def process(self, recondata):
print(np.shape(recondata[0].data.data))
image = cifftn(recondata[0].data.data, axes=(0, 1, 2))
image = np.reshape(
image,
(image.shape[0], image.shape[1], image.shape[2], image.shape[3]))
#Create a new image header and transfer value
acq = np.ravel(recondata[0].data.headers)[0]
img_head = ismrmrd.ImageHeader()
img_head.channels = acq.active_channels
img_head.slice = acq.idx.slice
img_head.matrix_size = (image.shape[0], image.shape[1], image.shape[2])
img_head.position = acq.position
img_head.read_dir = acq.read_dir
img_head.phase_dir = acq.phase_dir
img_head.slice_dir = acq.slice_dir
img_head.patient_table_position = acq.patient_table_position
img_head.acquisition_time_stamp = acq.acquisition_time_stamp
img_head.image_index = 0
img_head.image_series_index = 0
img_head.data_type = ismrmrd.DATATYPE_CXFLOAT
#Return image to Gadgetron
self.put_next(img_head, image)
print("Slice ", img_head.slice)
print("----------------------------------------------")
return 0
def crop_acquisition(acquisition):
x_space = cifftn(acquisition.data, axes=[1])
x_space = x_space[:, x0:x1]
acquisition.resize(number_of_samples=x_space.shape[1],
active_channels=x_space.shape[0])
acquisition.center_sample = recon_space.x // 2
acquisition.data[:] = cfftn(x_space, axes=[1])
return acquisition
def process(self, acq, data, *args):
if self.myBuffer is None:
channels = acq.active_channels
if self.enc.encodingLimits.slice != None:
nslices = self.enc.encodingLimits.slice.maximum + 1
else:
nslices = 1
eNz = self.enc.encodedSpace.matrixSize.z
eNy = self.enc.encodedSpace.matrixSize.y
eNx = self.enc.encodedSpace.matrixSize.x
self.myBuffer = np.zeros(
(int(eNx / 2), eNy, eNz, nslices, channels),
dtype=np.complex64)
line_offset = self.enc.encodedSpace.matrixSize.y / 2 - self.enc.encodingLimits.kspace_encoding_step_1.center
self.myBuffer[:,
int(acq.idx.kspace_encode_step_1 + line_offset),
int(acq.idx.kspace_encode_step_2),
int(acq.idx.slice), :] = data
if (acq.flags & (1 << 7)): #Is this the last scan in slice
image = cifftn(self.myBuffer, axes=(0, 1, 2))
image = image * np.product(
image.shape) * 100 #Scaling for the scanner
#Create a new image header and transfer value
img_head = ismrmrd.ImageHeader()
img_head.channels = acq.active_channels
img_head.slice = acq.idx.slice
img_head.matrix_size[0] = self.myBuffer.shape[0]
img_head.matrix_size[1] = self.myBuffer.shape[1]
img_head.matrix_size[2] = self.myBuffer.shape[2]
img_head.position = acq.position
img_head.read_dir = acq.read_dir
img_head.phase_dir = acq.phase_dir
img_head.slice_dir = acq.slice_dir
img_head.patient_table_position = acq.patient_table_position
img_head.acquisition_time_stamp = acq.acquisition_time_stamp
img_head.image_index = self.myCounter
img_head.image_series_index = self.mySeries
img_head.data_type = ismrmrd.DATATYPE_CXFLOAT
self.myCounter += 1
if self.myCounter > 5:
self.mySeries += 1
self.myCounter = 1
#Return image to Gadgetron
self.put_next(img_head, image.astype('complex64'), *args)
#print "Returning to Gadgetron"
return 0 #Everything OK
def process(self, acq, data):
orig_size = list(data.shape)
data2 = data.reshape([data.shape[0], int(data.size / data.shape[0])])
new_length = data2.shape[0] // 2
data2 = cfftn(cifftn(data2, axes=[
0
])[(0 + (new_length // 2)):(new_length + (new_length // 2)), :],
axes=[0])
orig_size[0] = new_length
data2.reshape(tuple(orig_size))
acq.samples = new_length
self.put_next(acq, data2)
return 0
def process(self, recondata):
print(np.shape(recondata[0].data.data))
image = cifftn(recondata[0].data.data, axes=(0, 1, 2))
image = np.reshape(
image,
(image.shape[0], image.shape[1], image.shape[2], image.shape[3]))
#Create a new image header and transfer value
acq = np.ravel(recondata[0].data.headers)[0]
img_head = ismrmrd.ImageHeader()
img_head.version = 1
img_head.measurement_uid = acq.measurement_uid
img_head.channels = acq.active_channels
img_head.slice = acq.idx.slice
img_head.matrix_size = (image.shape[0], image.shape[1], image.shape[2])
img_head.field_of_view = (self.enc.reconSpace.fieldOfView_mm.x,
self.enc.reconSpace.fieldOfView_mm.y,
self.enc.reconSpace.fieldOfView_mm.z)
img_head.position = acq.position
img_head.read_dir = acq.read_dir
img_head.phase_dir = acq.phase_dir
img_head.slice_dir = acq.slice_dir
img_head.patient_table_position = acq.patient_table_position
img_head.acquisition_time_stamp = acq.acquisition_time_stamp
img_head.average = acq.idx.average
img_head.slice = acq.idx.slice
img_head.contrast = acq.idx.contrast
img_head.phase = acq.idx.phase
img_head.repetition = acq.idx.repetition
img_head.set = acq.idx.set
img_head.image_index = next(self.image_indices)
img_head.image_series_index = 0
img_head.data_type = ismrmrd.DATATYPE_CXFLOAT
#Return image to Gadgetron
self.put_next(img_head, image)
print("Slice ", img_head.slice)
print("----------------------------------------------")
return 0
def reconstruct_image(kspace_data):
# Reconstruction is an inverse fft in this case.
return cifftn(kspace_data, axes=[1, 2, 3])