def saveDicomFile(filename,
patientName,
patientId,
gender,
birthday,
imageArray,
transpose=False):
meta = Dataset()
SOPClassUID = "1.2.840.10008.5.1.4.1.1.2" # sop class UID dla obrazow CT
meta.MediaStorageSOPClassUID = SOPClassUID # Wygenerowany unikalny UID
date = datetime.datetime.now().strftime('%Y%m%d') # Obecny czas
time = datetime.datetime.now().strftime('%H%M%S.%f') # Obecny czas
randomUId = SOPClassUID + "." + date + time # Wygenerowany unikalny UID
meta.MediaStorageSOPInstanceUID = randomUId # Wygenerowany unikalny UID
meta.ImplementationClassUID = randomUId + "." + "1" # Wygenerowany unikalny UID
dataSet = FileDataset(filename, {}, file_meta=meta,
preamble=b"\0" * 128) # Utworzenie obiektu DICOM
dataSet.PatientName = patientName # Imie pacjenta
dataSet.PatientID = patientId # Id pacjenta
dataSet.PatientBirthDate = birthday # Data urodzenia pacjenta
dataSet.PatientSex = gender # Plec pacjenta
dataSet.is_little_endian = True
dataSet.is_implicit_VR = True
dataSet.ContentDate = date # Czas utworzenia pliku (YYYY:MM:DD)
dataSet.StudyDate = date # Czas ostatniego otworzenia obrazu (YYYY-MM-DD)
dataSet.StudyTime = time # Czas ostatniego otworzenia obrazu (HH:MM:SS)
dataSet.ContentTime = time # Czas utworzenia pliku (HH:MM:SS)
dataSet.StudyInstanceUID = randomUId + "." + "2" # Wygenerowany unikalny UID
dataSet.SeriesInstanceUID = randomUId + "." + "3" # Wygenerowany unikalny UID
dataSet.SOPInstanceUID = randomUId + "." + "4" # Wygenerowany unikalny UID
dataSet.SOPClassUID = "CT." + date + time # Wygenerowany unikalny UID
dataSet.SamplesPerPixel = 1 # Liczba kanałów. 1 - dla skali szarosci
dataSet.PhotometricInterpretation = "MONOCHROME2" # MONOCHROE - obraz jest w skali szarości, 2 - maksymalna wartosc wskazuje kolor bialy
dataSet.PixelRepresentation = 0 # 0 - wartosci sa tylko dodatnie (unsigned) 1 - wartosci sa tez ujemne
dataSet.HighBit = 15 # Najważniejszy bit w pliku z obrazem
dataSet.BitsStored = 16 # Liczba bitow na jedna wartosc w obrazie
dataSet.BitsAllocated = 16 # Liczba bitow na jedna wartosc ktora jest zaalokowana dla obrazu
dataSet.SmallestImagePixelValue = b'\\x00\\x00' # Wskazanie minimalnej wartosci dla kanalu
dataSet.LargestImagePixelValue = b'\\xff\\xff' # Wskazanie maksymalnej wartosci dla kanalu
dataSet.Rows = imageArray.shape[1] # Liczba wierszy
dataSet.Columns = imageArray.shape[0] # Liczba kolumn
if imageArray.dtype != np.uint16: # Sprawdzenie czy wartosci sa w przedziale [0,255]
imageArray = skimage.img_as_uint(
imageArray) # Zamiana na wartosci w przedziale [0,255]
if transpose == True: # Zamiana wierszy i kolumn (opcjonalne)
dataSet.Rows = imageArray.shape[0]
dataSet.Columns = imageArray.shape[1]
dataSet.PixelData = imageArray.tostring() # Zapisanie obrazu
dataSet.save_as(filename) # Zapisanie pliku na dysku
def write_dicom(pixel_array, filename):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble="\0" * 128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
def create_dicom_base(self):
if _dicom_loaded is False:
raise ModuleNotLoadedError("Dicom")
if self.header_set is False:
raise InputError("Header not loaded")
meta = Dataset()
meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage
meta.MediaStorageSOPInstanceUID = "1.2.3"
meta.ImplementationClassUID = "1.2.3.4"
meta.TransferSyntaxUID = UID.ImplicitVRLittleEndian # Implicit VR Little Endian - Default Transfer Syntax
ds = FileDataset("file", {}, file_meta=meta, preamble=b"\0" * 128)
ds.PatientsName = self.patient_name
ds.PatientID = "123456"
ds.PatientsSex = '0'
ds.PatientsBirthDate = '19010101'
ds.SpecificCharacterSet = 'ISO_IR 100'
ds.AccessionNumber = ''
ds.is_little_endian = True
ds.is_implicit_VR = True
ds.SOPClassUID = '1.2.3' # !!!!!!!!
ds.SOPInstanceUID = '1.2.3' # !!!!!!!!!!
ds.StudyInstanceUID = '1.2.3' # !!!!!!!!!!
ds.FrameofReferenceUID = '1.2.3' # !!!!!!!!!
ds.StudyDate = '19010101' # !!!!!!!
ds.StudyTime = '000000' # !!!!!!!!!!
ds.PhotometricInterpretation = 'MONOCHROME2'
ds.SamplesPerPixel = 1
ds.ImageOrientationPatient = ['1', '0', '0', '0', '1', '0']
ds.Rows = self.dimx
ds.Columns = self.dimy
ds.SliceThickness = str(self.slice_distance)
ds.PixelSpacing = [self.pixel_size, self.pixel_size]
return ds
def WriteDCMFile(pixel_array,filename):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'RT Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
ds.Modality = 'RTIMAGE'
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'RT Image Storage'
ds.SecondaryCaptureDeviceManufacturer = 'Varian Medical Systems'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.ImagePlanePixelSpacing=[0.392,0.392]
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
# ds.SmallestImagePixelValue = '\\x00\\x00'
# ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns =1024# pixel_array.shape[0]
ds.Rows =764# pixel_array.shape[1]
ds.RescaleSlope=1.0
ds.RescaleIntercept=1.0
# if type(pixel_array) != np.uint16:
# pixel_array =np.uint16(pixel_array)
ds.PixelData = pixel_array
ds.save_as(filename,write_like_original=True)
return
def write_dicom(pixel_array, index, filename):
## This code block was taken from the output of a MATLAB secondary
## capture. I do not know what the long dotted UIDs mean, but
## this code works.
target_dir = '/'.join(filename.split('/')[:-1])
print(target_dir)
mkdir_p(target_dir)
file_meta = Dataset()
ds = FileDataset(filename, {},file_meta = file_meta)
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.InstanceNumber = index
#ds.PhotometricInterpretation = bytes("MONOCHROME2", "UTF-8")
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
#ds.SmallestImagePixelValue = bytes('\\x00\\x00', 'UTF-8')
#ds.LargestImagePixelValue = bytes('\\xff\\xff', 'UTF-8')
ds.Columns = pixel_array.shape[1]
ds.Rows = pixel_array.shape[0]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
def write_dicom(pixel_array, filename):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684'
file_meta.MediaStorageSOPInstanceUID += '411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble=b"\0"*128)
ds.Modality = 'MR'
ds.ContentDate = str(date.today()).replace('-', '')
ds.ContentTime = str(time())
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.1243139774123601752342712'
ds.StudyInstanceUID += '87472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421'
ds.SeriesInstanceUID += '859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385'
ds.SOPInstanceUID += '720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 3.3.5'
# Options
ds.InstitutionName = "Imperial College London"
ds.RepetitionTime = 300
# These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.Columns = pixel_array.shape[1]
ds.Rows = pixel_array.shape[0]
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return 0
def WriteDICOM_slice(self, pixel_array,filename, itemnumber=0, PhotometricInterpretation="MONOCHROME2"):
from dicom.dataset import Dataset, FileDataset
import numpy as np
import datetime, time
"""
INPUTS:
pixel_array: 2D numpy ndarray. If pixel_array is larger than 2D, errors.
filename: string name for the output file.
"""
## This code block was taken from the output of a MATLAB secondary
## capture. I do not know what the long dotted UIDs mean, but
## this code works.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
if PhotometricInterpretation=="MONOCHROME2":
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
elif PhotometricInterpretation=="RGB":
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
pixel_array = pixel_array[0]
print pixel_array.shape
ds.Columns = pixel_array.shape[0]
ds.ItemNumber = str(itemnumber)
ds.InstanceNumber = str(itemnumber)
ds.SliceLocation = str(itemnumber)
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return filename
def write_dicom_mask(img_slice,
ds_slice,
slice_no,
window,
level,
outputdirectory,
mask_suffix,
filepattern=".dcm"):
series_number = ds_slice[0x0020, 0x0011].value
base_fname = str(slice_no).zfill(6)
filename = outputdirectory + os.path.sep + base_fname + "_" + str(
series_number) + mask_suffix + filepattern
ds = FileDataset(filename,
ds_slice,
file_meta=ds_slice.file_meta,
preamble=b"\0" * 128)
ds.SOPInstanceUID = ds_slice.SOPInstanceUID
ds.SOPClassUID = "1.2.840.10008.5.1.4.1.1.2"
ds.StudyID = "123"
ds.PatientName = "Liver^Larry^H"
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = False
# These are the necessary imaging components of the FileDataset object.
(rows, cols) = img_slice.shape
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0 # 0 for mask, 1=MIS AWL image
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.Columns = cols
ds.Rows = rows
ds.PixelData = img_slice.tobytes()
image_type_val = ds_slice[0x0008, 0x0008].value
image_type_val_str = "\\".join(str(x) for x in image_type_val)
image_type_val_str2 = image_type_val_str.replace("ORIGINAL", "DERIVED", 1)
ds.ImageType = image_type_val_str2
# display components
ds.WindowCenter = [0] # 0028,1050 Window Center
ds.WindowWidth = [1] # 0028,1051 Window Width
ds.RescaleIntercept = 0 # 0028,1052 Rescale Intercept: 0
ds.RescaleSlope = 1 # 0028,1053 Rescale Slope: 1
ds.save_as(filename)
def write_dicom(pixel_array, filename):
"""
INPUTS:
pixel_array: 2D or 3D numpy ndarray.
filename: string name for the output file.
"""
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.1'
file_meta.MediaStorageSOPInstanceUID = '1.2'
file_meta.ImplementationClassUID = '1.3'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble=b'\x00' * 128)
ds.Modality = 'PET' # imaging modality name
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) # milliseconds since the epoch
ds.StudyInstanceUID = '1.3'
ds.SeriesInstanceUID = '1.3'
ds.SOPInstanceUID = '1.3'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 3.7.5'
# Patient-related data
ds.PatientName = 'Wawrzyniec L. Dobrucki'
ds.PatientID = 'M12345'
ds.PatientSex = 'M'
ds.PatientBirthDate = '12/12/12'
ds.SeriesNumber = 5
ds.SeriesDescription = 'test image'
ds.SliceThickness = '0.5' # slice thickness in mm
ds.PixelSpacing = '0.5' # pixel spacing or size in mm
# These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0 # unsigned (default)
ds.HighBit = 15
ds.BitsStored = 16 # default
ds.BitsAllocated = 16 # default
ds.NumberOfFrames = 216 # number of frames for 3D files
# ds.SmallestImagePixelValue = '\\x00\\x00'
# ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return
def writeDicom(ods, mrn, studyID, outdir):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '0.0'
file_meta.ImplementationClassUID = '0.0'
ds = FileDataset(studyID, {}, file_meta=file_meta, preamble="\0" * 128)
ds.Modality = ods.Modality if "Modality" in ods else ""
ds.StudyDate = ods.StudyDate if "StudyDate" in ods else ""
ds.StudyTime = ods.StudyTime if "StudyTime" in ods else ""
ds.StudyInstanceUID = '0.0'
ds.SeriesInstanceUID = '0.0'
ds.SOPInstanceUID = '0.0'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.AccessionNumber = str(studyID)
ds.PatientID = str(mrn)
ds.StudyID = str(studyID)
ds.PatientName = str(studyID)
ds.PatientBirthDate = "00000000"
ds.PatientAge = calculateAge(
ods.StudyDate, ods.PatientBirthDate
) if "StudyDate" in ods and "PatientBirthDate" in ods else ""
ds.PatientSex = ods.PatientSex if "PatientSex" in ods else ""
ds.StudyDescription = ods.StudyDescription if "StudyDescription" in ods else ""
ds.SeriesDescription = ods.SeriesDescription if "SeriesDescription" in ods else ""
ds.ViewPosition = ods.ViewPosition if "ViewPosition" in ods else ""
ds.InstanceNumber = ods.InstanceNumber if "InstanceNumber" in ods else ""
ds.SeriesNumber = ods.SeriesNumber if "SeriesNumber" in ods else ""
ds.SamplesPerPixel = ods.SamplesPerPixel if "SamplesPerPixel" in ods else ""
ds.PhotometricInterpretation = ods.PhotometricInterpretation if "PhotometricInterpretation" in ods else ""
ds.PixelRepresentation = ods.PixelRepresentation if "PixelRepresentation" in ods else ""
ds.HighBit = ods.HighBit if "HighBit" in ods else ""
ds.BitsStored = ods.BitsStored if "BitsStored" in ods else ""
ds.BitsAllocated = ods.BitsAllocated if "BitsAllocated" in ods else ""
ds.Columns = ods.Columns if "Columns" in ods else ""
ds.Rows = ods.Rows if "Rows" in ods else ""
ds.PixelData = ods.PixelData if "PixelData" in ods else ""
filename = cleanString(
str(studyID) + "_" + str(ds.SeriesNumber) + "_" +
str(ds.InstanceNumber) + "_" + str(ds.Modality) + "_" +
str(ds.StudyDescription) + "_" + str(ds.SeriesDescription) + "_" +
str(ds.ViewPosition) + ".dcm")
outpath = os.path.join(outdir, filename)
ds.save_as(outpath)
return
def writeDicom(pixel_array, filename):
"""
Source: https://codedump.io/share/qCDN4fOKcTAS/1/create-pydicom-file-from-numpy-array
INPUTS:
pixel_array: 2D numpy ndarray. If pixel_array is larger than 2D, errors.
filename: string name for the output file.
"""
## This code block was taken from the output of a MATLAB secondary
## capture. I do not know what the long dotted UIDs mean, but
## this code works.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble=b'\x00' * 128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return
def write_dicom(pixel_array,filename):
"""
INPUTS:
pixel_array: 2D numpy ndarray. If pixel_array is larger than 2D, errors.
filename: string name for the output file.
"""
## Metadata is captured from Osirix
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.4'
file_meta.MediaStorageSOPInstanceUID = '1.2.276.0.7230010.3.1.4.680961.1356.1477424584.260802'
file_meta.ImplementationClassUID = '1.2.276.0.7230010.3.0.3.6.0'
ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
ds.Modality = 'MR'
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.2.840.113619.2.40.20410.4.1101731106.1478904046.976759'
ds.SeriesInstanceUID = '1.2.840.113619.2.40.20410.4.1101731106.1478904045.846068'
ds.SOPInstanceUID = '1.2.840.113619.2.40.20410.4.1101731106.1478904123.170427'
ds.SOPClassUID = '1.2.840.10008.5.1.4.1.1.4'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
ds.SliceThickness = 2.4
ds.SpacingBetweenSlices = 2.4
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 1
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return
def write_dicom(pixel_array,index,filename):
"""
INPUTS:
pixel_array: 2D numpy ndarray. If pixel_array is larger than 2D, errors.
filename: string name for the output file.
"""
## This code block was taken from the output of a MATLAB secondary
## capture. I do not know what the long dotted UIDs mean, but
## this code works.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.2.3.4.5.6.7.8.9.00'+str(index)
file_meta.ImplementationClassUID = '1.2.3.4.5.6.7.8.9.0'
ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.2.3.4.5.6.7.8.9'
ds.SeriesInstanceUID = '1.2.3.4.5.6.7.8.9.00'
ds.SOPInstanceUID = file_meta.MediaStorageSOPInstanceUID
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3 by Linkingmed'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return
def convert(self,imagefile,dicomfile,ox=0,oy=0,oz=0,xv=[1,0,0],yv=[0,1,0]):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.7' #
file_meta.MediaStorageSOPInstanceUID = dicom.UID.generate_uid()
file_meta.ImplementationClassUID = file_meta.MediaStorageSOPInstanceUID
# Create the FileDataset instance (initially no data elements, but file_meta supplied)
ds = FileDataset(dicomfile, {}, file_meta=file_meta, preamble=b"\0" * 128)
img = Image.open(imagefile)
# Add the data elements -- not trying to set all required here. Check DICOM standard
ds.PatientName = str(imagefile)
val = np.random.randint(1,99999)
ds.PatientID = '%05d' % (val)
ds.SOPInstanceUID = dicom.UID.generate_uid()
ds.add_new(Tag(0x20, 0x32), 'DS', [ox,oy,oz])
ds.add_new(Tag(0x20, 0x37),'DS', xv+yv)
ds.Columns = img.size[0] #img width
ds.Rows = img.size[1] #img height
#This is specific to the type of jpeg encoding
#These parameters were obtained from the dicom created by using img2dcm tool of dcmtk
#They seem to work for PIL loaded files
if img.mode == 'L':
ds.SamplesPerPixel=1
ds.PhotometricInterpretation= 'MONOCHROME2' # Should match img.mode
elif img.mode == 'RGB':
ds.SamplesPerPixel=3
ds.PhotometricInterpretation= 'RGB' # Should match img.mode
ds.PixelSpacing = [1,1,1]
ds.BitsAllocated= 8
ds.BitsStored=8
ds.HighBit=7
ds.PixelRepresentation=0
ds.LossyImageCompression='01'
ds.LossyImageCompressionMethod= 'ISO_10918_1'
#ds.TransferSyntaxIndex = '1.2.840.10008.1.2'
ds.PixelData = np.array(img).tostring()
ds.save_as(dicomfile)
def fileOutput(infilename, PLOT=False):
noiseAmp = 150 #units are "HA". See file in slack.
cellLocations = read_CA_output(infilename, noiseAmp)
outfilename = datetime.now().strftime("%y-%m-%d-%H%M.dcm")
# Populate required values for file meta information
file_meta = Dataset()
file_meta.FileMetaInformationGroupLength = 190
file_meta.FileMetaInformationVersion = '\x00\x01'
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.2.115051905812620145920373971792360190180'
file_meta.TransferSyntaxUID = '1.2.840.10008.1.2'
file_meta.ImplementationClassUID = '1.2.40.0.13.1.1'
file_meta.ImplementationVersionName = 'dcm4che-2.0'
ds = FileDataset(outfilename, {}, file_meta=file_meta, preamble="\0" * 128)
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
#Set the data that we copied from another dicom file
setAttributes(ds)
# Set the image's data equal to that from our CA output
ds.Rows = cellLocations.shape[0]
ds.Columns = cellLocations.shape[1]
if cellLocations.dtype != np.uint16:
ds.PixelData = cellLocations.astype(np.uint16).tostring()
else:
ds.PixelData = cellLocations.tostring()
ds.SmallestImagePixelValue = struct.pack(
"<i", nmin
) #'\\x00\\x00': struct.pack("<H",0), w/ little_endian=True https://docs.python.org/2/library/struct.html#byte-order-size-and-alignment
ds.LargestImagePixelValue = struct.pack(
"<i", nmax) #'\\xff\\xff': struct.pack("<H",65535)
ds.save_as(outfilename)
if (PLOT):
plt.imshow(ds.pixel_array, cmap=plt.cm.bone)
plt.show()
def _write_numpy_2_dicom(pixel_array, dcmfilename):
filename = dcmfilename[dcmfilename.index('-') +
1:dcmfilename.index('.mhd')]
print(filename)
for i in range(pixel_array.shape[0]):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage
file_meta.MediaStorageSOPInstanceUID = "1.2.3" # !! Need valid UID here for real work
file_meta.ImplementationClassUID = "1.2.3.4" # !!! Need valid UIDs here
ds = FileDataset(dcmfilename, {},
file_meta=file_meta,
preamble="\0" * 128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) # milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[1]
ds.Rows = pixel_array.shape[2]
ds.PixelData = pixel_array[i].tostring()
dcm = "{0}{1}-{2}.dcm".format(output_path, filename, i)
print("dcm filename = %s,shape = %s" % (dcm, pixel_array[0].shape))
ds.save_as(dcm)
def create_dicom_base(self):
if _dicom_loaded is False:
raise ModuleNotLoadedError("Dicom")
if self.header_set is False:
raise InputError("Header not loaded")
# TODO tags + code datatypes are described here:
# https://www.dabsoft.ch/dicom/6/6/#(0020,0012)
# datatype codes are described here:
# ftp://dicom.nema.org/medical/DICOM/2013/output/chtml/part05/sect_6.2.html
meta = Dataset()
meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage
# Media Storage SOP Instance UID tag 0x0002,0x0003 (type UI - Unique Identifier)
meta.MediaStorageSOPInstanceUID = self._ct_sop_instance_uid
meta.ImplementationClassUID = "1.2.3.4"
meta.TransferSyntaxUID = UID.ImplicitVRLittleEndian # Implicit VR Little Endian - Default Transfer Syntax
ds = FileDataset("file", {}, file_meta=meta, preamble=b"\0" * 128)
ds.PatientsName = self.patient_name
if self.patient_id in (None, ''):
ds.PatientID = datetime.datetime.today().strftime('%Y%m%d-%H%M%S')
else:
ds.PatientID = self.patient_id # Patient ID tag 0x0010,0x0020 (type LO - Long String)
ds.PatientsSex = '' # Patient's Sex tag 0x0010,0x0040 (type CS - Code String)
# Enumerated Values: M = male F = female O = other.
ds.PatientsBirthDate = '19010101'
ds.SpecificCharacterSet = 'ISO_IR 100'
ds.AccessionNumber = ''
ds.is_little_endian = True
ds.is_implicit_VR = True
ds.SOPClassUID = '1.2.3' # !!!!!!!!
# SOP Instance UID tag 0x0008,0x0018 (type UI - Unique Identifier)
ds.SOPInstanceUID = self._ct_sop_instance_uid
# Study Instance UID tag 0x0020,0x000D (type UI - Unique Identifier)
# self._dicom_study_instance_uid may be either set in __init__ when creating new object
# or set when import a DICOM file
# Study Instance UID for structures is the same as Study Instance UID for CTs
ds.StudyInstanceUID = self._dicom_study_instance_uid
# Series Instance UID tag 0x0020,0x000E (type UI - Unique Identifier)
# self._ct_dicom_series_instance_uid may be either set in __init__ when creating new object
# or set when import a DICOM file
# Series Instance UID for structures might be different than Series Instance UID for CTs
ds.SeriesInstanceUID = self._ct_dicom_series_instance_uid
# Study Instance UID tag 0x0020,0x000D (type UI - Unique Identifier)
ds.FrameofReferenceUID = '1.2.3' # !!!!!!!!!
ds.StudyDate = datetime.datetime.today().strftime('%Y%m%d')
ds.StudyTime = datetime.datetime.today().strftime('%H%M%S')
ds.PhotometricInterpretation = 'MONOCHROME2'
ds.SamplesPerPixel = 1
ds.ImageOrientationPatient = ['1', '0', '0', '0', '1', '0']
ds.Rows = self.dimx
ds.Columns = self.dimy
ds.SliceThickness = str(self.slice_distance)
ds.PixelSpacing = [self.pixel_size, self.pixel_size]
# Add eclipse friendly IDs
ds.StudyID = '1' # Study ID tag 0x0020,0x0010 (type SH - Short String)
ds.ReferringPhysiciansName = 'py^trip' # Referring Physician's Name tag 0x0008,0x0090 (type PN - Person Name)
ds.PositionReferenceIndicator = '' # Position Reference Indicator tag 0x0020,0x1040
ds.SeriesNumber = '1' # SeriesNumber tag 0x0020,0x0011 (type IS - Integer String)
return ds
def WriteDICOM_slice(self,
pixel_array,
filename,
itemnumber=0,
PhotometricInterpretation="MONOCHROME2"):
from dicom.dataset import Dataset, FileDataset
import numpy as np
import datetime, time
"""
INPUTS:
pixel_array: 2D numpy ndarray. If pixel_array is larger than 2D, errors.
filename: string name for the output file.
"""
## This code block was taken from the output of a MATLAB secondary
## capture. I do not know what the long dotted UIDs mean, but
## this code works.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {},
file_meta=file_meta,
preamble="\0" * 128)
ds.Modality = 'WSD'
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
if PhotometricInterpretation == "MONOCHROME2":
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
elif PhotometricInterpretation == "RGB":
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
pixel_array = pixel_array[0]
print pixel_array.shape
ds.Columns = pixel_array.shape[0]
ds.ItemNumber = str(itemnumber)
ds.InstanceNumber = str(itemnumber)
ds.SliceLocation = str(itemnumber)
ds.Rows = pixel_array.shape[1]
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return filename
def writeDicomFiles(self, filename):
for i in range(
1, len(self.PA)
): #Image 0 is a null image for default display and will not be written out
fileName = filename + str(i) + ".dcm"
# print ("printing dicom file " + fileName)
# Populate required values for file meta information
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
# Create the FileDataset instance (initially no data elements, but file_meta supplied)
ds = FileDataset(fileName, {},
file_meta=file_meta,
preamble="\0" * 128)
ds.Modality = 'MR'
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'MR Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
#Add MRI data elements
ds.bValue = self.bValue[i]
ds.Columns = self.Columns[i]
ds.FoVX = self.FoVX[i]
ds.FoVY = self.FoVY[i]
ds.ImageOrientationPatient = self.ImagePosition[i].tolist()
ds.InstitutionName = self.InstitutionName[i]
ds.PixelBandwidth = self.PixelBandwidth[i]
ds.PixelSpacing = [self.PixelSpacingY[i], self.PixelSpacingX[i]]
ds.Rows = self.Rows[i]
ds.PatientName = self.PatientName[i]
ds.PatientID = "123456"
ds.ProtocolName = self.ProtocolName[i]
ds.RepetitionTime = self.TR[i]
ds.SeriesDescription = self.SeriesDescription[i]
ds.EchoTime = self.TE[i]
ds.FlipAngle = self.FA[i]
ds.InversionTime = self.TI[i]
ds.ImageOrientationPatient[3:6] = self.ColumnDirection[i].tolist()
ds.ImageOrientationPatient[:3] = self.RowDirection[i].tolist()
ds.MagneticFieldStrength = self.MagneticFieldStrength[i]
ds.Manufacturer = self.Manufacturer[i]
pixel_array = np.transpose(self.PA[i]) #numpy pixel array
if self.DataType[i].lower() == "phase":
pixel_array = (
pixel_array + np.pi
) * 10000 # phase data -pi to pi is converted to 0 to 10000pi
#print "Adjusting phase to 16 bit integer"
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring() # image byte data
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
ds.save_as(fileName)
def copyCTtoRTDose(self, path, ds, doseData, imageRow, imageCol, sliceCount, dgs):
# Create a RTDose file for broadcasting.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.2' # CT Image Storage
# Needs valid UID
file_meta.MediaStorageSOPInstanceUID = ds.file_meta.MediaStorageSOPInstanceUID
file_meta.ImplementationClassUID = ds.file_meta.ImplementationClassUID
#create DICOM RT-Dose object.
rtdose = FileDataset(path + '/rtdose.dcm', {}, file_meta=file_meta, preamble="\0"*128)
#No DICOM object standard. Use only required to avoid errors with viewers.
rtdose.SOPInstanceUID = ds.SOPInstanceUID
rtdose.SOPClassUID = '1.2.840.10008.5.1.4.1.1.481.2'
rtdose.file_meta.TransferSyntaxUID = ds.file_meta.TransferSyntaxUID
rtdose.PatientsName = ds.PatientsName
rtdose.PatientID = ds.PatientID
rtdose.PatientsBirthDate = ds.PatientsBirthDate
#Crashed caused if no sex.
try:
rtdose.PatientsSex = ds.PatientsSex
except:
rtdose.PatientsSex, ds.PatientsSex = 'O', 'O'
self.data.update({'images':ds})
rtdose.StudyDate = ds.StudyDate
rtdose.StudyTime = ds.StudyTime
rtdose.StudyInstanceUID = ds.StudyInstanceUID
rtdose.SeriesInstanceUID = ds.SeriesInstanceUID
rtdose.StudyID = ds.StudyID
rtdose.SeriesNumber = ds.SeriesNumber
rtdose.Modality = 'RTDOSE'
rtdose.ImagePositionPatient = ds.ImagePositionPatient
rtdose.ImageOrientationPatient = ds.ImageOrientationPatient
rtdose.FrameofReferenceUID = ds.FrameofReferenceUID
rtdose.PositionReferenceIndicator = ds.PositionReferenceIndicator
rtdose.PixelSpacing = ds.PixelSpacing
rtdose.SamplesperPixel = 1
rtdose.PhotometricInterpretation = 'MONOCHROME2'
rtdose.NumberofFrames = sliceCount
rtdose.Rows = imageRow
rtdose.Columns = imageCol
rtdose.BitsAllocated = 32
rtdose.BitsStored = 32
rtdose.HighBit = 31
rtdose.PixelRepresentation = 0
rtdose.DoseUnits = 'GY'
rtdose.DoseType = 'PHYSICAL'
rtdose.DoseSummationType = 'FRACTION'
#In case spaceing tag is missing.
if not ds.has_key('SpacingBetweenSlices'):
ds.SpacingBetweenSlices = ds.SliceThickness
#Ensure patient pos is on "Last slice".
if fnmatch.fnmatch(ds.PatientPosition, 'FF*'):
#For compliance with dicompyler update r57d9155cc415 which uses a reverssed slice ordering.
doseData = doseData[::-1]
rtdose.ImagePositionPatient[2] = ds.ImagePositionPatient[2]
elif fnmatch.fnmatch(ds.PatientPosition, 'HF*'):
rtdose.ImagePositionPatient[2] = ds.ImagePositionPatient[2] - (sliceCount-1)*ds.SpacingBetweenSlices
#Create Type A(Relative) GFOV.
rtdose.GridFrameOffsetVector = list(np.arange(0., sliceCount*ds.SpacingBetweenSlices,ds.SpacingBetweenSlices))
#Scaling from int to physical dose
rtdose.DoseGridScaling = dgs
#Store images in pixel_array(int) & Pixel Data(raw).
rtdose.pixel_array = doseData
rtdose.PixelData = doseData.tostring()
#Tag required by dicompyler.
plan_meta = Dataset()
rtdose.ReferencedRTPlans = []
rtdose.ReferencedRTPlans.append([])
rtdose.ReferencedRTPlans[0] = plan_meta
rtdose.ReferencedRTPlans[0].ReferencedSOPClassUID = 'RT Plan Storage'
rtdose.ReferencedRTPlans[0].ReferencedSOPInstanceUID = ds.SOPInstanceUID
#Create RTPlan to acticate DVH
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.5' # RT Plan Storage
file_meta.MediaStorageSOPInstanceUID = ds.file_meta.MediaStorageSOPInstanceUID
file_meta.ImplementationClassUID = ds.file_meta.ImplementationClassUID
#create DICOM RT-Plan object.
rtPlan = FileDataset(path + '/rtplan.dcm', {}, file_meta=file_meta, preamble="\0"*128)
#No DICOM object standard. Use only required to avoid errora with viewers.
rtPlan.SOPInstanceUID = ds.SOPInstanceUID
rtPlan.SOPClassUID = '1.2.840.10008.5.1.4.1.1.481.5'
rtPlan.ReferencedSOPInstanceUID = ds.SOPInstanceUID #Need to change
rtPlan.file_meta.TransferSyntaxUID = ds.file_meta.TransferSyntaxUID
rtPlan.PatientsName = ds.PatientsName
rtPlan.PatientID = ds.PatientID
rtPlan.PatientsSex = ds.PatientsSex
rtPlan.PatientsBirthDate = ds.PatientsBirthDate
rtPlan.RTPlanLabel = 'Simulation'
rtPlan.RTPlanDate = ds.StudyDate
rtPlan.RTPlanTime = ds.StudyTime
rtPlan.Modality = 'RTPLAN'
rtPlan.StudyInstanceUID = ds.StudyInstanceUID
rtPlan.SeriesInstanceUID = ds.SeriesInstanceUID
rtPlan.StudyID = ds.StudyID
rtPlan.SeriesNumber = ds.SeriesNumber
return rtdose, rtPlan
def write_dicom(pixel_array, filename, ds_ori,
series_number, sop_id, nb_acq,
project, subject, session):
"""Write a dicom from a pixel_array (numpy).
:param pixel_array: 2D numpy ndarray.
If pixel_array is larger than 2D, errors.
:param filename: string name for the output file.
:param ds_ori: original pydicom object of the pixel_array
:param series_number: number of the series being processed
:param sop_id: SOPInstanceUID for the DICOM
:param nb_acq: acquisition number
:param project: project name on XNAT
:param subject: subject name on XNAT
:param session: session name on XNAT
"""
# Set the DICOM dataset
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = ds_ori.SOPInstanceUID
file_meta.ImplementationClassUID = ds_ori.file_meta.ImplementationClassUID
ds = FileDataset(filename, {}, file_meta=file_meta, preamble="\0"*128)
# Copy UID from dicom
ds.InstanceCreatorUID = ds_ori.InstanceCreatorUID
ds.SOPClassUID = ds_ori.SOPClassUID
# ds.ReferencedStudySequence = ds_ori.ReferencedStudySequence
ds.StudyInstanceUID = ds_ori.StudyInstanceUID
ds.SeriesInstanceUID = ds_ori.SeriesInstanceUID
# Other tags to set
ds.SeriesNumber = series_number
sop_uid = sop_id + str(datetime.datetime.now()).replace('-', '')\
.replace(':', '')\
.replace('.', '')\
.replace(' ', '')
ds.SOPInstanceUID = sop_uid[:-1]
ds.ProtocolName = 'ADC Map %d' % nb_acq
ds.SeriesDescription = 'ADC Map %d' % nb_acq
ds.PatientName = subject
ds.PatientID = session
# Set SeriesDate/ContentDate
now = datetime.date.today()
ds.SeriesDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.ContentDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.Modality = 'MR'
ds.StudyDescription = project
ds.AcquisitionNumber = 1
ds.SamplesperPixel = 1
ds.PhotometricInterpretation = 'MONOCHROME2'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.11'
ds.NumberOfFrames = pixel_array.shape[2]
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 8
ds.BitsAllocated = 8
ds.SmallestImagePixelValue = pixel_array.min()
ds.LargestImagePixelValue = pixel_array.max()
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
# Organise the array:
pixel_array2 = np.zeros((pixel_array.shape[0]*pixel_array.shape[2],
pixel_array.shape[1]))
for i in range(pixel_array.shape[2]):
pixel_array2[pixel_array.shape[0]*i:pixel_array.shape[0]*(i+1),
:] = pixel_array[:, :, i]
# Set the Image pixel array
if pixel_array2.dtype != np.uint8:
pixel_array2 = pixel_array2.astype(np.uint8)
ds.PixelData = pixel_array2.tostring()
# Save the image
ds.save_as(filename)
ds.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.66.4' # Segmentation SOP Class
ds.MediaStorageSOPInstanceUID = "1.2.840.10008.5.1.4.1.1.66.4" # !! Need valid UID here for real work
ds.ImplementationClassUID = "1.2.840.10008.5.1.4.1.1.66.4" # !!! Need valid UIDs here
# Required for Segmentation IOD
ds.Modality = 'SEG'
ds.SeriesNumber = '001'
ds.ImageType = '1' # Derived = 1, Primary = 2
#Set image parameters
ds.SpacingBetweenSlices = '2.0'
ds.ImageOrientationPatient = ['-1', '0', '0', '0', '-1', '0']
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = 'MONOCHROME2'
ds.Rows = 109
ds.Columns = 91
ds.PixelSpacing = ['-2.0', '2.0']
ds.BitsAllocated = 8 # Binary = 1
ds.BitsStored = 8 # Binary = 1
ds.HighBit = 7 # Binary = 0
ds.SegmentationType = 'FRACTIONAL' #FRACTIONAL or BINARY
ds.SegmentationFractionalType = 'OCCUPANCY' #Can be OCCUPANCY or PROBABILITY
ds.MaximumFractionalValue = 116 # 116 is the number of labels in AAL
ds.PixelRepresentation = 1
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
print "Writing test file", filename
def write_dicom(pixel_array, filename, ds_ori,
series_number, sop_id, series_description):
"""Write a dicom from a pixel_array (numpy).
:param pixel_array: 2D numpy ndarray.
If pixel_array is larger than 2D, errors.
:param filename: string name for the output file.
:param ds_ori: original pydicom object of the pixel_array
:param series_number: number of the series being processed
:param sop_id: SOPInstanceUID for the DICOM
:param series_description: series description for Osirix display
"""
# Set the DICOM dataset
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = ds_ori.SOPInstanceUID
file_meta.ImplementationClassUID = ds_ori.SOPClassUID
ds = FileDataset(filename, {}, file_meta=file_meta, preamble="\0"*128)
# Copy the tag from the original DICOM
for tag, d_obj in ds_ori.items():
if tag != ds_ori.data_element("PixelData").tag:
ds[tag] = d_obj
# Other tags to set
ds.SeriesNumber = series_number
sop_uid = sop_id + str(datetime.datetime.now()).replace('-', '')\
.replace(':', '')\
.replace('.', '')\
.replace(' ', '')
ds.SOPInstanceUID = sop_uid[:-1]
ds.ProtocolName = '%s Verdict MAP' % series_description
# Set SeriesDate/ContentDate
now = datetime.date.today()
ds.SeriesDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.ContentDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.Modality = 'MR'
ds.ConversionType = 'WSD'
ds.StudyDescription = 'INNOVATE'
ds.SeriesDescription = series_description
ds.AcquisitionNumber = 1
ds.SamplesperPixel = 1
ds.PhotometricInterpretation = 'MONOCHROME2'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
nb_frames = pixel_array.shape[2]*pixel_array.shape[3]
ds.NumberOfFrames = nb_frames
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 8
ds.BitsAllocated = 8
ds.SmallestImagePixelValue = pixel_array.min()
ds.LargestImagePixelValue = pixel_array.max()
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
# Fixing the sequence if the number of frames was less than the original
# it happens if we remove the last volume for phillips data (mean)
if ds_ori.NumberOfFrames > nb_frames:
new_seq = Sequence()
for i in xrange(0, ds_ori.NumberOfFrames):
if i % 5 == 0: # take one slice for each (14)
new_seq.append(ds_ori[0x5200, 0x9230][i])
ds[0x5200, 0x9230].value = new_seq
# Organise the array:
pixel_array2 = np.zeros((pixel_array.shape[0]*pixel_array.shape[2],
pixel_array.shape[1]))
for i in range(pixel_array.shape[2]):
pixel_array2[pixel_array.shape[0]*i:pixel_array.shape[0]*(i+1),
:] = pixel_array[:, :, i, 0]
# Set the Image pixel array
if pixel_array2.dtype != np.uint8:
pixel_array2 = pixel_array2.astype(np.uint8)
ds.PixelData = pixel_array2.tostring()
# Save the image
ds.save_as(filename)
def changer():
net = torch.load('Train1_Dream_8.pt') #LOAD THE REQUIRED MODEL
decoder = net.module.decoder
new_file = open('000135.kmxm', 'rb') #SPECIFY THE INPUT FILENAME
filename = 'conv.dcm' #SPECIFY THE OUTPUT FILE NAME
#READING THE REQUIRED CHARACTERS
new_file.seek(33)
rows = new_file.read(8)
h = struct.unpack('Q', rows)[0]
new_file.seek(41)
columns = new_file.read(8)
w = struct.unpack('Q', columns)[0]
new_file.seek(49)
bpp = new_file.read(1)
b = struct.unpack('B', bpp)[0]
new_file.seek(50)
img = new_file.read()
#print w,h
#CONVERTING TO THE VALUES WITH THE PADDING
wn = (int(np.floor(w / 16)) + 1) * 16
hn = (int(np.floor(h / 16)) + 1) * 16
imgfile = np.fromstring(img, sep='', dtype=np.uint8)
imgfile = imgfile.reshape((8, wn / 16, hn / 16))
w1 = (int(np.floor(w / 16)) + 1) * 16 - w
h1 = (int(np.floor(h / 16)) + 1) * 16 - h
imgfile = torch.unsqueeze(torch.from_numpy(imgfile.astype(float)),
0).float()
imgfile = imgfile / 255.0
imgfile = decoder(Variable(imgfile))
imgfile = imgfile.data
imgfile = torch.round((imgfile.clamp(0.0, 1.0)) * (255.0))
imgfile = imgfile[0]
imgfile = imgfile[0]
imgfile = imgfile.numpy().astype(np.uint16)
imgfile = imgfile[int(w1 / 2):int(w1 / 2) + w, int(h1 / 2):int(h1 / 2) + h]
#print "length of ",len(imgfile)
#SAVING PNG OF THE IMAGE (OPTIONAL)
im = fromarray(imgfile).convert("L") ## Saving preview image
im.save('dicom.png')
imgdata = imgfile.tostring()
#WRITING THE DICOM FILE
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble="\0" * 128)
#INCASE OF IRREGULARTIES FROM ACTUAL IMAGE CHECK THESE VALUES WITH ACTUAL FILE
ds.Modality = 'MG'
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 11
ds.BitsStored = 12
ds.WindowCenter = 128
ds.WindowWidth = 256
ds.BitsAllocated = 16
ds.Rows = w
ds.Columns = h
ds.PixelData = imgdata
ds.save_as(filename)
print "file saved"
ds.PatientsSex = dsOld.PatientsSex
ds.PatientAge = 0#dsOld.PatientAge
ds.SliceThickness = dsOld.SliceThickness
#ds.SpacingBetweenSlices = dsOld.SpacingBetweenSlices
ds.PatientPosition = dsOld.PatientPosition
#Image info (check need in ct modality
ds.ImagePositionPatient = dsOld.ImagePositionPatient
ds.ImageOrientationPatient = dsOld.ImageOrientationPatient
ds.FrameofReferenceUID = dsOld.FrameofReferenceUID
ds.SliceLocation = dsOld.SliceLocation
ds.PixelSpacing = dsOld.PixelSpacing
ds.SamplesperPixel = dsOld.SamplesperPixel
ds.PhotometricInterpretation = dsOld.PhotometricInterpretation
ds.Rows = dsOld.Rows
ds.Columns = dsOld.Columns
ds.BitsAllocated = dsOld.BitsAllocated
ds.BitsStored = dsOld.BitsStored
ds.HighBit = dsOld.HighBit
ds.PixelRepresentation = dsOld.PixelRepresentation
#ds.WindowCenter = dsOld.WindowCenter
#ds.WindowWidth = dsOld.WindowWidth
ds.RescaleIntercept = dsOld.RescaleIntercept
ds.RescaleSlope = dsOld.RescaleSlope
# create 2d image of air HU
ds.pixel_array = dsOld.pixel_array
ds.PixelData = dsOld.PixelData
# Set the transfer syntax
ds.is_little_endian = True
def copyCTtoRTDose(self, path, ds, doseData, imageRow, imageCol,
sliceCount, dgs):
# Create a RTDose file for broadcasting.
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.2' # CT Image Storage
# Needs valid UID
file_meta.MediaStorageSOPInstanceUID = ds.file_meta.MediaStorageSOPInstanceUID
file_meta.ImplementationClassUID = ds.file_meta.ImplementationClassUID
#create DICOM RT-Dose object.
rtdose = FileDataset(path + '/rtdose.dcm', {},
file_meta=file_meta,
preamble="\0" * 128)
#No DICOM object standard. Use only required to avoid errors with viewers.
rtdose.SOPInstanceUID = ds.SOPInstanceUID
rtdose.SOPClassUID = '1.2.840.10008.5.1.4.1.1.481.2'
rtdose.file_meta.TransferSyntaxUID = ds.file_meta.TransferSyntaxUID
rtdose.PatientsName = ds.PatientsName
rtdose.PatientID = ds.PatientID
rtdose.PatientsBirthDate = ds.PatientsBirthDate
#Crashed caused if no sex.
try:
rtdose.PatientsSex = ds.PatientsSex
except:
rtdose.PatientsSex, ds.PatientsSex = 'O', 'O'
self.data.update({'images': ds})
rtdose.StudyDate = ds.StudyDate
rtdose.StudyTime = ds.StudyTime
rtdose.StudyInstanceUID = ds.StudyInstanceUID
rtdose.SeriesInstanceUID = ds.SeriesInstanceUID
rtdose.StudyID = ds.StudyID
rtdose.SeriesNumber = ds.SeriesNumber
rtdose.Modality = 'RTDOSE'
rtdose.ImagePositionPatient = ds.ImagePositionPatient
rtdose.ImageOrientationPatient = ds.ImageOrientationPatient
rtdose.FrameofReferenceUID = ds.FrameofReferenceUID
rtdose.PositionReferenceIndicator = ds.PositionReferenceIndicator
rtdose.PixelSpacing = ds.PixelSpacing
rtdose.SamplesperPixel = 1
rtdose.PhotometricInterpretation = 'MONOCHROME2'
rtdose.NumberofFrames = sliceCount
rtdose.Rows = imageRow
rtdose.Columns = imageCol
rtdose.BitsAllocated = 32
rtdose.BitsStored = 32
rtdose.HighBit = 31
rtdose.PixelRepresentation = 0
rtdose.DoseUnits = 'GY'
rtdose.DoseType = 'PHYSICAL'
rtdose.DoseSummationType = 'FRACTION'
#In case spaceing tag is missing.
if not ds.has_key('SpacingBetweenSlices'):
ds.SpacingBetweenSlices = ds.SliceThickness
#Ensure patient pos is on "Last slice".
if fnmatch.fnmatch(ds.PatientPosition, 'FF*'):
#For compliance with dicompyler update r57d9155cc415 which uses a reverssed slice ordering.
doseData = doseData[::-1]
rtdose.ImagePositionPatient[2] = ds.ImagePositionPatient[2]
elif fnmatch.fnmatch(ds.PatientPosition, 'HF*'):
rtdose.ImagePositionPatient[2] = ds.ImagePositionPatient[2] - (
sliceCount - 1) * ds.SpacingBetweenSlices
#Create Type A(Relative) GFOV.
rtdose.GridFrameOffsetVector = list(
np.arange(0., sliceCount * ds.SpacingBetweenSlices,
ds.SpacingBetweenSlices))
#Scaling from int to physical dose
rtdose.DoseGridScaling = dgs
#Store images in pixel_array(int) & Pixel Data(raw).
rtdose.pixel_array = doseData
rtdose.PixelData = doseData.tostring()
#Tag required by dicompyler.
plan_meta = Dataset()
rtdose.ReferencedRTPlans = []
rtdose.ReferencedRTPlans.append([])
rtdose.ReferencedRTPlans[0] = plan_meta
rtdose.ReferencedRTPlans[0].ReferencedSOPClassUID = 'RT Plan Storage'
rtdose.ReferencedRTPlans[
0].ReferencedSOPInstanceUID = ds.SOPInstanceUID
#Create RTPlan to acticate DVH
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.5' # RT Plan Storage
file_meta.MediaStorageSOPInstanceUID = ds.file_meta.MediaStorageSOPInstanceUID
file_meta.ImplementationClassUID = ds.file_meta.ImplementationClassUID
#create DICOM RT-Plan object.
rtPlan = FileDataset(path + '/rtplan.dcm', {},
file_meta=file_meta,
preamble="\0" * 128)
#No DICOM object standard. Use only required to avoid errora with viewers.
rtPlan.SOPInstanceUID = ds.SOPInstanceUID
rtPlan.SOPClassUID = '1.2.840.10008.5.1.4.1.1.481.5'
rtPlan.ReferencedSOPInstanceUID = ds.SOPInstanceUID #Need to change
rtPlan.file_meta.TransferSyntaxUID = ds.file_meta.TransferSyntaxUID
rtPlan.PatientsName = ds.PatientsName
rtPlan.PatientID = ds.PatientID
rtPlan.PatientsSex = ds.PatientsSex
rtPlan.PatientsBirthDate = ds.PatientsBirthDate
rtPlan.RTPlanLabel = 'Simulation'
rtPlan.RTPlanDate = ds.StudyDate
rtPlan.RTPlanTime = ds.StudyTime
rtPlan.Modality = 'RTPLAN'
rtPlan.StudyInstanceUID = ds.StudyInstanceUID
rtPlan.SeriesInstanceUID = ds.SeriesInstanceUID
rtPlan.StudyID = ds.StudyID
rtPlan.SeriesNumber = ds.SeriesNumber
return rtdose, rtPlan
def encode(self, input_image, text, output_file):
print("using file: " + input_image)
print("analysis result is: " + text)
# first we need to setup some meta data
# Populate required values for file meta information
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage .... check here: http://dicom.nema.org/dicom/2013/output/chtml/part04/sect_B.5.html
file_meta.MediaStorageSOPInstanceUID = "1.2.3" # !! Need valid UID here for real work
file_meta.ImplementationClassUID = "1.2.3.4" # !!! Need valid UIDs here
# Create the FileDataset instance (initially no data elements, but file_meta supplied)
ds = FileDataset(output_file, {}, file_meta=file_meta, preamble=b"\0" * 128)
# Add the data elements -- not trying to set all required here. Check DICOM standard
# ds.PatientName = "John Smith2"
ds.add_new(Tag(0x10,0x10), "PN", "John 99") # this is the tag for patient's name
ds.PatientID = "123456"
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
# Set creation date/time
dt = datetime.datetime.now()
ds.ContentDate = dt.strftime('%Y%m%d')
timeStr = dt.strftime('%H%M%S.%f') # long format with micro seconds
ds.ContentTime = timeStr
# imge file data
# read this http://stackoverflow.com/questions/14350675/create-pydicom-file-from-numpy-array
# img=mpimg.imread(input_image)
img = cv2.imread(input_image, 0) # we read the image monochrome
print(img.shape)
plt.imshow(img)
print(img.shape[0])
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = bytes((0,))
ds.LargestImagePixelValue = bytes((255,))
img = img.astype(np.uint16)
print(np.amin(img))
print(np.amax(img))
ds.PixelData = img.tostring()
ds.Rows = img.shape[0]
ds.Columns = img.shape[1]
print("Writing test file", output_file)
ds.save_as(output_file)
print("File saved.")
return
def write_dicom(arguments, pixel_array, meta_arr=[]):
"""
Function to create completely new DICOM file.
:param arguments: Dictionary containing input parameters.
:param pixel_array: 2D or 3D numpy array containing pixel values.
:param meta_arr: An array contaning parsed meta data.
:return:
"""
#TODO:Check the possibility to use UID generator instead hardcoded ones.
file_meta = Dataset()
# The Secondary Capture (SC) Image Information Object Definition (IOD) specifies images that are converted from
# a non-DICOM format to a modality independent DICOM format.
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
# Since we don't want to store any additional info in the header, we set it to x00
ds = FileDataset(arguments['out_file'], {},
file_meta=file_meta,
preamble=b'0' * 128)
# Type of equipment that originally acquired the data.
ds.Modality = 'WSD'
# Setting the date and time
ds.ContentDate = str(datetime.date.today()).replace('-', '')
ds.ContentTime = str(time.time()) # milliseconds since the epoch
# Keep scrolling :)
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
# Manufacturer
ds.SecondaryCaptureDeviceManufctur = 'NCBJ'
# These are the necessary imaging components of the FileDataset object.
# Number of color channels.
ds.SamplesPerPixel = 1
# It defines what does every color channel hold
ds.PhotometricInterpretation = "MONOCHROME2"
#
ds.PixelRepresentation = 0
ds.HighBit = 15
# Here meta-data is put into the Data Set. It can override hardcoded values, but parameters providided as program
# arguments will be inserted no matter what is in the meta-data file.
ds = write_meta(meta_arr, ds)
# How many bits are allocated to store pixel info
ds.BitsAllocated = arguments['bytes_per_pix'] * 8
# How many bits are used to store pixel info
ds.BitsStored = arguments['bytes_per_pix'] * 8
# Whether signed or unsigned numbers are used
if arguments['is_signed']:
ds.PixelRepresentation = 1
else:
ds.PixelRepresentation = 0
# Read from data the dimensions.
if len(pixel_array.shape) == 3:
ds.NumberOfFrames = pixel_array.shape[0]
ds.Columns = pixel_array.shape[1]
ds.Rows = pixel_array.shape[2]
else:
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
try:
err_str = '[ERROR] The file could not be created because of: '
data_type = recognize_type(arguments['bytes_per_pix'],
arguments['is_signed'],
arguments['is_float'])
if pixel_array.dtype != data_type:
pixel_array = pixel_array.astype(data_type)
ds.PixelData = pixel_array.tostring()
ds.save_as(arguments['out_file'].replace('.dcm', '') + '.dcm')
print('[INFO] Writing to DICOM file is complete!')
except ValueError as ve:
print(err_str + 'ValueError ' + str(ve))
sys.exit(1)
except FileExistsError as fe:
print(err_str + 'FileExistsError ' + str(fe))
sys.exit(1)
except Exception as e:
print(err_str + str(e))
sys.exit(1)
def write_dicom(pixel_array, filename, series_number, fdf_info,
series_description, project, subject, session):
"""Write a dicom from a pixel_array (numpy).
:param pixel_array: 2D numpy ndarray.
If pixel_array is larger than 2D, errors.
:param filename: string name for the output file.
:param ds_ori: original pydicom object of the pixel_array
:param series_number: number of the series being processed
:param series_description: series description for Osirix display
:param project: XNAT Project ID
:param subject: XNAT Subject label
:param session: XNAT Session label
"""
# Variables
pixel_array = np.rot90(pixel_array)
pixel_array = np.rot90(pixel_array)
pixel_array = np.rot90(pixel_array)
# less than zero
# pixel_array[pixel_array < 0] = 0
now = datetime.datetime.now()
date = '%d%02d%02d' % (now.year, now.month, now.day)
sop_id = '1.2.840.10008.5.1.4.1.1.4.%s' % date
ti = str(time.time())
uid = sop_id + ti
# Other tags to set
sop_uid = sop_id + str(now).replace('-', '')\
.replace(':', '')\
.replace('.', '')\
.replace(' ', '')
# Number of frames
size = pixel_array.shape
nb_frames = None
if len(size) == 3:
nb_frames = size[2]
elif len(size) == 4:
nb_frames = size[2]*size[3]
# Create the ds object
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = sop_id
file_meta.ImplementationClassUID = '1.2.840.10008.5.1.4.1.1.4'
ds = FileDataset(filename, {}, file_meta=file_meta, preamble="\0"*128)
ds.SeriesDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.ContentDate = '%d%02d%02d' % (now.year, now.month, now.day)
ds.ContentTime = str(time.time()) # milliseconds since the epoch
ds.StudyInstanceUID = uid
ds.SeriesInstanceUID = uid
# Other tags to set
ds.SOPInstanceUID = sop_uid[:-1]
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
# These are the necessary imaging components of the FileDataset object.
ds.Modality = 'MR'
ds.ConversionType = 'WSD'
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
ds.ProtocolName = '%s 9.4T' % series_description
ds.StudyDescription = project
ds.PatientsName = subject
ds.PatientID = session
ds.SeriesDescription = series_description
ds.SeriesNumber = 1
ds.SmallestImagePixelValue = pixel_array.min()
ds.LargestImagePixelValue = pixel_array.max()
ds.AcquisitionNumber = 1
ds.SamplesperPixel = 1
ds.PixelSpacing = '%s\%s' % (fdf_info['spacing'][0],
fdf_info['spacing'][1])
ds.SpacingBetweenSlices = float(fdf_info['spacing'][2])
ds.ImageOrientation = '\\'.join(fdf_info['orientation'])
ds.PatientPosition = '\\'.join(fdf_info['origin'])
if nb_frames:
ds.NumberOfFrames = nb_frames
# Organise the array:
if len(size) == 3:
pixel_array2 = np.zeros((size[0]*size[2], size[1]))
for i in range(size[2]):
pixel_array2[size[0]*i:size[0]*(i+1), :] = pixel_array[:, :, i]
elif len(size) > 3:
pixel_array2 = np.zeros((size[0]*size[2]*size[3], size[1]))
for i in range(size[2]):
for j in range(size[3]):
pixel_array2[size[0]*j+i*size[3]*size[0]:
size[0]*(j+1)+i*size[3]*size[0],
:] = pixel_array[:, :, i, j]
else:
pixel_array2 = pixel_array
# Set the Image pixel array
if pixel_array2.dtype != np.uint16:
pixel_array2 = pixel_array2.astype(np.uint16)
print pixel_array2.max()
print pixel_array2.min()
ds.PixelData = pixel_array2.tostring()
# Save the image
ds.save_as(filename)
def writeDicomFiles(self, filename):
for i in range(1,len(self.PA)): #Image 0 is a null image for default display and will not be written out
fileName = filename + str(i) + ".dcm"
# print ("printing dicom file " + fileName)
# Populate required values for file meta information
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
# Create the FileDataset instance (initially no data elements, but file_meta supplied)
ds = FileDataset(fileName, {}, file_meta=file_meta, preamble="\0"*128)
ds.Modality = 'MR'
ds.ContentDate = str(datetime.date.today()).replace('-','')
ds.ContentTime = str(time.time()) #milliseconds since the epoch
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.SOPClassUID = 'MR Image Storage'
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
## These are the necessary imaging components of the FileDataset object.
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
#Add MRI data elements
ds.bValue= self.bValue[i]
ds.Columns=self.Columns[i]
ds.FoVX=self.FoVX[i]
ds.FoVY=self.FoVY[i]
ds.ImageOrientationPatient=self.ImagePosition[i].tolist()
ds.InstitutionName=self.InstitutionName[i]
ds.PixelBandwidth= self.PixelBandwidth[i]
ds.PixelSpacing =[self.PixelSpacingY[i],self.PixelSpacingX[i]]
ds.Rows= self.Rows[i]
ds.PatientName = self.PatientName[i]
ds.PatientID = "123456"
ds.ProtocolName=self.ProtocolName[i]
ds.RepetitionTime = self.TR[i]
ds.SeriesDescription=self.SeriesDescription[i]
ds.EchoTime = self.TE[i]
ds.FlipAngle= self.FA[i]
ds.InversionTime=self.TI[i]
ds.ImageOrientationPatient[3:6]=self.ColumnDirection[i].tolist()
ds.ImageOrientationPatient[:3]=self.RowDirection[i].tolist()
ds.MagneticFieldStrength = self.MagneticFieldStrength[i]
ds.Manufacturer = self.Manufacturer[i]
pixel_array=np.transpose(self.PA[i]) #numpy pixel array
if self.DataType[i].lower() == "phase" :
pixel_array=(pixel_array+np.pi) *10000 # phase data -pi to pi is converted to 0 to 10000pi
#print "Adjusting phase to 16 bit integer"
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring() # image byte data
# Set the transfer syntax
ds.is_little_endian = True
ds.is_implicit_VR = True
ds.save_as(fileName)
def process_mrs_ge(pfile_dir):
# Make a top-level instance and hide in top left corner, get filepath
# root = Tkinter.Tk()
# root.geometry('0x0+0+0')
# root.attributes("-topmost", 1)
# pfile_dir = tkFileDialog.askdirectory(parent=root)
# root.attributes("-topmost", 0)
# root.destroy()
print('Working...')
root_dir = os.path.abspath(os.path.join(pfile_dir, os.pardir))
pat_name = os.path.split(os.path.dirname(pfile_dir))[1]
filelist = glob.glob(pfile_dir + '/*.7')
filelist.extend(glob.glob(pfile_dir + '/*.anon'))
for fname in filelist:
try:
print('')
print(fname)
fbin = open(fname, 'rb')
sw_ver = int(
str(struct.unpack('f', fbin.read(4))[0]).split('.')[0])
print('software version: ' + str(sw_ver))
s = fbin.read()
if s.find('PROBE-P') > 0:
seqtype = 'PRESS'
elif s.find('PROBE-S') > 0:
seqtype = 'STEAM'
else:
print('WARNING: acquisition type not recognised!')
if sw_ver < 26:
fbin.seek(82)
pt_sz = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(1468)
hdr_sz = struct.unpack('<i', fbin.read(4))[0]
if sw_ver == 15:
fbin.seek(144580) # FOR REV 15
elif sw_ver == 16:
fbin.seek(144628) # FOR REV 16
elif sw_ver > 19:
fbin.seek(148404) # FOR REV 20 & 24
te = struct.unpack('<i', fbin.read(4))[0]
if sw_ver == 15:
fbin.seek(144572) # FOR REV 15
elif sw_ver == 16:
fbin.seek(144620) # FOR REV 16
elif sw_ver > 19 and sw_ver < 26:
fbin.seek(148396) # FOR REV 20 & 24
tr = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(148712)
exam_no = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(148724)
series_no = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(424)
c_freq = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(200)
st_rcv = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(202)
en_rcv = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(70)
nechoes = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(72)
navs = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(74)
nframes = struct.unpack('<h', fbin.read(2))[0]
n_rcv = (en_rcv - st_rcv) + 1
# pfile=headers.Pfile.from_file(fname)
# frm_sz=pfile.acq_x_res*2*pt_sz
# echo_size=frm_sz*pfile.acq_y_Res
# slice_size=echo_size*nechoes
# data_sz=pfile.acq_x_res*2*(pfile.acq_y_Res-1)
fbin.seek(102)
acq_x_res = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(104)
acq_y_Res = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(216)
bandwidth = struct.unpack('<f', fbin.read(4))[0]
# if sw_ver == 15:
# fbin.seek(948) # FOR REV 15
# elif sw_ver == 16:
# fbin.seek(948) # FOR REV 16
# elif sw_ver > 19:
# fbin.seek(948) # FOR REV 20 & 24
fbin.seek(232)
ws_nsa = struct.unpack('<f', fbin.read(4))[0]
elif sw_ver == 26:
fbin.seek(158)
pt_sz = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(4)
hdr_sz = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(199244) # FOR REV 26
te = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(199236) # FOR REV 26
tr = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(196428)
exam_no = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(194068)
series_no = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(504)
c_freq = struct.unpack('<i', fbin.read(4))[0]
fbin.seek(264)
st_rcv = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(266)
en_rcv = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(146)
nechoes = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(148)
navs = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(2520)
nframes = struct.unpack('<h', fbin.read(2))[0]
n_rcv = (en_rcv - st_rcv) + 1
# pfile=headers.Pfile.from_file(fname)
# frm_sz=pfile.acq_x_res*2*pt_sz
# echo_size=frm_sz*pfile.acq_y_Res
# slice_size=echo_size*nechoes
# data_sz=pfile.acq_x_res*2*(pfile.acq_y_Res-1)
fbin.seek(156)
acq_x_res = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(180)
acq_y_Res = struct.unpack('<h', fbin.read(2))[0]
fbin.seek(280)
bandwidth = struct.unpack('<f', fbin.read(4))[0]
fbin.seek(296)
ws_nsa = struct.unpack('<f', fbin.read(4))[0]
ws_avgs = int(ws_nsa) / int(navs)
frm_sz = acq_x_res * 2 * pt_sz
echo_size = frm_sz * acq_y_Res
slice_size = echo_size * nechoes
data_sz = acq_x_res * 2 * (acq_y_Res - 1)
#off_raw is the offset in bytes to the start of the raw data
off_raw = hdr_sz + frm_sz
w_avgs = acq_y_Res - 1 - ws_avgs
print('Number of water ref frames : ' + str(w_avgs))
print('Number of data frames : ' + str(ws_avgs))
fbin.seek(off_raw)
raw_data = np.zeros(data_sz)
frame_data = np.zeros(shape=(int(acq_y_Res - 1), int(acq_x_res),
int(n_rcv)),
dtype=np.int64)
frame_data = frame_data.view(np.complex64)
rot_frame_data = np.zeros(shape=(int(acq_y_Res - 1),
int(acq_x_res), int(n_rcv)),
dtype=np.float64)
rot_frame_data = rot_frame_data.view(np.complex64)
fid_norm = np.zeros(shape=(int(acq_y_Res - 1), int(acq_x_res),
int(n_rcv)),
dtype=np.float64)
fid_norm = fid_norm.view(np.complex64)
summed_signal = np.zeros(shape=(int(acq_y_Res - 1) *
int(acq_x_res)),
dtype=np.float64)
summed_signal = summed_signal.view(np.complex64)
for receiver in range(0, n_rcv):
fbin.seek(off_raw + (slice_size * receiver))
for element in range(0, data_sz):
raw_data[element] = struct.unpack('<i', fbin.read(4))[0]
for frm in range(0, int(acq_y_Res - 1)):
row_off = frm * acq_x_res * 2
for pt in range(0, int(acq_x_res)):
frame_data[frm, pt, receiver] = raw_data[
(2 * pt) +
row_off] + 1j * raw_data[(2 * pt) + 1 + row_off]
fbin.close()
amp = np.zeros(n_rcv, dtype=np.int64)
amp = amp.view(np.complex64)
amp = frame_data[0, 5, :]
ang = np.zeros(n_rcv)
fid_w = np.zeros(n_rcv)
for n in range(0, n_rcv):
ang[n] = cmath.phase(list(frame_data[0, 5, :])[n])
# print(ang)
ph_dif = ang - ang[0]
for receiver in range(0, n_rcv):
rot_frame_data[:, :,
receiver] = frame_data[:, :, receiver] * np.exp(
-1j * ph_dif[receiver])
for receiver in range(0, n_rcv):
fid_w[receiver] = abs(amp[receiver]) / np.sqrt(
sum(abs(amp * amp)))
# fid_w[receiver]=abs(amp[receiver])/sum(abs(amp))
# fid_w=np.max(np.abs(frame_data[1,:,:]),axis=0)/np.sqrt(np.sum(np.max(np.abs(frame_data[1,:,:]),axis=0)*np.max(np.abs(frame_data[1,:,:]),axis=0)))
# fid_w=np.abs(np.max(frame_data[0,:,:],axis=0))/(np.sum(np.abs(np.max(frame_data[0,:,:],axis=0))))
fid_norm[:, :,
receiver] = rot_frame_data[:, :,
receiver] * fid_w[receiver]
summed_signal = np.sum(fid_norm, axis=2)
#fbin.close()
#raw_data=raw_data.astype(np.int64)
print('coil weightings:')
print(fid_w)
output_data_ws = []
for pt in range(w_avgs * int(acq_x_res), np.size(summed_signal)):
output_data_ws.append(
np.float64(np.imag(summed_signal.flatten()[pt])))
output_data_ws.append(
np.float64(np.real(summed_signal.flatten()[pt])))
output_data_w = []
for pt in range(0, w_avgs * int(acq_x_res)):
output_data_w.append(
np.float64(np.imag(summed_signal.flatten()[pt])))
output_data_w.append(
np.float64(np.real(summed_signal.flatten()[pt])))
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'MRSpectroscopyStorage'
file_meta.MediaStorageSOPInstanceUID = dicom.UID.generate_uid()
file_meta.ImplementationClassUID = dicom.UID.generate_uid()
ds_ws = FileDataset(fname + '_dcm', {},
file_meta=file_meta,
preamble="\0" * 128)
ds_ws.Modality = 'MR'
ds_ws.ContentDate = str(datetime.date.today()).replace('-', '')
ds_ws.ContentTime = str(time.time()) #milliseconds since the epoch
ds_ws.StudyInstanceUID = dicom.UID.generate_uid()
ds_ws.SeriesInstanceUID = dicom.UID.generate_uid()
ds_ws.SOPInstanceUID = dicom.UID.generate_uid()
ds_ws.SOPClassUID = 'MRSpectroscopyStorage'
ds_ws.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
ds_ws.VolumeLocalizationTechnique = seqtype
ds_ws.RepetitionTime = tr / 1000
## These are the necessary imaging components of the FileDataset object.
ds_ws.Columns = 1
ds_ws.Rows = 1
ds_ws.DataPointColumns = int(acq_x_res)
ds_ws.SpectroscopyAcquisitionDataColumns = ds_ws.DataPointColumns
ds_ws.DataPointRows = ws_avgs
ds_ws.SpectroscopyData = output_data_ws
ds_ws.ImageType = ['ORIGINAL', 'PRIMARY', 'SPECTROSCOPY', 'NONE']
ds_ws.SpectralWidth = bandwidth
ds_ws.SeriesNumber = series_no
ds_ws.ProtocolName = fname.split('\\')[-1].split(
'.')[0] + '_SVS_TR' + str(int(tr / 1000)) + '_TE' + str(
int(te / 1000))
c_freq2 = c_freq / 10000000.0
ds_ws.TransmitterFrequency = c_freq2
ds_ws.EffectiveEchoTime = te / 1000
ds_ws.EchoTime = te / 1000
ds_ws.WaterReferencedPhaseCorrection = 'N'
if w_avgs > 0:
file_meta_w = Dataset()
file_meta_w.MediaStorageSOPClassUID = 'MRSpectroscopyStorage'
file_meta_w.MediaStorageSOPInstanceUID = dicom.UID.generate_uid(
)
file_meta_w.ImplementationClassUID = dicom.UID.generate_uid()
ds_w = FileDataset(fname + '_dcm', {},
file_meta=file_meta,
preamble="\0" * 128)
ds_w.Modality = 'MR'
ds_w.ContentDate = str(datetime.date.today()).replace('-', '')
ds_w.ContentTime = str(
time.time()) #milliseconds since the epoch
ds_w.StudyInstanceUID = dicom.UID.generate_uid()
ds_w.SeriesInstanceUID = dicom.UID.generate_uid()
ds_w.SOPInstanceUID = dicom.UID.generate_uid()
ds_w.SOPClassUID = 'MRSpectroscopyStorage'
ds_w.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
ds_w.VolumeLocalizationTechnique = seqtype
## These are the necessary imaging components of the FileDataset object.
ds_w.Columns = 1
ds_w.Rows = 1
ds_w.DataPointColumns = int(acq_x_res)
ds_w.SpectroscopyAcquisitionDataColumns = ds_w.DataPointColumns
ds_w.DataPointRows = w_avgs
ds_w.SpectroscopyData = output_data_w
ds_w.ImageType = [
'ORIGINAL', 'PRIMARY', 'SPECTROSCOPY', 'NONE'
]
ds_w.SpectralWidth = bandwidth
ds_w.SeriesNumber = series_no
ds_w.ProtocolName = fname.split('\\')[-1].split(
'.')[0] + '_SVS_TR' + str(int(tr / 1000)) + '_TE' + str(
int(te / 1000))
c_freq2 = c_freq / 10000000.0
ds_w.TransmitterFrequency = c_freq2
ds_w.EffectiveEchoTime = te / 1000
ds_w.EchoTime = te / 1000
ds_w.RepetitionTime = tr / 1000
ds_w.WaterReferencedPhaseCorrection = 'N'
if not os.path.exists(
str(root_dir) + '/' + str(pat_name) + '_PREPROC_LCM'):
os.makedirs(
str(root_dir) + '/' + str(pat_name) + '_PREPROC_LCM')
# if not os.path.exists(str(root_dir)+'/'+str(pat_name)+'_LCM_Results'):
# os.makedirs(str(root_dir)+'/'+str(pat_name)+'_LCM_Results')
ds_ws.save_as(
str(root_dir) + '/' + str(pat_name) + '_PREPROC_LCM/' +
str(ds_ws.ProtocolName))
if w_avgs > 0:
ds_w.save_as(
str(root_dir) + '/' + str(pat_name) + '_PREPROC_LCM/' +
str(ds_w.ProtocolName) + '_WATER')
except AttributeError:
print('...')
except IOError:
print 'no such file'
print('DONE!')
def write_dicom(pixel_array, filename, origin, origins, orientation):
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'
file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'
ds = FileDataset(filename, {},file_meta = file_meta,preamble="\0"*128)
ds.ImageType= 'ORIGINAL\PRIMARY\AXIAL\CT'
ds.SOPClassUID = 'Secondary Capture Image Storage'
ds.SOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
ds.StudyDate = str(datetime.date.today()).replace('-','')
#ds.ImageDate = str(datetime.date.today()).replace('-','')
ds.StudyTime = str(time.time())
#ds.ImageTime = str(time.time())
ds.AccessionNumber = '153745645'
ds.Modality = 'CT'
ds.Manufacturer = 'PY_BONEMAT_ABAQUS'
ds.InstitutionName = 'UNIVERISTY_OF_BATH'
ds.ReferringPhysiciansName = 'NOTAPPLICABLE'
ds.ManufacturersModelName = 'VERSION1-0-9'
ds.ReferencedImageSequence = ''
ds.PatientsName = 'SUBJECT001'
ds.PatientID = ''
ds.PatientsBirthDate = ''
ds.PatientsSex = 'F'
ds.SliceThickness = 2.7
ds.KVP = 120
ds.SpacingBetweenSlices = 1
ds.DataCollectionDiameter = 430
ds.ReconstructionDiameter = 430.00
ds.DistanceSourceToDetector = 1093
ds.DistanceSourceToPatient = 630.
ds.GantryDetectorTilt = 0.0
ds.ScanArc = 403
ds.XRayTubeCurrent = 200
ds.Exposure = 135
ds.FilterType = 'F'
ds.PatientPosition = 'HFS'
ds.StudyInstanceUID = '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'
ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780.1'
#ds.ReferenceSOPClassUID = 'Secondary Capture Image Storage'
#ds.ReferencedSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'
#ds.ContentDate = str(datetime.date.today()).replace('-','')
#ds.ContentTime = str(time.time())
ds.StudyID = '15346254'
ds.SeriesNumber = '8733'
ds.AcquisitionNumber = 1
ds.ImageNumber = origins.index(origin) + 1
ds.SliceLocation = origin[2]
ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.PixelRepresentation = 0
ds.HighBit = 15
ds.BitsStored = 16
ds.BitsAllocated = 16
ds.SmallestImagePixelValue = '\\x00\\x00'
ds.LargestImagePixelValue = '\\xff\\xff'
ds.Columns = pixel_array.shape[0]
ds.Rows = pixel_array.shape[1]
ds.PatientOrientation = ['L','P']
ds.ImagePositionPatient = origin
ds.ImageOrientationPatient = orientation
ds.PixelSpacing = [1.0, 1.0]
ds.RescaleSlope = 1.0
ds.RescaleIntercept = 0.0
if pixel_array.dtype != np.uint16:
pixel_array = pixel_array.astype(np.uint16)
ds.PixelData = pixel_array.tostring()
ds.save_as(filename)
return
