Analytic simulation of petsird v0.2 data using parallelproj

Create your conda / mamba environment

conda env create -f environment.yml
conda activate petsird-analytic-simulator
cd python

Simulate petsird LM data

python 01_analytic_petsird_lm_simulator.py

The simulation script creates a binary petsird LM file, but also many other files (e.g. a reference sensitivity image) that are all stored in the output directory

tree --charset=ascii my_lm_sim

my_lm_sim
|-- reference_histogram_mlem_50_epochs.npy
|-- reference_sensitivity_image.npy
|-- scanner_geometry.png
|-- sim_parameters.json
|-- simulated_lm_file.bin
`-- tof_profile_and_sensitivity_image.png

The simulation can be customized in many ways (number of counts to simulate, uniform or non-uniform efficiencies ...) via command line options.

These option can be listed via

python 01_analytic_petsird_lm_simulator.py -h

Note: The "reference" MLEM using histogrammed data is only run if a value > 0 is given via --num_epochs_mlem. Otherwise it is skipped to save time.

Run a listmode OSEM recon on the simulated

python 02_lm_osem_recon_simulated_data.py

Thes command line optione for the LM OSEM recon script can be listed via

python 02_lm_osem_recon_simulated_data.py -h