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.


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

Microvolution Preserves Thin Filaments

Image courtesy of Molecular Devices.


Deconvolve your fluorescence images, from small to very large, in a fraction of the time


Where Microvolution has been cited

McNamara, G., Difilippantonio, M., Ried, T. & Bieber, F. R. Microscopy and Image Analysis. in Current Protocols in Human Genetics , 4.4.1-4.4.89 (2017) 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 1519, 125-43 (2017) 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 micro-gradient substrate. ACS Biomaterials Science & Engineering 2, 2231-2239 (2016) doi:10.1021/acsbiomaterials.6b00426
Siegel, N., Storrie, B., Bruce, M. & Brooker, G. CINCH (confocal incoherent correlation holography) high spatial resolution super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography). in 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