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new-featur
| Author | SHA1 | Date |
|---|---|---|
 Ananthi Somasundaram
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d761866278 | |
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Ananthi Somasundaram
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f1730cdf1d | |
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Ananthi Somasundaram
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b67340a42a | |
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Ananthi Somasundaram
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908eb43722 | |
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Ananthi Somasundaram
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4128fcceb5 |
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@ -381,7 +381,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -392,10 +392,7 @@
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"\n",
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"PET image. Processing...\n",
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"Static scan. Processing...\n",
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"Ideal Image Orientation (0020,0037). Processing...\n",
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"Head First Supine (HFS). Processing...\n",
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"PET slice z-coordinate decreases -> Flip in z-coordinates applied\n",
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"DICOM converted to NIfTI: C:\\Users\\vallezgard\\NIfTI\\data\\output\\s1_2400s_Phantom_EARL1.nii\n"
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"DICOM converted to NIfTI: D:\\MyFiles\\Documents\\Work\\DICOM_files\\Test\\s1_300s_Phantom.nii\n"
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]
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}
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],
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@ -423,6 +420,8 @@
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"\n",
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"(0028,1053) Slope\n",
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"\n",
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"(0054,0081) Number of slices per frame\n",
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"\n",
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"(0054,1000) Series Type of the scan\n",
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"\n",
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"(0054,0414) Patient Gantry Relationship Code Sequence, i.e. orientation of the patient within the gantry.(Empty if Unknown)\n",
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@ -436,10 +435,11 @@
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"!cls\n",
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"## Libraries\n",
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"import os\n",
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"import numpy as np\n",
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"import nibabel as nib\n",
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"import sys\n",
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"import pydicom\n",
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"import numpy as np\n",
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"import tkinter as tk\n",
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"import nibabel as nib\n",
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"from tkinter import filedialog\n",
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"\n",
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"## Input data\n",
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@ -452,104 +452,107 @@
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"input_dicom_dir = os.path.abspath(filedialog.askdirectory(title=\"Please select the input DICOM folder\"))\n",
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"\n",
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"root.deiconify() # Makes the window visible again\n",
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"root.withdraw()\n",
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"output_dir = os.path.abspath(filedialog.askdirectory(title=\"Please select a folder to save output NIfTI file(s)\"))\n",
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"root.destroy()\n",
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"root.destroy() # Destroys the root window along with all other tkinter widgets\n",
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"\n",
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"study_name = 'Phantom_EARL1'\n",
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"study_name = os.path.basename(input_dicom_dir) # The study name is the same as input DICOM directory name\n",
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"\n",
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"## Predefined variables\n",
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"ideal_Image_Orientation = ['1', '0', '0', '0', '1', '0']\n",
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"\n",
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"## Functions\n",
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"def read_dicom_files(input_dicom_dir):\n",
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" \n",
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" # Read the DICOM dataset from the input directory. \n",
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" # If the DICOM dataset is not stored in accordance with the DICOM Standard File Format, SET force to True.\n",
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" # Do not read files with .db or .xml suffixes (to avoid Thumbs.db and VinciDC0.xml files). \n",
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" # ADD more such files to avoid later as required!\n",
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" \n",
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" dicom_files = os.listdir(input_dicom_dir)\n",
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" \n",
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" try:\n",
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" ds_list = [pydicom.dcmread(os.path.join(input_dicom_dir, filename), force = False) \\\n",
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" for filename in dicom_files \\\n",
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" if filename.endswith(('.IMA','.dcm'))]\n",
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" dcm_dataset = [pydicom.dcmread(os.path.join(input_dicom_dir, filename), force = False) \\\n",
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" for filename in os.listdir(input_dicom_dir) \\\n",
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" if not filename.endswith(('.db','.xml'))]\n",
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" except:\n",
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" sys.exit(\"Not a valid DICOM files were found. Only .IMA and .dcm is implemented.\")\n",
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" sys.exit(\"Invalid DICOM file(s) was found.\")\n",
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" \n",
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" return ds_list\n",
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" return dcm_dataset\n",
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"\n",
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"def sort_dicom_files(ds_list): \n",
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" sorted_ds_list = ds_list.copy()\n",
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" sorted_ds_list.sort(key = lambda x: int(x.ImageIndex))\n",
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"def get_z_coordinates(dcm_dataset):\n",
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" \n",
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" return sorted_ds_list\n",
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" # Get the z_coordinates based on the following Required tags. \n",
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"\n",
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"def get_img_volume(sorted_ds_list):\n",
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" # Get the voxel intensity array of each slice and stack it in the z-direction\n",
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" # Info:\n",
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" # direction cosines: https://en.wikipedia.org/wiki/Direction_cosine\n",
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" # http://mathworld.wolfram.com/DirectionCosine.html\n",
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" # cross product: https://en.wikipedia.org/wiki/Cross_product\n",
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" # http://mathworld.wolfram.com/CrossProduct.html\n",
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" # dot product: https://en.wikipedia.org/wiki/Dot_product\n",
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" # http://mathworld.wolfram.com/DotProduct.html\n",
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" \n",
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" # Transpose the array to change the array order from row-major to column-major\n",
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" # ToDo : Preallocate variable\n",
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" img_volume_data = np.transpose(sorted_ds_list[0].pixel_array)\n",
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" nr_of_slices = dcm_dataset[0].NumberOfSlices\n",
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" \n",
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" for ds in sorted_ds_list[1:]:\n",
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" img_slice_data = ds.pixel_array\n",
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" img_slice_data_transposed = img_slice_data.T\n",
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" img_volume_data = np.dstack((img_volume_data, img_slice_data_transposed))\n",
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" # x and y direction cosines\n",
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" x_dir_cos = np.array(list(map(float,dcm_dataset[0].ImageOrientationPatient[:3])))\n",
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" y_dir_cos = np.array(list(map(float,dcm_dataset[0].ImageOrientationPatient[3:])))\n",
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" \n",
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" Image_Orientation = sorted_ds_list[0].ImageOrientationPatient # (0020,0037)\n",
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" # For vectors a and b, a × b vector is perpendicular to both a and b and thus normal to the plane containing them\n",
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" z_dir_cos = np.cross(x_dir_cos, y_dir_cos)\n",
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" \n",
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" Patient_Position = sorted_ds_list[0].PatientPosition # (0018,5100)\n",
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" image_position_list = [dcm_dataset[i].ImagePositionPatient._list \\\n",
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" for i in range(0, nr_of_slices)]\n",
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" \n",
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" patient_orientation_code = sorted_ds_list[0].PatientGantryRelationshipCodeSequence._list[0].CodeValue # (0054,0414)\n",
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" patient_orientation = sorted_ds_list[0].PatientGantryRelationshipCodeSequence._list[0].CodeMeaning \n",
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" \n",
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" \n",
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" Image_Position_0 = sorted_ds_list[0].ImagePositionPatient # (0020,0032)\n",
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" Image_Position_1 = sorted_ds_list[1].ImagePositionPatient # (0020,0032)\n",
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" \n",
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" # Check for the Patient Position: HFS or FFS, and reorder the slices in z-direction accordingly\n",
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" if str(Image_Orientation) == str(ideal_Image_Orientation):\n",
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" \n",
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" print(\"Ideal Image Orientation (0020,0037). Processing...\")\n",
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" \n",
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" if Patient_Position == \"HFS\" and patient_orientation_code == 'F-10470' and patient_orientation == 'headfirst':\n",
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" \n",
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" print(\"Head First Supine (HFS). Processing...\")\n",
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" \n",
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" if int(Image_Position_0[2]) > int(Image_Position_1[2]):\n",
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" \n",
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" print(\"PET slice z-coordinate decreases -> Flip in z-coordinates applied\")\n",
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" \n",
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" final_ds_list = sorted_ds_list.copy() \n",
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" final_ds_list.reverse()\n",
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" \n",
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" img_volume_data_final = np.transpose(final_ds_list[0].pixel_array)\n",
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" \n",
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" for ds in final_ds_list[1:]:\n",
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" img_slice_data_final = ds.pixel_array\n",
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" img_slice_data_final_transposed = img_slice_data_final.T\n",
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" img_volume_data_final = np.dstack((img_volume_data_final, img_slice_data_final_transposed))\n",
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" \n",
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" elif int(Image_Position_0[2]) < int(Image_Position_1[2]):\n",
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" \n",
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" print(\"PET slice z-coordinate increases. Please, check DICOM information.\")\n",
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" \n",
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" elif Patient_Position == \"FFS\" and patient_orientation_code == 'F-10480' and patient_orientation == 'feet-first': \n",
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" print(\"Feet First Supine (FFS). Processing...\")\n",
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" \n",
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" if int(Image_Position_0[2]) < int(Image_Position_1[2]): \n",
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" print(\"PET slice z-coordinate increases. Processing...\") \n",
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" img_volume_data_final = img_volume_data\n",
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" \n",
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" elif int(Image_Position_0[2]) > int(Image_Position_1[2]): \n",
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" print(\"PET slice z-coordinate decreases. Please, check DICOM information.\") \n",
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" \n",
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" else:\n",
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" sys.exit(\"Sorry, this orientation was not implemented yet.\")\n",
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" \n",
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" else:\n",
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" sys.exit(\"Please, check Image Orientation tag (0020,0037) and Patient Gantry Relationship tag (0054,0414)\")\n",
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" \n",
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" return img_volume_data_final\n",
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" # Converting the x,y,z coordinates from str to float\n",
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" image_position_patient = [[float(coordinate) \\\n",
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" for coordinate in image_position] \\\n",
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" for image_position in image_position_list]\n",
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"\n",
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"''''' No need to anonymize now the DICOM\n",
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"def get_header_data(dcm_set, output_filename): \n",
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" # Vector dot product or scalar inner product to get z coordinates\n",
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" z_coordinates = np.dot(image_position_patient, z_dir_cos)\n",
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" \n",
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" return z_coordinates, nr_of_slices\n",
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"\n",
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"def sort_dicom_files(dcm_dataset):\n",
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" \n",
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" # Sort the DICOM dataset based on the z_coordinates.\n",
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"\n",
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" z_coordinates, nr_of_slices = get_z_coordinates(dcm_dataset)\n",
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" sorted_dcm_dataset = [x for _,x in sorted(zip(z_coordinates, dcm_dataset))]\n",
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" \n",
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" return sorted_dcm_dataset, nr_of_slices\n",
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"\n",
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"def get_voxel_size_z(dcm_dataset):\n",
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" \n",
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" # The absolute difference between two consecutive z_coordinates gives the voxel size along the z-direction, in mm. \n",
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" \n",
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" z_coordinates, _ = get_z_coordinates(dcm_dataset)\n",
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" voxel_size_z = abs(z_coordinates[0] - z_coordinates[1])\n",
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" \n",
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" return voxel_size_z\n",
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"\n",
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"def get_img_volume(dcm_dataset): \n",
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" \n",
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" # 1. Get the voxel intensity array of each slice\n",
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" # 2. Transpose the array to account for the change in array order from row-major to column-major\n",
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" # 3. Stack it in the z-direction\n",
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" \n",
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" img_volume_array = np.transpose(dcm_dataset[0].pixel_array)\n",
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" \n",
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" for dcm_slice in dcm_dataset[1:]:\n",
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" img_slice_array = np.transpose(dcm_slice.pixel_array)\n",
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" img_volume_array = np.dstack((img_volume_array, img_slice_array))\n",
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" \n",
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" return img_volume_array\n",
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"\n",
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"def get_header_data(dcm_set, output_filename):\n",
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"\n",
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" # Dump the DICOM header into a text file with the same filename as NIFTI \n",
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" # Set anonymize_flag = \"Y\" to not add patient data tags in the text file\n",
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" \n",
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" anonymize_flag = \"Y\"\n",
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" \n",
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" patient_tags = ['PatientID', 'PatientName', 'PatientBirthDate']\n",
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" \n",
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" output_txt_filename = output_filename + \".txt\"\n",
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@ -559,107 +562,96 @@
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" if anonymize_flag == \"Y\":\n",
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" if header_tag not in [dcm_set[0].data_element(tag) for tag in patient_tags]:\n",
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" file.write(str(header_tag) + '\\n')\n",
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" file.close() \n",
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"'''''\n",
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" file.close() \n",
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"\n",
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"def convert_to_nifti(dcm_set, output_file, frame_number, scan_duration_in_sec):\n",
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"def convert_to_nifti(dcm_dataset, output_nifti_file):\n",
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" \n",
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" img_volume_data_final = get_img_volume(dcm_set)\n",
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" img_volume_array_in_LPS = get_img_volume(dcm_dataset)\n",
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" \n",
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" # From LPS in DICOM to RAS in NIfTI\n",
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" img_data = np.fliplr(img_volume_data_final)\n",
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" img_data_volume = np.flipud(img_data)\n",
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" #img_data_volume = np.rot90(img_volume_data_final, 2) # Alternative: 180 rotation instead of two flips\n",
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" img_volume_array_in_RAS = np.flipud(np.fliplr(img_volume_array_in_LPS))\n",
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" #img_volume_array_in_RAS = np.rot90(img_volume_array_in_LPS, 2) # Alternative: 180 rotation instead of two flips\n",
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" \n",
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" Pixel_Spacing = dcm_set[0].PixelSpacing\n",
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" pixel_spacing = dcm_dataset[0].PixelSpacing\n",
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" \n",
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" Slice_Thickness = dcm_set[0].SliceThickness\n",
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" voxel_size_z = get_voxel_size_z(dcm_dataset)\n",
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" \n",
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" voxel_size = np.array([float(Pixel_Spacing[0]), float(Pixel_Spacing[1]), float(Slice_Thickness)])\n",
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" voxel_size = np.array([float(pixel_spacing[0]), float(pixel_spacing[1]), voxel_size_z])\n",
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" \n",
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" slope = dcm_set[0].RescaleSlope # (0028,1053) \n",
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" intercept = dcm_set[0].RescaleIntercept # (0028,1052)\n",
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" slope = dcm_dataset[0].RescaleSlope # (0028,1053) \n",
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" intercept = dcm_dataset[0].RescaleIntercept # (0028,1052)\n",
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" \n",
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" # Change the datatype from int to float and apply slope and intercept to the array\n",
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" img_data_volume = img_data_volume.astype(float)\n",
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" img_data_volume_final = (img_data_volume * slope) + intercept\n",
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" img_volume_array = (img_volume_array_in_RAS.astype(float) * slope) + intercept\n",
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" \n",
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" # Origin of coordinates: centre of the image\n",
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" center = (voxel_size * img_data_volume.shape) / 2\n",
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" center = (voxel_size * img_volume_array.shape) / 2\n",
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" \n",
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" # Affine matrix\n",
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" apply_affine = np.diag([voxel_size[0], voxel_size[1], voxel_size[2], 1])\n",
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" apply_affine[:3,3] = np.array([-center[0], -center[1], -center[2]])\n",
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" \n",
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" nii_out = nib.Nifti1Image(img_data_volume_final, apply_affine)\n",
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" nii_out = nib.Nifti1Image(img_volume_array, apply_affine)\n",
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" \n",
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" # Adjust NIfTI header\n",
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" nii_out.header['qform_code'] = 1\n",
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" nii_out.header['sform_code'] = 2\n",
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"\n",
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" #nii_out.set_data_dtype(np.float32)\n",
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" #xyz_unit = 'mm'\n",
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" #nii_out.header.set_xyzt_units(xyz=xyz_unit)\n",
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" #nii_out.header.set_data_offset(352)\n",
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" #nii_out.header['extents'] = 16384 # Remove?\n",
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" #nii_out.header['regular'] = 'r' # Remove?\n",
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" #nii_out.header['intent_name'] = 0 # Remove?\n",
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" #nii_out.header['cal_max'] = np.max(img_data_volume_final) # Check if present in header if not specified\n",
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" #nii_out.header['cal_min'] = np.min(img_data_volume_final) # Check if present in header if not specified \n",
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" nii_out.set_data_dtype(np.float32) # Float32: slope=1 and intercept=0. Software does not have to apply manually. \n",
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" nii_out.header.set_xyzt_units(xyz='mm') # Set the voxel size unit manually\n",
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" nii_out.header['intent_code'] = 0 # None\n",
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" nii_out.header['intent_name'] = 'PET'\n",
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" nii_out.header['cal_max'] = np.max(img_volume_array) # For software to apply proper color scaling for visualization\n",
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" nii_out.header['cal_min'] = np.min(img_volume_array) \n",
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" \n",
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" # Save the NIfTI file\n",
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" nib.save(nii_out, output_file) \n",
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" nib.save(nii_out, output_nifti_file) \n",
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" \n",
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" print(\"DICOM converted to NIfTI: \",output_file)\n",
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" print(\"DICOM converted to NIfTI: \",output_nifti_file)\n",
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"\n",
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"## Read DICOM\n",
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"ds_list = read_dicom_files(input_dicom_dir) # Read the DICOM files from the directory \n",
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"sorted_ds_list = sort_dicom_files(ds_list) # Sort the DICOM files based on Image Index (0054,1330)\n",
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"dcm_dataset = read_dicom_files(input_dicom_dir) # Read the DICOM files from the directory \n",
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"sorted_dcm_dataset, nr_of_slices = sort_dicom_files(dcm_dataset) # Sort the DICOM files based on z-coordinates\n",
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"\n",
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"# Check DICOM modality\n",
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"if sorted_ds_list[0].Modality == 'PT' and sorted_ds_list[0].SOPClassUID == '1.2.840.10008.5.1.4.1.1.128': \n",
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"if sorted_dcm_dataset[0].Modality == 'PT' and sorted_dcm_dataset[0].SOPClassUID == '1.2.840.10008.5.1.4.1.1.128': \n",
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" print(\"PET image. Processing...\")\n",
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" \n",
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" # Number of slices per frame\n",
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" nr_of_slices = sorted_ds_list[0].NumberOfSlices\n",
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" \n",
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||||
" frame_number = 1\n",
|
||||
" \n",
|
||||
" nr_of_dcm_files = len(sorted_ds_list)\n",
|
||||
" \n",
|
||||
" scan_series_type = sorted_ds_list[0].SeriesType[0] # (0054,1000) \n",
|
||||
" scan_series_type = sorted_dcm_dataset[0].SeriesType[0] # (0054,1000) \n",
|
||||
" \n",
|
||||
" if scan_series_type == 'STATIC' or scan_series_type == 'WHOLE BODY': \n",
|
||||
" print(\"Static scan. Processing...\") \n",
|
||||
" scan_duration_in_msec = sorted_ds_list[0].ActualFrameDuration \n",
|
||||
" scan_duration_in_sec = int(scan_duration_in_msec / 1000) \n",
|
||||
" output_file = \"s\" + str(frame_number) + \"_\" + str(scan_duration_in_sec) + \"s_\" + study_name + \".nii\"\n",
|
||||
" output_file = os.path.join(output_dir, output_file)\n",
|
||||
" convert_to_nifti(sorted_ds_list, output_file, frame_number, scan_duration_in_msec) \n",
|
||||
" scan_duration_in_sec = int(sorted_dcm_dataset[0].ActualFrameDuration / 1000) \n",
|
||||
" output_filename = \"s\" + str(frame_number) + \"_\" + str(scan_duration_in_sec) + \"s_\" + study_name \n",
|
||||
" output_nifti_filename = output_filename + \".nii\"\n",
|
||||
" output_nifti_file = os.path.join(output_dir, output_nifti_filename)\n",
|
||||
" convert_to_nifti(sorted_dcm_dataset, output_nifti_file) \n",
|
||||
" get_header_data(sorted_dcm_dataset, output_filename) # Dump the DICOM header into text file\n",
|
||||
" \n",
|
||||
" elif scan_series_type == 'DYNAMIC': \n",
|
||||
" print(\"Dynamic scan. Processing...\") \n",
|
||||
" nr_of_time_frames = sorted_ds_list[0].NumberOfTimeSlices \n",
|
||||
" \n",
|
||||
" # split files in multiple of nr_of_slices. Each frame has \"nr_of_slices\" slices \n",
|
||||
" dcm_set_split_by_frames = [sorted_ds_list[x:x+nr_of_slices] for x in range(0, len(sorted_ds_list), nr_of_slices)]\n",
|
||||
"\n",
|
||||
" # Split DICOM dataset in multiple of number of slices per frame. \n",
|
||||
" dcm_dataset_split_by_frames = [sorted_dcm_dataset[x:x+nr_of_slices] \\\n",
|
||||
" for x in range(0, len(sorted_dcm_dataset), nr_of_slices)]\n",
|
||||
" \n",
|
||||
" for dcm_set in dcm_set_split_by_frames: \n",
|
||||
" scan_duration_in_msec = dcm_set[0].ActualFrameDuration \n",
|
||||
" scan_duration_in_sec = int(scan_duration_in_msec / 1000) \n",
|
||||
" output_file = \"s\" + str(frame_number) + \"_\" + str(scan_duration_in_sec) + \"s_\" + study_name + \".nii\"\n",
|
||||
" output_file = os.path.join(output_dir, output_file)\n",
|
||||
" convert_to_nifti(dcm_set, output_file, frame_number, scan_duration_in_sec) \n",
|
||||
" for dcm_dataset in dcm_dataset_split_by_frames:\n",
|
||||
" scan_duration_in_sec = int(dcm_dataset[0].ActualFrameDuration / 1000)\n",
|
||||
" output_nifti_filename = output_filename + \".nii\"\n",
|
||||
" output_nifti_file = os.path.join(output_dir, output_nifti_filename)\n",
|
||||
" convert_to_nifti(sorted_dcm_dataset, output_nifti_file) \n",
|
||||
" get_header_data(sorted_dcm_dataset, output_filename) # Dump the DICOM header into text file\n",
|
||||
" frame_number += 1 \n",
|
||||
" \n",
|
||||
" elif scan_series_type == 'GATED':\n",
|
||||
" sys.exit(\"GATED scans are not supported yet\")\n",
|
||||
" \n",
|
||||
" else:\n",
|
||||
" sys.exit(\"Please, check Scan Series Type (0054,1000)\")\n",
|
||||
" sys.exit(\"Please check Scan Series Type (0054,1000)\")\n",
|
||||
" \n",
|
||||
"else: \n",
|
||||
" sys.exit(\"Please, check Image Modality\")\n"
|
||||
" sys.exit(\"Please check Image Modality\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -701,7 +693,7 @@
|
|||
"shape_Clinical = (111,400,400)\n",
|
||||
"\n",
|
||||
"# Reconstruction protocol used\n",
|
||||
"recon_used = \"Clinical\" # Options: Clinical, EARL1 and EALR2\n",
|
||||
"recon_used = \"Clinical\" # Options: Clinical, EARL1 and EARL2\n",
|
||||
"\n",
|
||||
"## Example dataset\n",
|
||||
"cwd = os.getcwd()\n",
|
||||
|
|
@ -729,21 +721,22 @@
|
|||
"elif recon_used == 'EARL1' or recon_used == 'EARL2':\n",
|
||||
" reshaped_voi_data = np.reshape(voi_data, shape_EARL)\n",
|
||||
" voxel_size = voxel_size_EARL\n",
|
||||
" \n",
|
||||
"else: \n",
|
||||
" sys.exit('This reconstruction was not implemented yet')\n",
|
||||
"\n",
|
||||
"# Transpose the array to change the array order from row-major to column-major \n",
|
||||
"voi_swapped_axes = reshaped_voi_data.T # Also: np.swapaxes(reshaped_voi_data, 2, 0) #\n",
|
||||
"\n",
|
||||
"# RAS in NIfTI: From Posterior to Anterior\n",
|
||||
"flipped_voi = np.flipud(voi_swapped_axes)\n",
|
||||
"\n",
|
||||
"# Binary Mask of 0's and 1's\n",
|
||||
"voi_swapped_axes[voi_swapped_axes <= 50] = 0\n",
|
||||
"voi_swapped_axes[voi_swapped_axes > 50] = 1\n",
|
||||
"\n",
|
||||
"# RAS in NIfTI: From Posterior to Anterior\n",
|
||||
"flipped_voi = np.flipud(voi_swapped_axes)\n",
|
||||
"\n",
|
||||
"# Shape of image\n",
|
||||
"matrix_size = np.array(voi_swapped_axes.shape)\n",
|
||||
"matrix_size = np.array(flipped_voi.shape)\n",
|
||||
"\n",
|
||||
"# Centre of image\n",
|
||||
"center = (voxel_size * matrix_size) / 2\n",
|
||||
|
|
@ -755,21 +748,19 @@
|
|||
"voi_nifti_out = nib.Nifti1Image(flipped_voi, apply_affine)\n",
|
||||
"\n",
|
||||
"# Set header\n",
|
||||
"voi_nifti_out.set_data_dtype(np.uint8)\n",
|
||||
"voi_nifti_out.set_data_dtype(np.int8)\n",
|
||||
"voi_nifti_out.header['qform_code'] = 1\n",
|
||||
"voi_nifti_out.header['sform_code'] = 2\n",
|
||||
"#xyz_unit = 'mm'\n",
|
||||
"#voi_nifti_out.header.set_xyzt_units(xyz=xyz_unit)\n",
|
||||
"#voi_nifti_out.header.set_data_offset(352)\n",
|
||||
"#voi_nifti_out.header['extents'] = 16384\n",
|
||||
"#voi_nifti_out.header['regular'] = 'r'\n",
|
||||
"#voi_nifti_out.header['intent_name'] = 0\n",
|
||||
"#voi_nifti_out.header['cal_max'] = np.max(voi_swapped_axes)\n",
|
||||
"#voi_nifti_out.header['cal_min'] = np.min(voi_swapped_axes)\n",
|
||||
"voi_nifti_out.header.set_xyzt_units(xyz='mm') # Set the voxel size unit manually\n",
|
||||
"voi_nifti_out.header.set_data_offset(352)\n",
|
||||
"voi_nifti_out.header['intent_code'] = 0 # None\n",
|
||||
"voi_nifti_out.header['intent_name'] = 'PET_VOI'\n",
|
||||
"voi_nifti_out.header['cal_max'] = np.max(flipped_voi) # For software to apply proper color scaling for visualization\n",
|
||||
"voi_nifti_out.header['cal_min'] = np.min(flipped_voi) \n",
|
||||
"\n",
|
||||
"# Save the NIfTI file\n",
|
||||
"nib.save( voi_nifti_out, os.path.join(output_dir, voi_nifti_out_filename) )\n",
|
||||
"print('Congratulations! ACCURATE VOI was saved as NIfTI file')\n",
|
||||
"print('ACCURATE VOI was successfully saved as NIfTI file')\n",
|
||||
"print(os.path.join(output_dir, voi_nifti_out_filename))\n"
|
||||
]
|
||||
},
|
||||
|
|
@ -1096,7 +1087,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
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Reference in New Issue