Technology ID

Accelerating Multiview Registration and Iterative Deconvolution to Improve Spatial Resolution and Contrast in Fluorescence Microscopy

Lead Inventor
Shroff, Hari (NIBIB)
Li, Yue (Zhejiang University)
Wu, Yicong (NIBIB)
La Riviere, Patrick (University of Chicago)
Guo, Min (NIBIB)
Liu, Huafeng (Zhejiang University)
Software / Apps
Research Materials
Development Stages
Pre-Clinical (in vitro)
Research Products
Research Equipment
Lead IC

This technology includes algorithms and software that improve the speed of iterative deconvolution, a common method for improving spatial resolution and contrast in fluorescence microscopy images. These algorithms also improve the registration of multiview datasets, and apply deep learning to accelerate spatially varying deconvolution.

Commercial Applications
This suite of software and algorithms could be used to improve data acquired on any fluorescence microscope.

Competitive Advantages
Deconvolving or registering large multiview datasets is currently limited by the speed of computation; however, these new methods provide a tenfold to more than one-thousandfold improvement in the speed of data processing, allowing real-time deconvolution in many cases. It is expected that this speed improvement will accelerate the pace of biological discovery, as less computing time is necessary for these post-processing methods.
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