Towards high-speed computational scattered light imaging by introducing compressed sensing for optimized illumination

Franca auf der Heiden; Oliver Münzer; Simon van Staalduine; Katrin Amunts; Markus Axer; Miriam Menzel

doi:10.1117/12.3000869

12 March 2024 Towards high-speed computational scattered light imaging by introducing compressed sensing for optimized illumination

Franca auf der Heiden, Oliver Münzer, Simon van Staalduine, Katrin Amunts, Markus Axer, Miriam Menzel

Author Affiliations +

Proceedings Volume 12853, High-Speed Biomedical Imaging and Spectroscopy IX; 1285303 (2024) https://doi.org/10.1117/12.3000869
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We propose the application of Compressed Sensing to Computational Scattered Light Imaging to decrease measurement time and data storage. Computational Scattered Light Imaging (ComSLI) determines three-dimensional fiber orientations and crossings in biomedical tissues like brain tissue. Currently, conventional ComSLI is time-consuming and generates large data. Compressed Sensing reconstructs signals with fewer samples than required by the Shannon-Nyquist theorem with minimal perceptual loss, significantly reducing the number of measurements. We introduce an optimized illumination strategy for ComSLI based on the Discrete Cosine Transform and validate it by reconstructing characteristic scattering patterns in vervet brain tissue, thereby demonstrating the feasibility of Compressed Sensing in ComSLI.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Franca auf der Heiden, Oliver Münzer, Simon van Staalduine, Katrin Amunts, Markus Axer, and Miriam Menzel "Towards high-speed computational scattered light imaging by introducing compressed sensing for optimized illumination", Proc. SPIE 12853, High-Speed Biomedical Imaging and Spectroscopy IX, 1285303 (12 March 2024); https://doi.org/10.1117/12.3000869

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