In silico optimization of radioluminescence microscopy. Issue 3 (5th November 2017)

• Record Type: Journal Article
• Authors: Wang, Qian; Sengupta, Debanti; Kim, Tae Jin; Pratx, Guillem
• Is Part Of: Journal of biophotonics. Volume 11:Issue 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-11-05
• Subjects: computational simulation -- imaging system optimization -- Lu2O3 thin film scintillator -- radioluminescence microscopy; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201700138
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract : Radioluminescence microscopy (RLM) is a high‐resolution method for imaging radionuclide uptake in live cells within a fluorescence microscopy environment. Although RLM currently provides sufficient spatial resolution and sensitivity for cell imaging, it has not been systematically optimized. This study seeks to optimize the parameters of the system by computational simulation using a combination of numerical models for the system's various components: Monte‐Carlo simulation for radiation transport, 3D optical point‐spread function for the microscope, and stochastic photosensor model for the electron multiplying charge coupled device (EMCCD) camera. The relationship between key parameters and performance metrics relevant to image quality is examined. Results show that Lu2 O3 :Eu yields the best performance among 5 different scintillator materials, and a thickness: 8 μm can best balance spatial resolution and sensitivity. For this configuration, a spatial resolution of ~20 μm and sensitivity of 40% can be achieved for all 3 magnifications investigated, provided that the user adjusts pixel binning and electron multiplying (EM) gain accordingly. Hence the primary consideration for selecting the magnification should be the desired field of view and magnification for concurrent optical microscopy studies. In conclusion, this study estimates the optimal imaging performance achievable with RLM and promotes further development for more robust imaging of cellular processes using radiotracers. Abstract : Radioluminescence microscopy (RLM) is a new method for imaging radionuclide probes in a microscopy environment. This study uses a combination of numerical models for RLM to optimize its performance. How RLM performance metrics are affected by parameters of different system components is explored. With these relationships, we found efficiently the optimized parameters enhancing RLM performance (a comparison of radioluminescence cell images simulated using experimental and optimized parameters).