Single-molecule positioning based fast super-resolution imaging method and system

Abstract

The invention discloses a single-molecular positioning based fast super-resolution imaging method and system. The method comprises the following steps of: setting image acquisition parameters, hardware regulation and control parameters and super-resolution image processing and reconstruction parameters; performing hardware regulation and control according to the hardware regulation and control parameters; performing the super-resolution image processing and reconstruction on acquired images according to the super-resolution image processing and reconstruction parameters; and performing hardware regulation and control according to feedback results of super-resolution image processing and reconstruction. The single-molecular positioning based fast super-resolution imaging method adopts highalgorithm precision, can be realized through parallel arithmetic of a graphic process unit (GPU), and can achieve higher image processing and reconstruction speed and automatically regulate and control the whole imaging system including a shutter, an electric bench and a step motor, thereby realizing fast super-resolution imaging.

Description

technical field [0001] The invention belongs to the technical field of super-resolution imaging, and in particular relates to a fast super-resolution imaging method and system based on single-molecule positioning. Background technique [0002] For super-resolution imaging systems based on single-molecule localization, it is generally believed in the industry that EMCCD is a necessary detector for the system. The control of EMCCD is mainly realized through supporting software provided by the manufacturer, but the main problems are: [0003] In terms of hardware control, the supporting software only considers the control of EMCCD, and does not consider the control of other equipment, such as: the switching of excitation light and activation light and light intensity control in the process of super-resolution imaging, total internal reflection fluorescence (TIRF) imaging The control of the translation stage used, the control of the shutter (used to switch the laser), etc....

AI Technical Summary

Problems solved by technology

Therefore, the super-resolution imaging system based on supporting software has low experimental efficiency, time-consuming, and poor synchronization between various devices, which limits the realization of fast super-resolution imaging.

[0004] In terms of image processing, the supporting software does not provide super-resolution image processing and reconstruction functions

Secondly, the current commonly used algorithms for super-resolution image processing mainly include Gaussian fitting method, linear equation solution method, and centroid method, but the disadvantage of these algorithms is that they cannot achieve high processing speed and high positioning accuracy at the same time.

In addition, the current computing technology used in super-resolution image processing is based on CPU-based single-thread or multi-thread, resulting in slow processing and reconstruction speeds, making it difficult to achieve fast super-resolution imaging

Images