def read_image(self):
self.recvImages += 1
logging.info("<-- Received MRD_MESSAGE_ISMRMRD_IMAGE (1022)")
# return ismrmrd.Image.deserialize_from(self.read)
# Explicit version of deserialize_from() for more verbose debugging
logging.debug(" Reading in %d bytes of image header",
ctypes.sizeof(ismrmrd.ImageHeader))
header_bytes = self.read(ctypes.sizeof(ismrmrd.ImageHeader))
attribute_length_bytes = self.read(ctypes.sizeof(ctypes.c_uint64))
attribute_length = ctypes.c_uint64.from_buffer_copy(
attribute_length_bytes)
logging.debug(" Reading in %d bytes of attributes",
attribute_length.value)
attribute_bytes = self.read(attribute_length.value)
if (attribute_length.value > 25000):
logging.debug(" Attributes (truncated): %s",
attribute_bytes[0:24999].decode('utf-8'))
else:
logging.debug(" Attributes: %s", attribute_bytes.decode('utf-8'))
image = ismrmrd.Image(header_bytes, attribute_bytes.decode('utf-8'))
logging.info(
" Image is size %d x %d x %d with %d channels of type %s",
image.matrix_size[0], image.matrix_size[1], image.matrix_size[2],
image.channels, ismrmrd.get_dtype_from_data_type(image.data_type))
def calculate_number_of_entries(nchannels, xs, ys, zs):
return nchannels * xs * ys * zs
nentries = calculate_number_of_entries(image.channels,
*image.matrix_size)
nbytes = nentries * ismrmrd.get_dtype_from_data_type(
image.data_type).itemsize
logging.debug("Reading in %d bytes of image data", nbytes)
data_bytes = self.read(nbytes)
image.data.ravel()[:] = np.frombuffer(
data_bytes,
dtype=ismrmrd.get_dtype_from_data_type(image.data_type))
if self.savedata is True:
if self.dset is None:
self.create_save_file()
image.attribute_string = ismrmrd.Meta.deserialize(
image.attribute_string.split(
'\x00', 1)[0]).serialize() # Strip off null teminator
self.dset.append_image("images_%d" % image.image_series_index,
image)
return image
def read_image(self):
logging.info("<-- Received MRD_MESSAGE_ISMRMRD_IMAGE (1022)")
# return ismrmrd.Image.deserialize_from(self.read)
# Explicit version of deserialize_from() for more verbose debugging
logging.debug(" Reading in %d bytes of image header",
ctypes.sizeof(ismrmrd.ImageHeader))
header_bytes = self.read(ctypes.sizeof(ismrmrd.ImageHeader))
attribute_length_bytes = self.read(ctypes.sizeof(ctypes.c_uint64))
attribute_length = ctypes.c_uint64.from_buffer_copy(
attribute_length_bytes)
logging.debug(" Reading in %d bytes of attributes",
attribute_length.value)
attribute_bytes = self.read(attribute_length.value)
logging.debug(" Attributes: %s", attribute_bytes)
image = ismrmrd.Image(header_bytes, attribute_bytes.decode('utf-8'))
logging.debug(" Image is size %d x %d x %d of type %s",
image.matrix_size[0], image.matrix_size[1],
image.matrix_size[2],
ismrmrd.get_dtype_from_data_type(image.data_type))
def calculate_number_of_entries(nchannels, xs, ys, zs):
return nchannels * xs * ys * zs
nentries = calculate_number_of_entries(image.channels,
*image.matrix_size)
nbytes = nentries * ismrmrd.get_dtype_from_data_type(
image.data_type).itemsize
logging.debug("Reading in %d bytes of image data", nbytes)
data_bytes = self.read(nbytes)
image.data.ravel()[:] = np.frombuffer(
data_bytes,
dtype=ismrmrd.get_dtype_from_data_type(image.data_type))
if (self.savedata is True):
self.dset.append_image("images_%d" % image.image_series_index,
image)
return image
def process_image(image, config, metadata):
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Incoming image data of type %s",
ismrmrd.get_dtype_from_data_type(image.data_type))
# Extract image data itself
data = image.data
logging.debug("Original image data is size %s" % (data.shape, ))
np.save(debugFolder + "/" + "imgOrig.npy", data)
# Normalize and convert to int16
data = data.astype(np.float64)
data *= 32767 / data.max()
data = np.around(data)
data = data.astype(np.int16)
# Invert image contrast
data = 32767 - data
data = np.abs(data)
data = data.astype(np.int16)
np.save(debugFolder + "/" + "imgInverted.npy", data)
# Create new MRD instance for the inverted image
imageInverted = ismrmrd.Image.from_array(data.transpose())
data_type = imageInverted.data_type
np.save(debugFolder + "/" + "imgInvertedMrd.npy", imageInverted.data)
# Copy the fixed header information
oldHeader = image.getHead()
oldHeader.data_type = data_type
imageInverted.setHead(oldHeader)
# Set ISMRMRD Meta Attributes
meta = ismrmrd.Meta({
'DataRole': 'Image',
'ImageProcessingHistory': ['FIRE', 'PYTHON'],
'WindowCenter': '16384',
'WindowWidth': '32768'
})
xml = meta.serialize()
logging.debug("Image MetaAttributes: %s", xml)
logging.debug("Image data has %d elements", image.data.size)
imageInverted.attribute_string = xml
return imageInverted
def process_image(images, connection, config, metadata):
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Processing data with %d images of type %s", len(images),
ismrmrd.get_dtype_from_data_type(images[0].data_type))
# Extract image data into a 5D array of size [img cha z y x]
data = np.stack([img.data for img in images])
head = [img.getHead() for img in images]
meta = [ismrmrd.Meta.deserialize(img.attribute_string) for img in images]
# Reformat data to the more intuitive [x y z cha img]
data = data.transpose()
# Reformat data again to [y x z cha img], i.e. [row col] for the first two
# dimensions. Note we will need to undo this later prior to sending back
# to the client
data = data.transpose((1, 0, 2, 3, 4))
# Display MetaAttributes for first image
logging.debug("MetaAttributes[0]: %s", ismrmrd.Meta.serialize(meta[0]))
# Optional serialization of ICE MiniHeader
if 'IceMiniHead' in meta[0]:
logging.debug("IceMiniHead[0]: %s",
base64.b64decode(meta[0]['IceMiniHead']).decode('utf-8'))
logging.debug("Original image data is size %s" % (data.shape, ))
np.save(debugFolder + "/" + "imgOrig.npy", data)
# Normalize and convert to int16
data = data.astype(np.float64)
data *= 32767 / data.max()
data = np.around(data)
data = data.astype(np.int16)
# Invert image contrast
data = 32767 - data
data = np.abs(data)
data = data.astype(np.int16)
np.save(debugFolder + "/" + "imgInverted.npy", data)
# Reformat data from [row col z cha img] back to [x y z cha img] before sending back to client
data = data.transpose((1, 0, 2, 3, 4))
currentSeries = 0
# Re-slice back into 2D images
imagesOut = [None] * data.shape[-1]
for iImg in range(data.shape[-1]):
# Create new MRD instance for the inverted image
# NOTE: from_array() takes input data as [x y z coil], which is
# different than the internal representation in the "data" field as
# [coil z y x]. However, we already transposed this data when
# extracting it earlier.
imagesOut[iImg] = ismrmrd.Image.from_array(data[..., iImg])
data_type = imagesOut[iImg].data_type
# Create a copy of the original fixed header and update the data_type
# (we changed it to int16 from all other types)
oldHeader = head[iImg]
oldHeader.data_type = data_type
# Unused example, as images are grouped by series before being passed into this function now
# oldHeader.image_series_index = currentSeries
# Increment series number when flag detected (i.e. follow ICE logic for splitting series)
if mrdhelper.get_meta_value(meta[iImg], 'IceMiniHead') is not None:
if mrdhelper.extract_minihead_bool_param(
base64.b64decode(
meta[iImg]['IceMiniHead']).decode('utf-8'),
'BIsSeriesEnd') is True:
currentSeries += 1
imagesOut[iImg].setHead(oldHeader)
# Create a copy of the original ISMRMRD Meta attributes and update
tmpMeta = meta[iImg]
tmpMeta['DataRole'] = 'Image'
tmpMeta['ImageProcessingHistory'] = ['PYTHON', 'INVERT']
tmpMeta['WindowCenter'] = '16384'
tmpMeta['WindowWidth'] = '32768'
tmpMeta['SequenceDescriptionAdditional'] = 'FIRE'
tmpMeta['Keep_image_geometry'] = 1
# tmpMeta['ROI_example'] = create_example_roi(data.shape)
# Example for setting colormap
# tmpMeta['LUTFileName'] = 'MicroDeltaHotMetal.pal'
# Add image orientation directions to MetaAttributes if not already present
if tmpMeta.get('ImageRowDir') is None:
tmpMeta['ImageRowDir'] = [
"{:.18f}".format(oldHeader.read_dir[0]),
"{:.18f}".format(oldHeader.read_dir[1]),
"{:.18f}".format(oldHeader.read_dir[2])
]
if tmpMeta.get('ImageColumnDir') is None:
tmpMeta['ImageColumnDir'] = [
"{:.18f}".format(oldHeader.phase_dir[0]),
"{:.18f}".format(oldHeader.phase_dir[1]),
"{:.18f}".format(oldHeader.phase_dir[2])
]
metaXml = tmpMeta.serialize()
logging.debug("Image MetaAttributes: %s",
xml.dom.minidom.parseString(metaXml).toprettyxml())
logging.debug("Image data has %d elements", imagesOut[iImg].data.size)
imagesOut[iImg].attribute_string = metaXml
return imagesOut
def process_image(images, config, metadata):
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Incoming image data of type %s",
ismrmrd.get_dtype_from_data_type(images[0].data_type))
# Display MetaAttributes for first image
tmpMeta = ismrmrd.Meta.deserialize(images[0].attribute_string)
logging.debug("MetaAttributes[0]: %s", ismrmrd.Meta.serialize(tmpMeta))
# Optional serialization of ICE MiniHeader
if 'IceMiniHead' in tmpMeta:
logging.debug("IceMiniHead[0]: %s",
base64.b64decode(tmpMeta['IceMiniHead']).decode('utf-8'))
slice = [img.slice for img in images]
phase = [img.phase for img in images]
# Process each group of venc directions separately
unique_venc_dir = np.unique([
ismrmrd.Meta.deserialize(img.attribute_string)['FlowDirDisplay']
for img in images
])
# Start the phase images at series 10. When interpreted by FIRE, images
# with the same image_series_index are kept in the same series, but the
# absolute series number isn't used and can be arbitrary
last_series = 10
imagesOut = []
for venc_dir in unique_venc_dir:
# data array has dimensions [x y sli phs]
# info lists has dimensions [sli phs]
data = np.zeros((images[0].data.shape[2], images[0].data.shape[3],
max(slice) + 1, max(phase) + 1), images[0].data.dtype)
head = [[None] * (max(phase) + 1) for _ in range(max(slice) + 1)]
meta = [[None] * (max(phase) + 1) for _ in range(max(slice) + 1)]
for img, sli, phs in zip(images, slice, phase):
if ismrmrd.Meta.deserialize(
img.attribute_string)['FlowDirDisplay'] == venc_dir:
data[:, :, sli, phs] = img.data
head[sli][phs] = img.getHead()
meta[sli][phs] = ismrmrd.Meta.deserialize(img.attribute_string)
logging.debug("Phase data with venc encoding %s is size %s" % (
venc_dir,
data.shape,
))
np.save(debugFolder + "/" + "data_" + venc_dir + ".npy", data)
# Mask out data with high mean temporal diff
threshold = 250
data_meandiff = np.mean(np.abs(np.diff(data, 3)), 3)
data_masked = data
data_masked[(data_meandiff > threshold)] = 2048
np.save(debugFolder + "/" + "data_" + venc_dir + ".npy", data_masked)
# Normalize and convert to int16
data_masked = (data_masked.astype(np.float64) - 2048) * 32767 / 2048
data_masked = np.around(data_masked).astype(np.int16)
# Re-slice back into 2D images
for sli in range(data_masked.shape[2]):
for phs in range(data_masked.shape[3]):
# Create new MRD instance for the processed image
tmpImg = ismrmrd.Image.from_array(data_masked[..., sli,
phs].transpose())
# Set the header information
tmpHead = head[sli][phs]
tmpHead.data_type = tmpImg.getHead().data_type
tmpHead.image_index = phs + sli * data_masked.shape[3]
tmpHead.image_series_index = last_series
tmpImg.setHead(tmpHead)
# Set ISMRMRD Meta Attributes
tmpMeta = meta[sli][phs]
tmpMeta['DataRole'] = 'Image'
tmpMeta['ImageProcessingHistory'] = ['FIRE', 'PYTHON']
tmpMeta['WindowCenter'] = '16384'
tmpMeta['WindowWidth'] = '32768'
xml = tmpMeta.serialize()
logging.debug("Image MetaAttributes: %s", xml)
tmpImg.attribute_string = xml
imagesOut.append(tmpImg)
last_series += 1
return imagesOut
def process_image(images, config, metadata):
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Incoming image data of type %s",
ismrmrd.get_dtype_from_data_type(images[0].data_type))
# Display MetaAttributes for first image
meta = ismrmrd.Meta.deserialize(images[0].attribute_string)
logging.debug("MetaAttributes: %s", ismrmrd.Meta.serialize(meta))
# Optional serialization of ICE MiniHeader
if 'IceMiniHead' in meta:
logging.debug("IceMiniHead: %s",
base64.b64decode(meta['IceMiniHead']).decode('utf-8'))
# Extract image data into a 5D array of size [img cha z y x]
data = np.stack([img.data for img in images])
head = [img.getHead() for img in images]
logging.debug("Original image data is size %s" % (data.shape, ))
np.save(debugFolder + "/" + "imgOrig.npy", data)
# Normalize and convert to int16
data = data.astype(np.float64)
data *= 32767 / data.max()
data = np.around(data)
data = data.astype(np.int16)
# Invert image contrast
data = 32767 - data
data = np.abs(data)
data = data.astype(np.int16)
np.save(debugFolder + "/" + "imgInverted.npy", data)
# Re-slice back into 2D images
imagesOut = [None] * data.shape[0]
for iImg in range(data.shape[0]):
# Create new MRD instance for the inverted image
imagesOut[iImg] = ismrmrd.Image.from_array(data[iImg, ...].transpose())
data_type = imagesOut[iImg].data_type
# Copy the fixed header information
oldHeader = head[iImg]
oldHeader.data_type = data_type
imagesOut[iImg].setHead(oldHeader)
# Set ISMRMRD Meta Attributes
meta = ismrmrd.Meta({
'DataRole': 'Image',
'ImageProcessingHistory': ['FIRE', 'PYTHON'],
'WindowCenter': '16384',
'WindowWidth': '32768'
})
xml = meta.serialize()
logging.debug("Image MetaAttributes: %s", xml)
logging.debug("Image data has %d elements", imagesOut[iImg].data.size)
imagesOut[iImg].attribute_string = xml
return imagesOut
def process_image(images, connection, config, metadata):
# Start timer
tic = perf_counter()
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Processing data with %d images of type %s", len(images),
ismrmrd.get_dtype_from_data_type(images[0].data_type))
# Display MetaAttributes for first image
tmpMeta = ismrmrd.Meta.deserialize(images[0].attribute_string)
logging.debug("MetaAttributes[0]: %s", ismrmrd.Meta.serialize(tmpMeta))
# Optional serialization of ICE MiniHeader
if 'IceMiniHead' in tmpMeta:
logging.debug("IceMiniHead[0]: %s",
base64.b64decode(tmpMeta['IceMiniHead']).decode('utf-8'))
# Extract some indices for the images
slice = [img.slice for img in images]
phase = [img.phase for img in images]
# Process each group of venc directions separately
unique_venc_dir = np.unique([
ismrmrd.Meta.deserialize(img.attribute_string)['FlowDirDisplay']
for img in images
])
# Measure processing time
toc = perf_counter()
strProcessTime = "Total processing time: %.2f ms" % ((toc - tic) * 1000.0)
logging.info(strProcessTime)
# Send this as a text message back to the client
connection.send_logging(constants.MRD_LOGGING_INFO, strProcessTime)
# Start the phase images at series 10. When interpreted by FIRE, images
# with the same image_series_index are kept in the same series, but the
# absolute series number isn't used and can be arbitrary
last_series = 10
imagesOut = []
for venc_dir in unique_venc_dir:
# data array has dimensions [row col sli phs], i.e. [y x sli phs]
# info lists has dimensions [sli phs]
data = np.zeros((images[0].data.shape[2], images[0].data.shape[3],
max(slice) + 1, max(phase) + 1), images[0].data.dtype)
head = [[None] * (max(phase) + 1) for _ in range(max(slice) + 1)]
meta = [[None] * (max(phase) + 1) for _ in range(max(slice) + 1)]
for img, sli, phs in zip(images, slice, phase):
if ismrmrd.Meta.deserialize(
img.attribute_string)['FlowDirDisplay'] == venc_dir:
# print("sli phs", sli, phs)
data[:, :, sli, phs] = img.data
head[sli][phs] = img.getHead()
meta[sli][phs] = ismrmrd.Meta.deserialize(img.attribute_string)
logging.debug("Phase data with venc encoding %s is size %s" % (
venc_dir,
data.shape,
))
np.save(debugFolder + "/" + "data_" + venc_dir + ".npy", data)
# Mask out data with high mean temporal diff
threshold = 250
data_meandiff = np.mean(np.abs(np.diff(data, 3)), 3)
data_masked = data
data_masked[(data_meandiff > threshold)] = 2048
np.save(debugFolder + "/" + "data_masked_" + venc_dir + ".npy",
data_masked)
# Normalize and convert to int16
data_masked = (data_masked.astype(np.float64) - 2048) * 32767 / 2048
data_masked = np.around(data_masked).astype(np.int16)
# Re-slice back into 2D images
for sli in range(data_masked.shape[2]):
for phs in range(data_masked.shape[3]):
# Create new MRD instance for the processed image
# NOTE: from_array() takes input data as [x y z coil], which is
# different than the internal representation in the "data" field as
# [coil z y x], so we need to transpose
tmpImg = ismrmrd.Image.from_array(data_masked[..., sli,
phs].transpose())
# Set the header information
tmpHead = head[sli][phs]
tmpHead.data_type = tmpImg.getHead().data_type
tmpHead.image_index = phs + sli * data_masked.shape[3]
tmpHead.image_series_index = last_series
tmpImg.setHead(tmpHead)
# Set ISMRMRD Meta Attributes
tmpMeta = meta[sli][phs]
tmpMeta['DataRole'] = 'Image'
tmpMeta['ImageProcessingHistory'] = ['FIRE', 'PYTHON']
tmpMeta['WindowCenter'] = '16384'
tmpMeta['WindowWidth'] = '32768'
tmpMeta['Keep_image_geometry'] = 1
# Add image orientation directions to MetaAttributes if not already present
if tmpMeta.get('ImageRowDir') is None:
tmpMeta['ImageRowDir'] = [
"{:.18f}".format(tmpHead.read_dir[0]),
"{:.18f}".format(tmpHead.read_dir[1]),
"{:.18f}".format(tmpHead.read_dir[2])
]
if tmpMeta.get('ImageColumnDir') is None:
tmpMeta['ImageColumnDir'] = [
"{:.18f}".format(tmpHead.phase_dir[0]),
"{:.18f}".format(tmpHead.phase_dir[1]),
"{:.18f}".format(tmpHead.phase_dir[2])
]
xml = tmpMeta.serialize()
logging.debug("Image MetaAttributes: %s", xml)
tmpImg.attribute_string = xml
imagesOut.append(tmpImg)
last_series += 1
return imagesOut
def process_image(images, config, metadata):
# Create folder, if necessary
if not os.path.exists(debugFolder):
os.makedirs(debugFolder)
logging.debug("Created folder " + debugFolder +
" for debug output files")
logging.debug("Processing data with %d images of type %s", len(images),
ismrmrd.get_dtype_from_data_type(images[0].data_type))
# Extract image data into a 5D array of size [img cha z y x]
data = np.stack([img.data for img in images])
head = [img.getHead() for img in images]
meta = [ismrmrd.Meta.deserialize(img.attribute_string) for img in images]
# Reformat data to the more intuitive [x y z cha img]
data = data.transpose()
# Reformat data again to [y x z cha img], i.e. [row col] for the first two
# dimensions. Note we will need to undo this later prior to sending back
# to the client
data = data.transpose((1, 0, 2, 3, 4))
# Display MetaAttributes for first image
logging.debug("MetaAttributes[0]: %s", ismrmrd.Meta.serialize(meta[0]))
# Optional serialization of ICE MiniHeader
if 'IceMiniHead' in meta[0]:
logging.debug("IceMiniHead[0]: %s",
base64.b64decode(meta[0]['IceMiniHead']).decode('utf-8'))
logging.debug("Original image data is size %s" % (data.shape, ))
np.save(debugFolder + "/" + "imgOrig.npy", data)
if data.shape[3] != 1:
logging.error("Multi-channel data is not supported")
return []
# Normalize to (0.0, 1.0) as expected by get_cmap()
data = data.astype(float)
data -= data.min()
data *= 1 / data.max()
# Apply colormap
cmap = plt.get_cmap('jet')
rgb = cmap(data)
# Remove alpha channel
# Resulting shape is [row col z rgb img]
rgb = rgb[..., 0:-1]
rgb = rgb.transpose((0, 1, 2, 5, 4, 3))
rgb = np.squeeze(rgb, 5)
# MRD RGB images must be uint16 in range (0, 255)
rgb *= 255
data = rgb.astype(np.uint16)
np.save(debugFolder + "/" + "imgRGB.npy", data)
# Reformat data from [row col z cha img] back to [x y z cha img] before sending back to client
data = data.transpose((1, 0, 2, 3, 4))
currentSeries = 0
# Re-slice back into 2D images
imagesOut = [None] * data.shape[-1]
for iImg in range(data.shape[-1]):
# Create new MRD instance for the new image
# NOTE: from_array() takes input data as [x y z coil], which is
# different than the internal representation in the "data" field as
# [coil z y x]. However, we already transposed this data when
# extracting it earlier.
imagesOut[iImg] = ismrmrd.Image.from_array(data[..., iImg])
data_type = imagesOut[iImg].data_type
# Create a copy of the original fixed header and update the data_type
# (we changed it to int16 from all other types)
oldHeader = head[iImg]
oldHeader.data_type = data_type
# Set RGB parameters
oldHeader.image_type = 6 # To be defined as ismrmrd.IMTYPE_RGB
oldHeader.channels = 3 # RGB "channels". This is set by from_array, but need to be explicit as we're copying the old header instead
# Increment series number when flag detected (i.e. follow ICE logic for splitting series)
if mrdhelper.get_meta_value(meta[iImg], 'IceMiniHead') is not None:
if mrdhelper.extract_minihead_bool_param(
base64.b64decode(
meta[iImg]['IceMiniHead']).decode('utf-8'),
'BIsSeriesEnd') is True:
currentSeries += 1
imagesOut[iImg].setHead(oldHeader)
# Create a copy of the original ISMRMRD Meta attributes and update
tmpMeta = meta[iImg]
tmpMeta['DataRole'] = 'Image'
tmpMeta['ImageProcessingHistory'] = ['PYTHON', 'RGB']
tmpMeta['SequenceDescriptionAdditional'] = 'FIRE_RGB'
tmpMeta['Keep_image_geometry'] = 1
# Add image orientation directions to MetaAttributes if not already present
if tmpMeta.get('ImageRowDir') is None:
tmpMeta['ImageRowDir'] = [
"{:.18f}".format(oldHeader.read_dir[0]),
"{:.18f}".format(oldHeader.read_dir[1]),
"{:.18f}".format(oldHeader.read_dir[2])
]
if tmpMeta.get('ImageColumnDir') is None:
tmpMeta['ImageColumnDir'] = [
"{:.18f}".format(oldHeader.phase_dir[0]),
"{:.18f}".format(oldHeader.phase_dir[1]),
"{:.18f}".format(oldHeader.phase_dir[2])
]
metaXml = tmpMeta.serialize()
logging.debug("Image MetaAttributes: %s",
xml.dom.minidom.parseString(metaXml).toprettyxml())
logging.debug("Image data has %d elements", imagesOut[iImg].data.size)
imagesOut[iImg].attribute_string = metaXml
return imagesOut
