def bench_fanbeam_forward(phantom, det_count, num_angles, source_dist,
det_dist, warmup, repeats):
geometry = create_fanbeam_geometry(det_count, num_angles, source_dist,
det_dist)
f = lambda x: fan_projection2d(x, geometry)
return benchmark(f, phantom, warmup, repeats)
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 bench_fanbeam_backward(batch, size, det_count, num_angles, source_dist,
det_dist, det_spacing, *bench_args):
with tf.device('/GPU:0'):
phantom = tf.random.normal((batch, size, size))
geometry = create_fanbeam_geometry(size, det_count, num_angles,
source_dist, det_dist, det_spacing)
sino = fan_projection2d(phantom, geometry)
def f(x):
return fan_backprojection2d(x, geometry)
return benchmark(f, sino, *bench_args)
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 example_fan_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
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).astype(np.float32)
# Add required batch dimension
phantom = np.expand_dims(phantom, axis=0)
sino = fan_projection2d(phantom,geometry)
@tf.function
def test_func_proj(x):
return fan_projection2d(x,geometry)
@tf.function
def test_func_reco(x):
return fan_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 test_func_proj(x):
return fan_projection2d(x,geometry)
def f(x):
return fan_projection2d(x, geometry)
