title: Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance.
abstract: OBJECTIVE
  Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridmantis, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webmantis and visualmantis to facilitate the setup of computational experiments via hybridmantis.
  
  
  METHODS
  The visualization tools visualmantis and webmantis enable the user to control simulation properties through a user interface. In the case of webmantis, control via a web browser allows access through mobile devices such as smartphones or tablets. webmantis acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel.
  
  
  RESULTS
  The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridmantis. The users can download the output images and statistics through a zip file for future reference. In addition, webmantis provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons.
  
  
  CONCLUSIONS
  The visualization tools visualmantis and webmantis provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.
authors.affiliation: Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993.
authors.forename: Han
authors.initials: H
authors.lastname: Dong
authors.affiliation: Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993.
authors.forename: Diksha
authors.initials: D
authors.lastname: Sharma
authors.affiliation: Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993.
authors.forename: Aldo
authors.initials: A
authors.lastname: Badano
mesh_terms: D040421:Cell Phone; D003196:Computer Graphics; D003198:Computer Simulation; D003201:Computers; D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods*; D004583:Electrons; D056228:Feedback, Sensory; D020407:Internet; D009010:Monte Carlo Method; D061848:Optical Imaging / Q000295:instrumentation / Q000379:methods; D017785:Photons; D011859:Radiography / Q000295:instrumentation / Q000379:methods; D014584:User-Computer Interface