{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<div class='alert alert-info' style='text-align:center'><h1>Determining MR Image Planes</h1>\n- yet another MR processing notebook -</div>\n\n![planes.jpg](attachment:3010bc31-292e-4b19-a867-e2ebe2006009.jpg)\n\n#### - Calculate the plane (Axial, Coronal, Sagittal) of an MR image relative to the patient's body using the DICOM Image Orientation Patient tag.\n\n#### - It is important to compare co-planar images, i.e. Axial to Axial, Coronal to Coronal etc.\n\n- I noticed that the series aren't standard across studies with respect to the reconstruction planes.\n- That is, in some studies, the FLAIR sequence is in the Coronal plane and in the Sagittal plane in other studies etc.\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-07-14T16:28:30.086918Z","iopub.execute_input":"2021-07-14T16:28:30.087293Z","iopub.status.idle":"2021-07-14T16:28:30.092754Z","shell.execute_reply.started":"2021-07-14T16:28:30.087257Z","shell.execute_reply":"2021-07-14T16:28:30.09168Z"}},"attachments":{"910dc337-fd09-4886-a729-aed2aba7faf2.jpg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wgARCAF3AfQDAREAAhEBAxEB/8QAHAABAAIDAQEBAAAAAAAAAAAAAAUGAQMEAgcI/8QAGgEBAQADAQEAAAAAAAAAAAAAAAECAwQFBv/aAAwDAQACEAMQAAAB/VIAAAAAAAAAAAABqk9Hq3WxxWyPC7FyAAAAAAAAAAAAeTJkAAAAAAAAAAAAGDJ8x5vVlMtHLc4zHLrz1pbpt4Zi4FAAAAAAAAAAAAAAAAAwDIBgGQAAAAACnae3lmyu4dNs3cGiZ4ltW3h7rMgAAAAAGDIAAAAMAGQAAARJ8aLcWYs5RyZMGQAAAAAFxIrIOc6AAAAAAADJPAAAAAAAAAAGD54VwvpaiCPRNgAAAAFYPJaDIBGFfLmAAAAAAAV8sAAAAAAAAAAAAAIMyTYAAAAB8fOk+rGTyRJvI8sIAAAAAABXywAAAAAAAAAAAAAgzJNgAAAAEKVIph93IUkjrK8WEAAAAAAAr5YAAAAAAAAAAAAAQZkmwAU0wXMAAiStF0Ic7CTBXiwgAAAAAAFfLAAAAAAAAAAAAACDMk2ACPPmJ9eAPkZfiBLqQxNnGSIK8WEAAAAAAAr5YAAAAAAAAAAAAAQZkmwAfJDYSoB8xPpheCNJwwcJIgrxYQAAAAAACvlgAAAPJkyAAAAAAAAQZkmwAVokjuAPmx9COAmjJ4OIkAV4sIAAAAAABXywAAAAAAAAAAAAAgzJNgAAAHzguZ0nWDwcJIgrxYQAAAAAACvlgAAAAAAAAAAAABBmSaAAAKSUospcjqOoHg4iQBXywAAAAAAAEAT5kAAAAAAAAAAAAgzJNAAAHxQ7iuH2ElTpI04ziJQ7CKLCAAAAAAAQBPmQAAAAAAAAAAACDMk0AAADyfOyZNZ4OImzkOw2lPJ40EkDUWE6wAAACAJ8yAAAAAAAAAAAAQZkmgAADBoIAiCxECWMqx2k0dx81PoJ1A5DoPkR9HLAewAACAJ8yAAAAAAAAAAAAQZkmgAAcBWThJ07SLLWcpvPBTDcTp1mk1HKV8kCzFdLGdYAAIAnzIAAAAAAAAAAABBmSaABgjCEOw9E+QRxFqPJg5SKLAchynGdBvNB3EOdRxkiTAABXywGQAAAAAAAAAADBElaMkwADaajSWQr5JlVLaaCROYrxageCuHsshEkoejURpXyulyOoAwQhOAAAji1HQAAAAAAACPPJymSaAIs4yRPRFk6Vk6CUBtIAsBEHWQZZjBHm86DpNJBnGThyk2bACvlgMgA1ldIYjywHEQ5Yy4gAAAA0EUThBmSaBHFLLmSIIQ7CqljIssxqIU6SXKsWcycpVy4lfLEeThOsqJYzgLEZBAE+ZAMEAeCwnzsyfOC9k+XIAAAA8EGTp6IMyTRylTOw2EgRxtJohjqOw3kCShzHMdZIEWVMvRuOUjjuO80mCqHecZcgQBPmQCOIolzvBgwegAAAACDJU3gjDnJoph0kydJXS1HkwRpFE8bioFuNhXyaK4YLKbTJoIUsB5MA1nGVovRkgCfMg8ECYLCZAAAAAABEnGdgMg4TtIE2nedRwlgNJk9FGLgeysFrPRHFZLOdx7PR5K4WI9A0GSDJcqhOnSR5IAEeT5tMgAAAAAA4jkJYGQDBDG0jycIY7DoOorhYCnFlOI4iwEAbyfPZqPZg4ztI8jwYJ8waSokkWU2AycJGE6bADIAAAABrIUngAAYK6dREFgOg9nAeyCLMUssBxlbJYs51Hg9kOaTeS4MmCIOw6QQ5JFPO4twMFfMFjAAAAAAAK+T5kAAGCtnkmDUSAIw3kKTpTivliLmbSDME8QB0nUcxJnsA1kaSBpK+T588LOWoEIcRZj0DIAAAAAIg6zsBgyADBpKiWw9EaSpxA2lLKqfVCRPRyHMdhBnSTZoIMshg0Hg9Ho9EES54Iw1lmOIhSWJIAAAAACMGuyvy+08LmXZljlR6OdOVZQ+dkkWY5joOs4iGIUtpsJA1mDwQRYD0aTUchKldIMvx6PBqOU7TjOQ9kwcJgthkGQAAAADCa0+OcP0dj28XHbH47Pr3V4vtRghDJNHOfPC6kQTRsIkqx9LKoSpLmCungsp7NRqPR6IsjSyHkwU01nQTJ7OMmiOJI3FBKeeT7wZAAAABiNdxiJu043zctswnc9XpRghDJNGCuHIdJHnefOj68QhGlsIQwTx0mTgIA1HQbTcS4NZAlWLmRJaiLO0lyAJ8Hz03neWwyAAAADB5YjMoA9WjBCGSaBgjD54bzoOE+kEebjoK2W8HghTSWE0msyRpyHSZO0GQZO42mSAJ8yAAAAAAAAAAAAQZkmgAYIkrR4LobSOB3Gw1HghiVOchTvNh3kgZAAAABAE+ZAAAAAAAAAAAAIMyTQAABWTeSxwnKThykKT5CkGbC9HoAAAAAAEAT5kAAAAAAAAAAAAgzJNAAAGCpE6QRsO00E0aiKLEQJYES4rIAAAAAIAnzIAAAAAAAAAAABBmSaAAABDFfNx2HYdpAEeXwEAWFC4AAAAAAIAnzIAAAAAAAAAAABBmSaAAAAMFXOM6zYbCwHojT2SAMAAAAAAEAT5kAAAAAAAAAAAAgzJNAAAAAizqOoESSpqIkmooun0Ltu87bWVAAAAAEAT5kAAAAAAAAAAAAgzJNAAAAAjDpOoESSxXydiqYdlK5vT+v8AZ4GywoAAAAAgCwAAAAAAAAAAAAAgzJNgAAAAizpOsEQDrTnx2/Neb2fo3T40tlq9KAAAAABXywAAAAAAAAAFWIc5TpJQ4C2nGZJsAAAAEWdJ1ghzJIsfmfL7M7nz3XfwAAAAAAAV8sAAAAAAAAABUCmGSSNBvLERhZSbAAAABFnSdZggCfKrp7KZq9H6t1+B0MgAAAAAAK+WAAAAAAAAAAAGDIBxkWbjIAAABGHQdRDEmm0onP69g28E1s5/coAAAAAAwaSwAAAAAAAAAAAAAECRBJgAAAEadBKnURQMIXIAAAAAABBl+AAAAAAAAAAAAAMAwegYMgAAizWbSTMGQAAAAAAADB5PYAAAAAAAAAAAAPJ8ALSQBk3EmfRSYAAIw4SfPQAAAAAAB8XJE2kOdx1mgv5MgAAAAAAAAAAA8ECbDjOAmyJLkZAAMGD0AAAAAAACvkCajSeiUIU+lHsAAAAAA//EADMQAAEEAQIDBwMEAQUBAAAAAAQBAgMFABEUBhI1EBMVITAxQBYgNCIkJUEHIzI2QlAm/9oACAEBAAEFAvl64nZrmvYn/slWJY9uVxI0K1N4pePlnbljyhW1i63TiTxKsq3rJW/+wdRb9z+FoJDbaiKJMJoIiMl4bZPIPw1FC0WBBhv/ABLGAkgRlrchnLxq2OKluvF0xeIdOKTlmkM2BaZsS82JebEvNiXmxLzYl5sS82JebEvNiXmwLzYl5si82JebAvNgXmwLx8E8b9iXmxLzYl5sS82JebEvNiXmxLzYl5sS82JebEvNiXmxLzYl5UyyvZ8YrhVJn2HCc8baOmWpbj+tTdX9M/iAestK7iAe0sfsJL7lSh1jK9eo9/lP61N1f0/8kSMKrf8AHEw7eHOzXJiXLIMI0dLT8j16j3+U/rU3V/TSmDS2vhoaTOBgTieIFXTJJ3FSQQMGZlp+R69R7/Kf1qbq/wBl5xAtEXPxD/8ARfbZWMFUJWV05xqI1ic77Ba+JIgey0/I9eo9/lP61N1f7DwYLMP/ABbAz6S7fqO2lrCrgYOsra2ewLklbC3u3WC6aYAnKF2Wn5Hr1Hv8p/Wpur/Ze/5Hrqkqs40oAgPr+hz6/oc+vqHN/XeH1NXKfKROwaNkDyZNMdgH4XZafkevUe/yn9am6v8AZacPgXCBhIIL3bc7tud23L6GN3FpEu3gEgWTE7HYAuoXZafkevUe/wAp/Wpur+jef8utOmifjdjsAXmC7LT8j16j3+U/rU3V/vuvqLdcMcSX9vxDef8ALrTpov43Y7AH84XZafkevU+/yn9am6v9/wDkTi9tM3hHiPhmJnEHFEQ3ExT3y0gv42EWIweJdgOyG9BiZ4pDpEXFPlp+R69T7/Kf1qbq/o3XCEdtxHd2sA4os9lLFNAZ3IzRZGxKDY5Lw4MyWLfRLOFES2zJmpYUPsZ8kuzAid5YS488sdAuJgDooCIyE9Gp9/lP61N1f0JZmQsVJ7JJ44/D5CmB1otE0hxBKQ4Tw/LaTrFZA4IfGbG7yXiZHFwwuerNNU/SuPFilzu2xtNLnjuW3DWKjkd6FT7/ACn9am6v9xJkQjCrgpIqiI4pqNfa5YMSOqdE0iX2yOFsTtPPTVLlza50fiFlgdfEC1dNZHKs5ZzBWR7ydxVGU4sUwYRXRN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"}}},{"cell_type":"markdown","source":"### Import stuff and define some functions","metadata":{}},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport pydicom\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.645778Z","iopub.execute_input":"2021-09-01T23:27:27.64617Z","iopub.status.idle":"2021-09-01T23:27:27.651105Z","shell.execute_reply.started":"2021-09-01T23:27:27.646136Z","shell.execute_reply":"2021-09-01T23:27:27.650299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# specify the dataset and the series names\ndataset = 'test'\nseries_names = ['FLAIR','T1w','T1wCE','T2w']\ndirectory = '../input/rsna-miccai-brain-tumor-radiogenomic-classification'","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.656131Z","iopub.execute_input":"2021-09-01T23:27:27.656468Z","iopub.status.idle":"2021-09-01T23:27:27.66676Z","shell.execute_reply.started":"2021-09-01T23:27:27.656436Z","shell.execute_reply":"2021-09-01T23:27:27.665841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_series_list(path):\n    files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]\n    return files","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.668178Z","iopub.execute_input":"2021-09-01T23:27:27.668533Z","iopub.status.idle":"2021-09-01T23:27:27.679874Z","shell.execute_reply.started":"2021-09-01T23:27:27.668447Z","shell.execute_reply":"2021-09-01T23:27:27.678999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Find the image in the middle of each series stack, returns a list of four images\ndef get_middle_images(study_id):\n    \n    middle_images = []\n    \n    # Iterate through each of the four series directories and get the files \n    for ser in series_names:\n        series_files = get_series_list(f'{directory}/{dataset}/{study_id}/{ser}')\n        series_df = pd.DataFrame(columns = ['image','instance_number'])\n\n        # Get the DICOM InstanceNumber tag to order the images since we can't rely on the filenames to be in order\n        for s in series_files:\n            img = pydicom.dcmread(s)\n            series_df.loc[len(series_df.index)] = [s, img[0x0020,0x0013].value]\n \n        series_df['instance_number'] = pd.to_numeric(series_df['instance_number'])\n\n        # Sort the image list by InstanceNumber\n        series_df = series_df.sort_values(by=['instance_number'])\n        \n        # Find the image in the middle of the list\n        middle_index = int(series_df.shape[0] / 2)\n        middle_image = series_df.iloc[middle_index]['image']\n\n        middle_images.append(middle_image)\n\n    return middle_images","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.684941Z","iopub.execute_input":"2021-09-01T23:27:27.685291Z","iopub.status.idle":"2021-09-01T23:27:27.69407Z","shell.execute_reply.started":"2021-09-01T23:27:27.68526Z","shell.execute_reply":"2021-09-01T23:27:27.692972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Convert the Image Orientation Patient tag cosine values into a text string of the plane.\n# This represents the plane the image is 'closest to' .. it does not explain any obliqueness\ndef get_image_plane(loc):\n\n    row_x = round(loc[0])\n    row_y = round(loc[1])\n    row_z = round(loc[2])\n    col_x = round(loc[3])\n    col_y = round(loc[4])\n    col_z = round(loc[5])\n\n    if row_x == 1 and row_y == 0 and col_x == 0 and col_y == 0:\n        return \"Coronal\"\n\n    if row_x == 0 and row_y == 1 and col_x == 0 and col_y == 0:\n        return \"Sagittal\"\n\n    if row_x == 1 and row_y == 0 and col_x == 0 and col_y == 1:\n        return \"Axial\"\n\n    return \"Unknown\"","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.703639Z","iopub.execute_input":"2021-09-01T23:27:27.704336Z","iopub.status.idle":"2021-09-01T23:27:27.71228Z","shell.execute_reply.started":"2021-09-01T23:27:27.704291Z","shell.execute_reply":"2021-09-01T23:27:27.710925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Display images and their plane\ndef plot_images(images):\n    for img in images:\n        fig = plt.figure(figsize=(5, 5))\n        image = pydicom.dcmread(img)\n        ser = img.split(\"/\")\n        image_orientation_patient = image[0x0020,0x0037]\n        plane = get_image_plane(image_orientation_patient)\n        plt.title(ser[-2] + \" - \" + plane)\n        plt.imshow(image.pixel_array, cmap='gray')","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.723178Z","iopub.execute_input":"2021-09-01T23:27:27.72367Z","iopub.status.idle":"2021-09-01T23:27:27.72917Z","shell.execute_reply.started":"2021-09-01T23:27:27.723638Z","shell.execute_reply":"2021-09-01T23:27:27.728232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Load four images from a study\n- Grab the 'middle' image of each sequence\n- Get the plane of each image/sequence","metadata":{}},{"cell_type":"code","source":"# ID / Directory name of the study we want to get\nstudy_id = '00037'\nplot_images(get_middle_images(study_id))","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:27:27.741211Z","iopub.execute_input":"2021-09-01T23:27:27.741691Z","iopub.status.idle":"2021-09-01T23:27:33.279713Z","shell.execute_reply.started":"2021-09-01T23:27:27.74166Z","shell.execute_reply":"2021-09-01T23:27:33.278754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### We can see this study has the following planes for each sequence:\n\n- FLAIR = Coronal\n- T1w = Axial\n- T1wCE = Coronal\n- T2w = Sagittal\n\n#### Let's get another study to compare","metadata":{}},{"cell_type":"code","source":"study_id = '00079'\nplot_images(get_middle_images(study_id))","metadata":{"execution":{"iopub.status.busy":"2021-09-01T23:29:03.924551Z","iopub.execute_input":"2021-09-01T23:29:03.924925Z","iopub.status.idle":"2021-09-01T23:29:14.552723Z","shell.execute_reply.started":"2021-09-01T23:29:03.924893Z","shell.execute_reply":"2021-09-01T23:29:14.551657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Compare the planes between the two studies\n\n#### Second Study:\n- FLAIR = Sagittal\n- T1w = Axial\n- T1wCE = Sagittal\n- T2w = Sagittal\n\n#### First study:\n- FLAIR = Coronal\n- T1w = Axial\n- T1wCE = Coronal\n- T2w = Sagittal\n\n### As you can see, the planes are different, so they can't be directly compared to each other.\n##### * Also, notice the last image in the second study is only 256x256 .. while all the others are 512x512 .. this means not all the series in this dataset are the same size.\n\n#### Some of my other MR notebooks\n- Tumor Object Detection -> https://www.kaggle.com/davidbroberts/brain-tumor-object-detection\n- Determining MR Slice Orientation -> https://www.kaggle.com/davidbroberts/determining-mr-slice-orientation\n- Determining DICOM image order -> https://www.kaggle.com/davidbroberts/determining-dicom-image-order\n- Manual VOI LUT on MR images -> https://www.kaggle.com/davidbroberts/manual-voi-lut-on-mr-images\n- Reference Lines on MR images -> https://www.kaggle.com/davidbroberts/mr-reference-lines\n- Export DICOM Images by Plane -> https://www.kaggle.com/davidbroberts/export-dicom-series-by-plane/","metadata":{}}]}