Simulation of PET data
General idea is that with a flawless input function (plasma and/or blood curves), comprehensive compartmental model, and certain set of model parameters, a flawless tissue curve can be calculated (which is also called simulation). Different analysis methods can then be tested with the simulated tissue data, trying to reproduce the applied model parameters, or at least the physiologically most relevant parameter.
Measurement noise can be added to the simulated tissue and input data to study noise-induced biases and to compare the noise-sensitivity of analysis methods.
As a rule the steps are:
- Creating representative input data
- Determining the simulation model parameters
- Simulation of tissue curves using compartmental model
- Simulation of PET time frames
- Simulation of measurement noise
- Analysis of created datasets with the methods to be tested
Dynamic PET images can be simulated as well. Regions of interest in PET image can be filled with the tissue curves from the steps above, and noise can then be added to the dynamic image.
The performance of PET scanners and reconstruction algorithms can be simulated by using mathematical phantoms of the body or organs (usually brain or heart), where predefined regions are filled with flawless concentration curves, and which then can generate dynamic PET sinograms or images for further analysis.
Physical phantoms with pumps and tubing (representing circulation) and containers (representing certain organs) have also been developed and used to measure the performance of PET scanners, reconstruction, and analysis tools.
Created at: 2010-09-20
Updated at: 2017-11-14
Written by: Vesa Oikonen