Real-time Deconvolution Microscope System
Microvolution® software delivers nearly instantaneous deconvolution by combining intelligent software programming with the power of a GPU
Developed by Stanford scientists, Microvolution software will improve your research:
Work more effectively in dim light and realize greater success rates with live-cell and time-lapse experiments
Create cleaner measurements after deconvolution (e.g., colocalization, FRET data, neurite lengths, fluorescence intensities)
Make adjustments to your microscopy experiments on the fly and collect more data from the same sample
The software deconvolves images from widefield, confocal, two photon, light sheet, and HCA microscopes.
Blind deconvolution option improves noisy data, such as deep tissue imaging. Multi-GPU options enable even giant images to be processed in seconds.
*Bruce MA, Butte MJ. Real-time GPU-based 3D Deconvolution, Optics Express, 2013; 21(4): 4766.
Deconvolve your fluorescence images, from small to very large, in a fraction of the time
Thin Filaments Are Preserved with Microvolution Software
Microvolution’s method starts with the proven Richardson-Lucy algorithm that is used by most software programs. Other vendors take mathematical shortcuts to speed up iterations, resulting in imprecise images after deconvolution. Microvolution takes no shortcuts. Our software delivers accurate images, up to 200 times faster.
Image courtesy of Molecular Devices.
When collected under the right conditions, deconvolution can help break the diffraction barrier. Pictured below are 180 nm separated lines on an Argo-SIM slide, imaged with widefield microscopy. Deconvolution brings a √2 improvement in visual resolution.
Consist of ;
Microvolution® Deconvolution Software
Delivers almost instantaneous deconvolution
Easy to use ImageJ plugin
Unlike other vendors, deconvolves your images with accuracy
Enables both 2-D and 3-D Deconvolution
Works on computers that have NVIDIA GPU boards incorporated into the computer hardware.
Blind Deconvolution Option
Algorithm modifies the PSF during the run to correct for extra aberrations
Ideal for deep tissue imaging and other noisy applications
Point spread functions are determined by computer for optimal imaging
Simplifies processing of large datasets, e.g. multi-channel timelapse experiments
Reduced memory needs for big image
Enables faster computation of very large images
Processes multiple channels or time points in parallel
Operates with 2 or more NVIDIA GPU cards.
Plugin for µManager 2.0 Option New!!
This plugin to µManager 2.0, enables hands-off deconvolution of your images as they are acquired.
Microscopy for Multi-positions, Multi-channels, Z-stacks and Time-series acquisition.
Olympus IX 83 Motorised with Z-focus Drive
XY Motorised Stages
Microscope Light Sources
Microscope Incubator for Live-cell Imaging
Objective Heater option
High-speed TTL Device Controller for hardware synchronization
and PC Workstation with Micro-Manager ver.2 Software
Microvolution has been cited in the following publications
Vogt, E.-J. et al. Anchoring cortical granules in the cortex ensures trafficking to the plasma membrane for post-fertilization exocytosis. Nature Communications 10, 2271 (2019) doi:10.1038/s41467-019-10171-7
Ozel, M.N. et al. Serial synapse formation through filopodial competition for synaptic seeding factors. Developmental Cell 50, 447-461.E8 (2019) doi:10.1016/j.devcel.2019.06.014
Kim, C., Seedorf, G.J., Abman, S.H., & Shepherd, D.P. High throughput imaging identifies a spatially localized response of primary fetal pulmonary artery endothelial cells to insulin-like growth factor 1 treatment. bioRxiv (preprint) , (2019) doi:10.1101/674499
Sancer, G. et al. Modality-specific circuits for skylight orientation in the fly visual system. Current Biology 29, 1-14 (2019) doi:10.1016/j.cub.2019.07.020
Mohan, A. et al. Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1-P29S Melanoma Cells. Developmental Cell 43, 444-460.e9 (2019) doi:10.1016/j.devcel.2019.04.007
Chakraborty, T. et al. Light-sheet microscopy with isotropic, sub-micron resolution and solvent-independent large-scale imaging. bioRxiv (preprint) , (2019) doi:10.1101/605493
Condon, N. et al. Macropinosome formation by tent pole ruffling in macrophages. Journal of Cell Biology 217, 3873-3885 (2018) doi:10.1083/jcb.201804137
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968-981 (2018) doi:10.1016/j.cell.2018.07.010
Wall, A., Condon, N., Luo, L., & Stow, J. Rab8a localisation and activation by Toll-like receptors on macrophage macropinosomes. Philosophical Transactions of the Royal Society B: Biological Sciences 374, (2018) doi:10.1098/rstb.2018.0151
Kashekodi, A., Meinert, T., Michiels, R. & Rohrbach, A. Miniature scanning light-sheet illumination implemented in a conventional microscope. Biomedical Optics Express 9, 4263-4274 (2018) doi:10.1364/BOE.9.004263
Kwak, B., Lee, Y., Lee, J., Lee, S. & Lim, J. Mass fabrication of uniform sized 3D tumor spheroid using high-throughput microfluidic system. Journal of Controlled Release 275, 201-207 (2018) doi:10.1016/j.jconrel.2018.02.029
Jin, E. et al. Live Observation of Two Parallel Membrane Degradation Pathways at Axon Terminals. Current Biology 28, 1027-1038.e4 (2018) doi:10.1016/j.cub.2018.02.032
Donnelly, S. K. et al. Rac3 regulates breast cancer invasion and metastasis by controlling adhesion and matrix degradation. Journal of Cell Biology (2017) doi:10.1083/jcb.201704048
Ryan, D. P. et al. Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy. Nature Communications 8, 612 (2017) doi:10.1038/s41467-017-00514-7
McNamara, G., Difilippantonio, M., Ried, T. & Bieber, F. R. in Current Protocols in Human Genetics 4.4.1-4.4.89 doi:10.1002/cphg.42
Singh, J., Nowlin, T., Seedorf, G., Abman, S., & Shepherd, D. Quantifying three-dimensional rodent retina vascular development using optical tissue clearing and light-sheet microscopy. Journal of Biomedical Optics 22, 31753 (2017) doi:10.1117/1.JBO.22.7.076011