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In silico optimization of radioluminescence microscopy.

Qian Wang1, Debanti Sengupta1, Tae Jin Kim1

  • 1Department of Radiation Oncology, Stanford University, California.

Journal of Biophotonics
|September 26, 2017
PubMed
Summary

This study optimized radioluminescence microscopy (RLM) for live cell imaging. Simulations identified optimal parameters, achieving ~20 μm resolution and 40% sensitivity for robust cellular process imaging with radiotracers.

Keywords:
Lu2O3 thin film scintillatorcomputational simulationimaging system optimizationradioluminescence microscopy

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Area of Science:

  • Cellular imaging
  • Biophysics
  • Radiochemistry

Background:

  • Radioluminescence microscopy (RLM) offers high-resolution imaging of radionuclide uptake in live cells.
  • Current RLM systems provide adequate resolution and sensitivity but lack systematic optimization.

Purpose of the Study:

  • To computationally optimize RLM system parameters for enhanced image quality.
  • To determine the optimal scintillator material and thickness for RLM.

Main Methods:

  • Utilized Monte-Carlo simulations for radiation transport.
  • Employed a 3D optical point-spread function for microscope modeling.
  • Incorporated a stochastic photosensor model for the EMCCD camera.

Main Results:

  • Lu2O3:Eu demonstrated superior performance among five scintillator materials.
  • An 8 μm scintillator thickness achieved the best balance between spatial resolution and sensitivity.
  • Achieved ~20 μm spatial resolution and 40% sensitivity across magnifications with adjusted pixel binning and EM gain.

Conclusions:

  • Optimal RLM performance was estimated through computational simulation.
  • The findings guide further development for robust cellular imaging using radiotracers.
  • Magnification choice should align with desired field of view for concurrent optical microscopy.