def correct_lors(lors,
fov_center,
fov_size,
lyso_center0,
lyso_size0,
lyso_center1,
lyso_size1,
weights=None,
atten_coef=0.0096,
lyso_atten_coef=0.087,
system_shape=miil.default_system_shape,
correct_lyso_atten=True,
correct_uniform_atten=True,
packing_frac=default_packing_frac):
vec = miil.create_sparse_column_vector(lors)
if correct_uniform_atten or correct_lyso_atten:
line_start, line_end = miil.get_lor_positions(vec.indices,
system_shape)
if correct_uniform_atten:
fov_length = voxel_intersection_length(line_start, line_end,
fov_center, fov_size)
vec.data *= np.squeeze(np.exp(atten_coef * fov_length))
if correct_lyso_atten:
vec.data *= lyso_atten_weight(lors, lyso_center0, lyso_size0,
lyso_center1, lyso_size1,
lyso_atten_coef, packing_frac)
if weights is not None:
crystal0, crystal1 = miil.lor_to_crystals(vec.indices,
miil.default_system_shape)
vec.data *= weights[crystal0]
vec.data *= weights[crystal1]
return vec
def test_back_project_multiple_subsets(self):
image_shape = (324, 128, 68)
voxel_size_mm = 0.5
sigma = voxel_size_mm
model = miil.recon.BreastPETSystemMatrix(image_shape,
voxel_size_mm=voxel_size_mm,
sigma=sigma,
tor_width=1)
lors = np.array(2 * (855655487, ),
dtype=np.int64) # LOR straight across FOV
x_mm = voxel_size_mm * (np.arange(0, image_shape[0]) -
image_shape[0] / 2 + 0.5)
z_mm = voxel_size_mm * (np.arange(0, image_shape[2]) -
image_shape[2] / 2 + 0.5)
pos0, pos1 = miil.get_lor_positions(lors)
ref = np.ones(image_shape, dtype=np.float32)
d2 = ((pos0[0,0] - x_mm[:, None, None]) / voxel_size_mm) ** 2 + \
((pos0[0,2] - z_mm[None, None, :]) / voxel_size_mm) ** 2
d2_exp = np.exp(-d2 * sigma)
d2_exp[d2 > 1.0] = 0
ref *= d2_exp
# Double the reference for the two of the same lors
ref *= 2
# split the two lors into two subsets.
val = model.back_project(lors, subset_size=1)
assert (((val == 0) == (ref == 0)).all())
assert ((np.abs(val - ref) < 1e-5).all())
def test_back_project_with_slor_weights(self):
image_shape = (324, 128, 68)
voxel_size_mm = 0.5
sigma = voxel_size_mm
model = miil.recon.BreastPETSystemMatrix(image_shape,
voxel_size_mm=voxel_size_mm,
sigma=sigma,
tor_width=1)
weight_val = 2.0
slor_weights = weight_val * np.ones(miil.no_slors())
lors = np.array((855655487, ),
dtype=np.int64) # LOR straight across FOV
val = model._back_project(lors, slor_weights=slor_weights)
x_mm = voxel_size_mm * (np.arange(0, image_shape[0]) -
image_shape[0] / 2 + 0.5)
z_mm = voxel_size_mm * (np.arange(0, image_shape[2]) -
image_shape[2] / 2 + 0.5)
pos0, pos1 = miil.get_lor_positions(lors)
ref = np.ones(image_shape, dtype=np.float32)
d2 = ((pos0[0,0] - x_mm[:, None, None]) / voxel_size_mm) ** 2 + \
((pos0[0,2] - z_mm[None, None, :]) / voxel_size_mm) ** 2
d2_exp = np.exp(-d2 * sigma)
d2_exp[d2 > 1.0] = 0
ref *= d2_exp
ref *= weight_val
assert (((val == 0) == (ref == 0)).all())
assert ((np.abs(val - ref) < 1e-5).all())
val = model.back_project(lors, slor_weights=slor_weights)
assert (((val == 0) == (ref == 0)).all())
assert ((np.abs(val - ref) < 1e-5).all())
def lyso_length(lors,
lyso_center0=default_panel0_lyso_center,
lyso_size0=default_lyso_size,
lyso_center1=default_panel1_lyso_center,
lyso_size1=default_lyso_size,
system_shape=miil.default_system_shape):
line_start, line_end = miil.get_lor_positions(lors, system_shape)
length = \
voxel_intersection_length(
line_start, line_end, lyso_center0, lyso_size0) + \
voxel_intersection_length(
line_start, line_end, lyso_center1, lyso_size1)
return length
def uniform_phantom_nonuniform_illum_weight(
lors,
fov_center=default_fov_center,
fov_size=default_fov_size,
ref_length=None,
system_shape=miil.default_system_shape):
# If the refence length is not specified, then assume the width of the FOV
# in Y.
if ref_length is None:
ref_length = fov_size[1]
line_start, line_end = miil.get_lor_positions(lors, system_shape)
fov_length = voxel_intersection_length(line_start, line_end, fov_center,
fov_size)
weight = fov_length / ref_length
return weight
def correct_uniform_phantom_lors(lors,
fov_center,
fov_size,
lyso_center0,
lyso_size0,
lyso_center1,
lyso_size1,
atten_coef=0.0096,
lyso_atten_coef=0.087,
system_shape=miil.default_system_shape,
packing_frac=default_packing_frac):
vec = miil.create_sparse_column_vector(lors)
line_start, line_end = miil.get_lor_positions(vec.indices, system_shape)
fov_length = voxel_intersection_length(line_start, line_end, fov_center,
fov_size)
vec.data *= np.squeeze(np.exp(atten_coef * fov_length) / fov_length)
vec.data *= lyso_atten_weight(lors, lyso_center0, lyso_size0, lyso_center1,
lyso_size1, lyso_atten_coef, packing_frac)
return vec
