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Performance evaluation of 18 F radioluminescence microscopy using computational simulation
Author(s) -
Wang Qian,
Sengupta Debanti,
Kim Tae Jin,
Pratx Guillem
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12198
Subject(s) - radioluminescence , dosimetry , materials science , radiochemistry , nuclear medicine , nuclear physics , optics , physics , chemistry , medicine , detector , scintillation
Purpose Radioluminescence microscopy can visualize the distribution of beta‐emitting radiotracers in live single cells with high resolution. Here, we perform a computational simulation of 18 F positron imaging using this modality to better understand how radioluminescence signals are formed and to assist in optimizing the experimental setup and image processing. Methods First, the transport of charged particles through the cell and scintillator and the resulting scintillation is modeled using the GEANT4 Monte‐Carlo simulation. Then, the propagation of the scintillation light through the microscope is modeled by a convolution with a depth‐dependent point‐spread function, which models the microscope response. Finally, the physical measurement of the scintillation light using an electron‐multiplying charge‐coupled device (EMCCD) camera is modeled using a stochastic numerical photosensor model, which accounts for various sources of noise. The simulated output of the EMCCD camera is further processed using our ORBIT image reconstruction methodology to evaluate the endpoint images. Results The EMCCD camera model was validated against experimentally acquired images and the simulated noise, as measured by the standard deviation of a blank image, was found to be accurate within 2% of the actual detection. Furthermore, point source simulations found that a reconstructed spatial resolution of 18.5 μm can be achieved near the scintillator. As the source is moved away from the scintillator, spatial resolution degrades at a rate of 3.5 μm per μm distance. These results agree well with the experimentally measured spatial resolution of 30–40 μm (live cells). The simulation also shows that the system sensitivity is 26.5%, which is also consistent with our previous experiments. Finally, an image of a simulated sparse set of single cells is visually similar to the measured cell image. Conclusions Our simulation methodology agrees with experimental measurements taken with radioluminescence microscopy. This in silico approach can be used to guide further instrumentation developments and to provide a framework for improving image reconstruction.