Biobeam
Biobeam performs wave-optical simulations of light-sheet microscopy systems using multiplexed GPU-accelerated point-spread-function calculations to model optical image formation.
Key Features:
- Multiplexed Point-Spread-Function Calculations: Performs large-scale PSF calculations ranging from 105 to 106 operations for accurate simulation of light-sheet microscopy imaging.
- GPU-Accelerated Computation: Utilizes GPU acceleration to efficiently execute computationally intensive wave-optical simulations.
- Wave-Optical Image Formation Modeling: Simulates optical phenomena in light-sheet microscopy including spatially varying aberrations, diffraction artifacts, and geometric distortions.
- Adaptive Optics Simulation: Supports simulation of adaptive optics systems to evaluate correction of optical aberrations.
- Emergent Wave-Optical Phenomena Modeling: Simulates complex wave-optical interactions arising during image formation in light-sheet microscopy systems.
Scientific Applications:
- Light-Sheet Microscope Design Optimization: Enables computational evaluation of optical configurations to predict and mitigate image degradation in microscope systems.
- Microscopy Image Quality Analysis: Supports analysis of aberrations and optical artifacts affecting image resolution and clarity.
- Wave-Optical Phenomena Investigation: Facilitates study of diffraction, aberration, and optical interaction effects in advanced microscopy systems.
Methodology:
Biobeam simulates light-sheet microscopy image formation through multiplexed point-spread-function calculations implemented with GPU acceleration and wave-optical modeling of optical aberrations, diffraction effects, and adaptive optics corrections.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Python
- Added:
- 7/7/2018
- Last Updated:
- 11/25/2024
Operations
Publications
Weigert M, Subramanian K, Bundschuh ST, Myers EW, Kreysing M. Biobeam—Multiplexed wave-optical simulations of light-sheet microscopy. PLOS Computational Biology. 2018;14(4):e1006079. doi:10.1371/journal.pcbi.1006079. PMID:29652879. PMCID:PMC5898703.
PMID: 29652879
PMCID: PMC5898703
Funding: - Bundesministerium für Bildung und Forschung (DE): SYSBIO II