        "outputId": "61627a03-bff4-4ace-da8f-e4f8e48ba487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Basic Application Confidentiality Profile'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    }
  ]
}