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 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)
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 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
