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Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA

Jiaxun Lin1,2, Kun Wang1,2, Zhen-Li Huang1,2

  • 1State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.

Biomedical Optics Express
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

A new method, HCC-STORM, accelerates multi-color single-molecule localization microscopy (SMLM) using a heterogeneous computing platform. This advancement speeds up image processing for colorimetry camera-based SMLM (CC-STORM) by three times without performance loss.

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Area of Science:

  • Biophysics
  • Microscopy
  • Computational Imaging

Background:

  • Conventional low-light cameras in single-molecule localization microscopy (SMLM) lack color distinction, necessitating complex setups for multi-color imaging.
  • Colorimetry cameras offer a simpler alternative for multi-color SMLM, but image extraction (CC-STORM) is time-consuming due to extensive pixel traversal.

Purpose of the Study:

  • To develop an accelerated method for multi-color SMLM using colorimetry cameras.
  • To integrate data processing tasks into a heterogeneous computing platform for enhanced speed.

Main Methods:

  • Developed HCC-STORM, integrating CC-STORM data processing onto a CPU-GPU-FPGA heterogeneous computing platform.
  • Leveraged FPGA parallelism and pipelining for efficient data handling.
  • Provided interfaces for C/C++, Python, Java, and Matlab to simplify algorithm deployment.

Main Results:

  • HCC-STORM achieved a threefold increase in execution speed compared to CC-STORM, without compromising performance.
  • Real-time data processing was enabled for 1024x1024 pixel images with a 30 ms exposure time.
  • Total data processing time per raw image was reduced to 26.9 ms.

Conclusions:

  • HCC-STORM significantly enhances the efficiency of colorimetry camera-based SMLM.
  • The developed heterogeneous computing platform accelerates computationally intensive tasks in SMLM.
  • This work advances CC-STORM and provides a robust platform for demanding computational imaging applications.