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It will guide you through the necessary steps of your imaging experiment to produce the most insightful and data-laden image stack.

Add channels from the desired imaging mode and tune the Protocol to your needs, before saving for re-use, per Windows user, along with channel settings and imaging modes.

In these circumstances, obtaining a high temporal resolution is paramount, while photo-bleaching is not critically endangering the sample during such a time lapse.

Fusion is the result of over 100 person-years of shared imaging and development experience, making it easy to design an experiment, choose necessary devices and configure optimal data acquisition protocols.

It simplifies the control of the Dragonfly system, translating imaging modes into software states and fluorophores into channels to help you build protocols in just a few mouse clicks.

Integrating deconvolution is simple, tasking the GPU with the associated calculations so that acquisition continues unaffected: a big time saver!

You can control the critical angle for TIRF and penetration depth into the specimen, or choose to operate in oblique or HILO (Highly Inclined Laminated Optical Sheet) mode to capture weaker signals at better S/N ratio.

As life science research accelerates and demands greater throughput for deeper study, we suggest the community should consider this new and powerful platform wherever there is a need for fast, sensitive, high resolution confocal imaging.

Extended observations, large fields of view and high quality data provide the input for powerful statistical analysis and data mining.

Visualize Complex Data Welcome to a new era of data interpretation, plotting and presentation.

Fusion provides automated tools to combine neighbouring images into large area datasets in 2D and 3D. Dragonfly’s large field of view and illumination quality provided by Borealis, combined with Fusion’s image processing tools allow large samples to be captured in exquisite detail.

Central to the Dragonfly concept is the goal of maximizing throughput, which is achieved by optimizing both imaging performance and data flow.

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