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 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 get_test_data(number_of_samples=1):
data = np.empty((number_of_samples,) + tuple(GEOMETRY.sinogram_shape))
labels = np.empty((number_of_samples,) + tuple(GEOMETRY.volume_shape))
with tf.Session() as sess:
# get shepp logan 2d
if number_of_samples == 1:
labels[0] = shepp_logan.shepp_logan_enhanced(GEOMETRY.volume_shape)
data[0] = generate_sinogram(np.expand_dims(labels[0],axis=0), parallel_projection2d, GEOMETRY)
# every slice of shepp logan 3d with number_of_samples as Z-dimension as own image
else:
labels = shepp_logan.shepp_logan_3d((number_of_samples,) + tuple(GEOMETRY.sinogram_shape))
for i in range(number_of_samples):
data[i] = generate_sinogram_parallel_2d(np.expand_dims(labels[i], axis=0), GEOMETRY)
return data, labels
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')