def example_parallel_2d():
# ------------------ Declare Parameters ------------------
# Volume Parameters:
volume_size = 64
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = 100
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 90
angular_range = np.pi
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape, detector_spacing, number_of_projections, angular_range)
geometry.set_trajectory(circular_trajectory.circular_trajectory_2d(geometry))
# Get Phantom
phantom = shepp_logan.shepp_logan_enhanced(volume_shape).astype(np.float32)
# Add required batch dimension
phantom = np.expand_dims(phantom, axis=0)
sino = parallel_projection2d(phantom,geometry)
@tf.function
def test_func_proj(x):
return parallel_projection2d(x,geometry)
@tf.function
def test_func_reco(x):
return parallel_backprojection2d(x,geometry)
proj_theoretical, proj_numerical = tf.test.compute_gradient(test_func_proj, [sino])
reco_theoretical, reco_numerical = tf.test.compute_gradient(test_func_reco, [sino])
def example_fan_2d_shortscan():
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = 500
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 250
angular_range = None # will get set to pi + 2 * fan_angle
source_detector_distance = 1200
source_isocenter_distance = 750
# Create Geometry class
geometry = GeometryFan2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range, source_detector_distance,
source_isocenter_distance)
geometry.angular_range = np.pi + 2 * geometry.fan_angle # fan_angle gets defined by sdd and detector_shape
geometry.set_trajectory(circular_trajectory_2d(geometry))
# Create Phantom
phantom = shepp_logan_enhanced(volume_shape)
# Add required batch dimension
phantom = np.expand_dims(phantom, axis=0)
# Build up Reconstruction Pipeline
# Create Sinogram of Phantom
sinogram = fan_projection2d(phantom, geometry)
# Redundancy Weighting: Create Weights Image and pointwise multiply
redundancy_weights = weights.parker_weights_2d(geometry)
sinogram_redun_weighted = sinogram * redundancy_weights
# Filtering: Create 2D Filter and pointwise multiply
reco_filter = filters.ram_lak_2D(geometry)
sino_freq = tf.signal.fft(
tf.cast(sinogram_redun_weighted, dtype=tf.complex64))
sino_filtered_freq = tf.multiply(sino_freq,
tf.cast(reco_filter, dtype=tf.complex64))
sinogram_filtered = tf.math.real(tf.signal.ifft(sino_filtered_freq))
# Final Backprojection
reco = fan_backprojection2d(sinogram_filtered, geometry)
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_fan_short_scan_reco.png',
dpi=150,
transparent=False,
bbox_inches='tight')
def example_fan_2d_shortscan():
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1,1]
# Detector Parameters:
detector_shape = 500
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 250
angular_range = None # will get set to pi + 2 * fan_angle
source_detector_distance = 1200
source_isocenter_distance = 750
# Create Geometry class
geometry = GeometryFan2D(volume_shape, volume_spacing,
detector_shape, detector_spacing,
number_of_projections, angular_range,
source_detector_distance, source_isocenter_distance)
geometry.angular_range = np.pi + 2*geometry.fan_angle # fan_angle gets defined by sdd and detector_shape
geometry.central_ray_vectors = circular_trajectory_2d(geometry)
# Create Phantom
phantom = shepp_logan_enhanced(volume_shape)
phantom = np.expand_dims(phantom,axis=0)
# Build up Reconstruction Pipeline
with tf.Session() as sess:
# Create Sinogram of Phantom
result = fan_projection2d(phantom, geometry)
sinogram = result.eval()
# Redundancy Weighting: Create Weights Image and pointwise multiply
redundancy_weights = weights.parker_weights_2d(geometry)
sinogram_redun_weighted = sinogram * redundancy_weights
# Filtering: Create 2D Filter and pointwise multiply
the_filter = filters.ram_lak_2D(geometry)
sino_fft = np.fft.fft(sinogram_redun_weighted, axis=-1)
sino_filtered_fft = np.multiply(sino_fft, the_filter)
sinogram_filtered = np.fft.ifft(sino_filtered_fft, axis=-1)
# Final Backprojection
result_back_proj = fan_backprojection2d(sinogram_filtered, geometry)
reco = result_back_proj.eval()
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_fan_short_scan_reco.png', dpi=150, transparent=False, bbox_inches='tight')
def example_fan_2d():
# ------------------ Declare Parameters ------------------
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = 800
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 360
angular_range = 2 * np.pi
source_detector_distance = 1200
source_isocenter_distance = 750
# create Geometry class
geometry = GeometryFan2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range, source_detector_distance,
source_isocenter_distance)
geometry.set_central_ray_vectors(
circular_trajectory.circular_trajectory_2d(geometry))
# Get Phantom
phantom = shepp_logan.shepp_logan_enhanced(volume_shape)
phantom = np.expand_dims(phantom, axis=0)
# ------------------ Call Layers ------------------
with tf.Session() as sess:
result = fan_projection2d(phantom, geometry)
sinogram = result.eval()
#TODO: Add Cosine weighting
#TODO: Add redundancy weighting for 360 degree
reco_filter = filters.ramp_2D(geometry)
sino_freq = np.fft.fft(sinogram, axis=-1)
sino_filtered_freq = np.multiply(sino_freq, reco_filter)
sinogram_filtered = np.fft.ifft(sino_filtered_freq, axis=-1)
result_back_proj = fan_backprojection2d(sinogram_filtered, geometry)
reco = result_back_proj.eval()
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_fan_reco.png',
dpi=150,
transparent=False,
bbox_inches='tight')
def example_parallel_2d():
# ------------------ Declare Parameters ------------------
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = 800
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 360
angular_range = 2 * np.pi
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range)
geometry.set_ray_vectors(
circular_trajectory.circular_trajectory_2d(geometry))
# Get Phantom
phantom = shepp_logan.shepp_logan_enhanced(volume_shape)
phantom = np.expand_dims(phantom, axis=0)
# ------------------ Call Layers ------------------
with tf.Session() as sess:
result = parallel_projection2d(phantom, geometry)
sinogram = result.eval()
#sinogram = sinogram + np.random.normal(
# loc=np.mean(np.abs(sinogram)), scale=np.std(sinogram), size=sinogram.shape) * 0.02
reco_filter = filters.ram_lak_2D(geometry)
sino_freq = np.fft.fft(sinogram, axis=1)
sino_filtered_freq = np.multiply(sino_freq, reco_filter)
sinogram_filtered = np.fft.ifft(sino_filtered_freq, axis=1)
result_back_proj = parallel_backprojection2d(sinogram_filtered,
geometry)
reco = result_back_proj.eval()
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_par_reco.png',
dpi=150,
transparent=False,
bbox_inches='tight')
def example_parallel_2d():
# ------------------ Declare Parameters ------------------
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = 800
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 360
angular_range = 2 * np.pi
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range)
geometry.set_trajectory(
circular_trajectory.circular_trajectory_2d(geometry))
# Get Phantom
phantom = shepp_logan.shepp_logan_enhanced(volume_shape)
# Add required batch dimension
phantom = np.expand_dims(phantom, axis=0)
# ------------------ Call Layers ------------------
sinogram = parallel_projection2d(phantom, geometry)
#sinogram = sinogram + np.random.normal(
# loc=np.mean(np.abs(sinogram)), scale=np.std(sinogram), size=sinogram.shape) * 0.02
reco_filter = filters.ram_lak_2D(geometry)
sino_freq = tf.signal.fft(tf.cast(sinogram, dtype=tf.complex64))
sino_filtered_freq = tf.multiply(sino_freq,
tf.cast(reco_filter, dtype=tf.complex64))
sinogram_filtered = tf.math.real(tf.signal.ifft(sino_filtered_freq))
reco = parallel_backprojection2d(sinogram_filtered, geometry)
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_par_reco.png',
dpi=150,
transparent=False,
bbox_inches='tight')
def iterative_reconstruction():
# ------------------ Declare Parameters ------------------
parser = argparse.ArgumentParser(description='')
parser.add_argument('--lr', dest='learning_rate', type=float, default=1e-2, help='initial learning rate for adam')
parser.add_argument('--epoch', dest='num_epochs', type=int, default=1000000, help='# of epoch')
args = parser.parse_args()
# Volume Parameters:
volume_size = 512
volume_shape = [volume_size, volume_size]
volume_spacing = [0.5, 0.5]
# Detector Parameters:
detector_shape = 625
detector_spacing = 0.5
# Trajectory Parameters:
number_of_projections = 30
angular_range = np.radians(200) # 200 * np.pi / 180
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape, detector_spacing, number_of_projections, angular_range)
geometry.set_ray_vectors(circular_trajectory.circular_trajectory_2d(geometry))
phantom = shepp_logan.shepp_logan_enhanced(volume_shape)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
# ------------------ Call Layers ------------------
with tf.Session(config=config) as sess:
acquired_sinogram = generate_sinogram(phantom,projection_2d.parallel_projection2d,geometry)
acquired_sinogram = acquired_sinogram + np.random.normal(
loc=np.mean(np.abs(acquired_sinogram)), scale=np.std(acquired_sinogram), size=acquired_sinogram.shape) * 0.02
zero_vector = np.zeros(np.shape(phantom), dtype=np.float32)
iter_pipeline = pipeline(sess, args, geometry)
iter_pipeline.train(zero_vector,np.asarray(acquired_sinogram))
plt.figure()
plt.imshow(iter_pipeline.result[0], cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('iter_tv_reco.png', dpi=150, transparent=False, bbox_inches='tight')
def create_parallel_geometry(det_count, num_angles):
# Detector Parameters:
detector_shape = det_count
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = num_angles
angular_range = np.pi
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range)
geometry.set_trajectory(
circular_trajectory.circular_trajectory_2d(geometry))
return geometry
def example_fan_2d():
# ------------------ Declare Parameters ------------------
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size, volume_size]
volume_spacing = [1,1]
# Detector Parameters:
detector_shape = 800
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = 360
angular_range = 2*np.pi
source_detector_distance = 1200
source_isocenter_distance = 750
# create Geometry class
geometry = GeometryFan2D(volume_shape, volume_spacing, detector_shape, detector_spacing, number_of_projections, angular_range, source_detector_distance, source_isocenter_distance)
geometry.set_trajectory(circular_trajectory.circular_trajectory_2d(geometry))
# Get Phantom
phantom = shepp_logan.shepp_logan_enhanced(volume_shape)
# Add required batch dimension
phantom = np.expand_dims(phantom,axis=0)
# ------------------ Call Layers ------------------
sinogram = fan_projection2d(phantom, geometry)
#TODO: Add Cosine weighting
#TODO: Add redundancy weighting for 360 degree
reco_filter = filters.ramp_2D(geometry)
sino_freq = tf.signal.fft(tf.cast(sinogram,dtype=tf.complex64))
sino_filtered_freq = tf.multiply(sino_freq,tf.cast(reco_filter,dtype=tf.complex64))
sinogram_filtered = tf.math.real(tf.signal.ifft(sino_filtered_freq))
reco = fan_backprojection2d(sinogram_filtered, geometry)
plt.figure()
plt.imshow(np.squeeze(reco), cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('2d_fan_reco.png', dpi=150, transparent=False, bbox_inches='tight')
def create_fanbeam_geometry(det_count, num_angles, source_dist, det_dist):
# Detector Parameters:
detector_shape = det_count
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = num_angles
angular_range = np.pi
source_detector_distance = source_dist + det_dist
# create Geometry class
geometry = GeometryFan2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range, source_detector_distance,
source_dist)
geometry.set_trajectory(
circular_trajectory.circular_trajectory_2d(geometry))
return geometry
def get_pyronn_geometry(radon_geometry: RadonGeometry):
# Volume Parameters:
volume_size = radon_geometry.volume_img_width
volume_shape = [volume_size, volume_size]
volume_spacing = [1, 1]
# Detector Parameters:
detector_shape = radon_geometry.projection_width
detector_spacing = 1
# Trajectory Parameters:
number_of_projections = radon_geometry.nr_projections
angular_range = np.radians(180.)
# create Geometry class
pyronn_geometry = GeometryParallel2D(volume_shape, volume_spacing,
detector_shape, detector_spacing,
number_of_projections, angular_range)
pyronn_geometry.set_trajectory(
circular_trajectory.circular_trajectory_2d(pyronn_geometry))
return pyronn_geometry
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_gen_log_dir = './logs/gradient_tape/' + current_time + '/generator_train'
train_dis_log_dir = './logs/gradient_tape/' + current_time + '/discriminator_train'
train_gen_summary_writer = tf.summary.create_file_writer(train_gen_log_dir)
train_dis_summary_writer = tf.summary.create_file_writer(train_dis_log_dir)
geometry_inpainted = GeometryParallel2D(volume_shape, volume_spacing,
detector_shape, detector_spacing,
number_of_projections_inpainted,
angular_range_inpainted)
geometry_inpainted.set_trajectory(
circular_trajectory.circular_trajectory_2d(geometry_inpainted))
dataset_paths = [x[2] for x in os.walk('./train_dataset')]
dataset_paths = [args.tfolder + s for s in dataset_paths[0]]
current_it = 0
for epoch in range(number_epochs):
for dataset_path in dataset_paths:
#Get and prepare training array
X = prepare_training_array(dataset_path, img_size)
for idx in range(X.shape[0]):
if idx == 0:
inpainted_sinogram_0 = parallel_projection2d(
np.expand_dims(X[0], 0), geometry_inpainted)
else:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from pyronn.ct_reconstruction.geometry.geometry_parallel_2d import GeometryParallel2D
from pyronn.ct_reconstruction.helpers.trajectories import circular_trajectory
"""
This file defines the Geometry parameters used by the whole model.
A GeometryParallel2D instance is provided to be used by everyone that needs it.
"""
# Declare Parameters
volume_shape = [256, 256]
volume_spacing = [1 , 1]
detector_shape = 400
detector_spacing = 1
number_of_projections = 180
angular_range = np.pi
# Create Geometry class instance
GEOMETRY = GeometryParallel2D(volume_shape, volume_spacing, detector_shape, detector_spacing, number_of_projections, angular_range)
GEOMETRY.set_trajectory(circular_trajectory.circular_trajectory_2d(GEOMETRY))
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from pyronn.ct_reconstruction.geometry.geometry_parallel_2d import GeometryParallel2D
from pyronn.ct_reconstruction.helpers.trajectories import circular_trajectory
"""
This file defines the Geometry parameters used by the hole model.
A GeometryParallel2D instance is provided to be used by everyone that needs it.
"""
# Declare Parameters
volume_shape = [256, 256]
volume_spacing = [0.5, 0.5]
detector_shape = 365
detector_spacing = 0.5
number_of_projections = 720
angular_range = 2 * np.pi
# Create Geometry class instance
GEOMETRY = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range)
GEOMETRY.set_ray_vectors(circular_trajectory.circular_trajectory_2d(GEOMETRY))
def iterative_reconstruction():
# ------------------ Declare Parameters ------------------
parser = argparse.ArgumentParser(description='')
parser.add_argument('--lr',
dest='learning_rate',
type=float,
default=1e-2,
help='initial learning rate for adam')
parser.add_argument('--epoch',
dest='num_epochs',
type=int,
default=100000,
help='# of epoch')
args = parser.parse_args()
# Volume Parameters:
volume_size = 256
volume_shape = [volume_size - 1, volume_size] #, volume_size+1]
volume_spacing = [1, 1] #, 1]
# Detector Parameters:
detector_shape = [375] #, 375]
detector_spacing = [1] #,1]
# Trajectory Parameters:
number_of_projections = 30
angular_range = np.radians(200) # 200 * np.pi / 180
# create Geometry class
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range) #, 1200.0, 750.0 )
matrices = circular_trajectory.circular_trajectory_2d(geometry)
geometry.set_trajectory(matrices)
phantom = shepp_logan.shepp_logan_enhanced(volume_shape).astype(
dtype=np.float32)
# Add required batch dimension
phantom = np.expand_dims(phantom, axis=0)
# ------------------ Call Layers ------------------
acquired_sinogram = generate_sinogram(phantom,
projection_2d.parallel_projection2d,
geometry)
# acquired_sinogram = acquired_sinogram + np.random.normal(
# loc=np.mean(np.abs(acquired_sinogram)), scale=np.std(acquired_sinogram), size=acquired_sinogram.shape) * 0.02
zero_vector = np.zeros(np.shape(phantom), dtype=np.float32)
iter_pipeline = pipeline(args, geometry)
iter_pipeline.train(zero_vector, np.asarray(acquired_sinogram))
plt.figure()
plt.imshow(np.squeeze(iter_pipeline.result),
cmap=plt.get_cmap('gist_gray'))
plt.axis('off')
plt.savefig('iter_tv_reco.png',
dpi=150,
transparent=False,
bbox_inches='tight')
train_gen_log_dir = './logs/gradient_tape/' + current_time + '/generator_train'
train_dis_log_dir = './logs/gradient_tape/' + current_time + '/discriminator_train'
if os.path.isdir('./logs/gradient_tape/'):
shutil.rmtree('./logs/gradient_tape/')
train_gen_summary_writer = tf.summary.create_file_writer(train_gen_log_dir)
train_dis_summary_writer = tf.summary.create_file_writer(train_dis_log_dir)
##Workable 2D, input 1 slice
geometry = GeometryParallel2D(volume_shape, volume_spacing, detector_shape,
detector_spacing, number_of_projections,
angular_range)
geometry.set_trajectory(circular_trajectory.circular_trajectory_2d(geometry))
geometry_inpainted = GeometryParallel2D(volume_shape, volume_spacing,
detector_shape, detector_spacing,
number_of_projections_inpainted,
angular_range_inpainted)
geometry_inpainted.set_trajectory(
circular_trajectory.circular_trajectory_2d(geometry_inpainted))
reco_filter = filters.ram_lak_2D(geometry)
epoch = 0
for dataset_path in [
args.recon,
]:
#['./test_dataset/series_5-area_0_SIRT30i_2n.h5', ]:# ['./test_dataset/20170710.h5', ] #['./test_dataset/clean4test.h5', ] ['./test_dataset/phantom_00016_recon.h5', ] ['./dataset/TIQ.h5', ]
