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

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