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


Accuracy

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.

Thin Filaments are Missing with Other Software Vendor
Thin Filaments are Missing with Other Software Vendor
Microvolution Preserves Thin Filaments
Microvolution Preserves Thin Filaments

Image courtesy of Molecular Devices.


Clarity

Increased resolution

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.

No visible separation at 180 nm.
Widefield image at 180 nm separation
Visible 180 nm separation after deconvolution.
Clearly separated lines after deconvolution

Image courtesy of Tong Zhang and Puifai Santisakultarm, Salk Institute.


Microvolution has been cited in the following publications

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, (2018) doi:10.1083/jcb.201804137
Ozel, M. N. et al. Serial synapse formation through filopodial competition for synaptic seeding factors. BioRxiv (preprint) , (2018) doi:10.1101/506378
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
Thauland, T., Hu, K., Bruce, M., & Butte, M. Cytoskeletal adaptivity regulates T cell receptor signaling. Science Signaling 10, eaah3737 (2017) doi:10.1126/scisignal.aah3737
Ahern, M. et al. Hyperoxia impairs pro-angiogenic RNA production in preterm endothelial colony-forming cells. AIMS Biophysics 4, 284-297 (2017) doi:10.3934/biophy.2017.2.284
Miskolci, V., Hodgson, L. & Cox, D. Using Fluorescence Resonance Energy Transfer-Based Biosensors to Probe Rho GTPase Activation During Phagocytosis. Methods in Molecular Biology 125–143 (2016) doi:10.1007/978-1-4939-6581-6_9
Siegel, N., Lupashin, V., Storrie, B. & Brooker, G. High-magnification super-resolution FINCH microscopy using birefringent crystal lens interferometers. Nature Photonics 10, 802-808 (2016) doi:10.1038/nphoton.2016.207
Kotera, I. et al. Pan-neuronal screening in Caenorhabditis elegans reveals asymmetric dynamics of AWC neurons is critical for thermal avoidance behavior. eLife 5, e19021 (2016) doi:10.7554/eLife.19021
Huethorst, E. et al. Enhanced Human-Induced Pluripotent Stem Cell Derived Cardiomyocyte Maturation Using a Dual Microgradient Substrate. ACS Biomaterials Science & Engineering (2016) doi:10.1021/acsbiomaterials.6b00426
Siegel, N., Storrie, B., Bruce, M. & Brooker, G. CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography). Proc. SPIE 9336, Quantitative Phase Imaging, 93360S (2015) doi:10.1117/12.2081319
Lai, C. K. et al. Cell fate decisions in malignant hematopoiesis: leukemia phenotype is determined by distinct functional domains of the MN1 oncogene. PLOS ONE 9, e112671 (2014)
Perillo, E. P. et al. Enhanced 3D localization of individual RNA transcripts via astigmatic imaging. in Proc. SPIE 8950, Single Molecule Spectroscopy and Superresolution Imaging VII, 895003 (2014)
Oreopoulos, J., Berman, R. & Browne, M. Spinning-disk confocal microscopy. present technology and future trends. Methods Cell Biol. 123, 153-175 (2014)
Zanella, R. et al. Towards real-time image deconvolution: application to confocal and STED microscopy. Sci. Rep. 3, 2523 (2013)
Eklund, A., Dufort, P., Forsberg, D. & LaConte, S. M. Medical image processing on the GPU - Past, present and future. Med. Image Anal. 17, 1073-1094 (2013)
Bruce, M. A. & Butte, M. J. Real-time GPU-based 3D Deconvolution. Optics Express 21, 4766-73 (2013) doi:10.1364/OE.21.004766