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Attempt to handle TOF data with nonTOF multfactors #114

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Attempt to handle TOF data with nonTOF multfactors #114

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194c5a8
use SensitivityModel for multiplication to cope with TOF
KrisThielemans Sep 24, 2024
e915a4f
cope with non-TOF multfactors for TOF data
KrisThielemans Sep 25, 2024
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5 changes: 4 additions & 1 deletion SIRF_data_preparation/create_initial_images.py
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Expand Up @@ -51,7 +51,10 @@ def scale_initial_image(acquired_data, additive_term, mult_factors, template_ima

WARNING: assumes that obj_fun has been set_up already
"""
data_sum = (acquired_data.sum() - (additive_term * mult_factors).sum())
sensitivity_factors = STIR.AcquisitionSensitivityModel(mult_factors)
sensitivity_factors.set_up(acquired_data)
background = sensitivity_factors.forward(additive_term)
data_sum = acquired_data.sum() - background.sum()
if data_sum <= 0 or math.isinf(data_sum) or math.isnan(data_sum):
raise ValueError("Something wrong with input data. Sum of (prompts-background) is negative:"
f" sum prompts: {acquired_data.sum()}, sum corrected: {data_sum}")
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4 changes: 3 additions & 1 deletion SIRF_data_preparation/data_QC.py
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Expand Up @@ -178,7 +178,9 @@ def main(argv=None):
acquired_data = STIR.AcquisitionData(os.path.join(srcdir, 'prompts.hs'))
additive_term = STIR.AcquisitionData(os.path.join(srcdir, 'additive_term.hs'))
mult_factors = STIR.AcquisitionData(os.path.join(srcdir, 'mult_factors.hs'))
background = additive_term * mult_factors
sensitivity_factors = STIR.AcquisitionSensitivityModel(mult_factors)
sensitivity_factors.set_up(acquired_data)
background = sensitivity_factors.forward(additive_term)
plot_sinogram_profile(acquired_data, background, srcdir=srcdir)

OSEM_image = plot_image_if_exists(os.path.join(srcdir, 'OSEM_image'), **slices)
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11 changes: 9 additions & 2 deletions main_OSEM.py
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Expand Up @@ -63,9 +63,16 @@ def __init__(self, data: Dataset, num_subsets: int = 7, update_objective_interva

acquisition_model_subset = STIR.AcquisitionModelUsingParallelproj()
acquisition_model_subset.set_additive_term(additive_term_subset)
acquisition_model_subset.set_up(prompts_subset, self.x)
if prompts_subset.dimensions()[0] > 1 and multiplicative_factors_subset.dimensions()[0] == 1:
# non-TOF multiplicative factors for TOF data
acquisition_model_subset.set_up(multiplicative_factors_subset, self.x)
subset_sensitivity = acquisition_model_subset.backward(multiplicative_factors_subset)
acquisition_model_subset.set_up(prompts_subset, self.x)
else:
assert prompts_subset.dimensions()[0] == multiplicative_factors_subset.dimensions()[0]
acquisition_model_subset.set_up(prompts_subset, self.x)
subset_sensitivity = acquisition_model_subset.backward(multiplicative_factors_subset)

subset_sensitivity = acquisition_model_subset.backward(multiplicative_factors_subset)
# add a small number to avoid NaN in division
subset_sensitivity += subset_sensitivity.max() * 1e-6

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