Image Deconvolution Techniques with Robust Background Estimation Applied to Photoactivation Localization Microscopy (palm)
نویسندگان
چکیده
Deconvolution techniques have long been used for image reconstruction, and because it works particularly well on sparse images, it has recently been applied successfully in stochastic optical reconstruction microscopy (STORM) [1,2]. While having the advantage of using genetically encoded probes (applicable in live-cell imaging) photoactivated localization microscopy PALM [3] however, presents distinct challenges. The organic dyes used in STORM typically yield 5-10 more photons then the brightest photo-switching fluorescent proteins used in PALM. Also, we observe a buildup of structured ‘background’ fluorescence, perhaps due to weak and slowly switching/bleaching fluorescent molecules. These factors result in an overall low signal to noise ratio, which strongly reduces reliable and accurate localization [4]. Using both synthetic and real PALM data, characterized by high event density, complex background, and low signal to noise ratios, we show that using Richardson-Lucy (RL) deconvolution methods [5] combined with a novel background estimation algorithm, genuine biological structures can be discerned, which are lost using traditional, fit-based and centre of mass localization methods [6,7]. Furthermore, because deconvolution is computationally expensive, these techniques have thus far only been used on small data sets [2, 5]. A typical PALM/STORM acquisition consists of 10,000-15,000 512x512 images, and to cope with these large data sets, we have adapted state of the art RL-deconvolution solvers and used a GPU implementation, allowing us to process full data-sets in mere hours, several orders of magnitude faster than existing deconvolution-based methods.
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