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)
