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 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 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, 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 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):
"""
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(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 _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 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 create_dicom(self):
""" Generates and returns Dicom RTSTRUCT object, which holds all VOIs.
:returns: a Dicom RTSTRUCT object holding any VOIs.
"""
if _dicom_loaded is False:
raise ModuleNotLoadedError("Dicom")
meta = Dataset()
meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.3' # RT Structure Set Storage SOP Class
# see https://github.com/darcymason/pydicom/blob/master/pydicom/_uid_dict.py
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)
if self.cube is not None:
ds.PatientsName = self.cube.patient_name
else:
ds.PatientsName = ""
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 = '1.2.840.10008.5.1.4.1.1.481.3' # RT Structure Set Storage SOP Class
ds.SOPInstanceUID = '1.2.3' # !!!!!!!!!!
ds.StudyInstanceUID = '1.2.3' # !!!!!!!!!!
ds.SeriesInstanceUID = '1.2.3' # !!!!!!!!!!
ds.FrameofReferenceUID = '1.2.3' # !!!!!!!!!
ds.SeriesDate = '19010101' # !!!!!!!!
ds.ContentDate = '19010101' # !!!!!!
ds.StudyDate = '19010101' # !!!!!!!
ds.SeriesTime = '000000' # !!!!!!!!!
ds.StudyTime = '000000' # !!!!!!!!!!
ds.ContentTime = '000000' # !!!!!!!!!
ds.StructureSetLabel = 'pyTRiP plan'
ds.StructureSetDate = '19010101'
ds.StructureSetTime = '000000'
ds.StructureSetName = 'ROI'
ds.Modality = 'RTSTRUCT'
roi_label_list = []
roi_data_list = []
roi_structure_roi_list = []
# to get DICOM which can be loaded in Eclipse we need to store information about UIDs of all slices in CT
# first we check if DICOM cube is loaded
if self.cube is not None:
rt_ref_series_data = Dataset()
rt_ref_series_data.SeriesInstanceUID = '1.2.3.4.5'
rt_ref_series_data.ContourImageSequence = Sequence([])
# each CT slice corresponds to one DICOM file
for slice_dicom in self.cube.create_dicom():
slice_dataset = Dataset()
slice_dataset.ReferencedSOPClassUID = '1.2.840.10008.5.1.4.1.1.2' # CT Image Storage SOP Class
slice_dataset.ReferencedSOPInstanceUID = slice_dicom.SOPInstanceUID # most important - slice UID
rt_ref_series_data.ContourImageSequence.append(slice_dataset)
rt_ref_study_seq_data = Dataset()
rt_ref_study_seq_data.ReferencedSOPClassUID = '1.2.840.10008.3.1.2.3.2' # Study Component Management Class
rt_ref_study_seq_data.ReferencedSOPInstanceUID = '1.2.3.4.5'
rt_ref_study_seq_data.RTReferencedSeriesSequence = Sequence([rt_ref_series_data])
rt_ref_frame_study_data = Dataset()
rt_ref_frame_study_data.RTReferencedStudySequence = Sequence([rt_ref_study_seq_data])
rt_ref_frame_study_data.FrameOfReferenceUID = '1.2.3.4.5'
ds.ReferencedFrameOfReferenceSequence = Sequence([rt_ref_frame_study_data])
for i in range(self.number_of_vois()):
roi_label = self.vois[i].create_dicom_label()
roi_label.ObservationNumber = str(i + 1)
roi_label.ReferencedROINumber = str(i + 1)
roi_label.RefdROINumber = str(i + 1)
roi_contours = self.vois[i].create_dicom_contour_data(i)
roi_contours.RefdROINumber = str(i + 1)
roi_contours.ReferencedROINumber = str(i + 1)
roi_structure_roi = self.vois[i].create_dicom_structure_roi()
roi_structure_roi.ROINumber = str(i + 1)
roi_structure_roi_list.append(roi_structure_roi)
roi_label_list.append(roi_label)
roi_data_list.append(roi_contours)
ds.RTROIObservations = Sequence(roi_label_list)
ds.ROIContours = Sequence(roi_data_list)
ds.StructureSetROIs = Sequence(roi_structure_roi_list)
return ds
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 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 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 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 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)
