Mr. Jacob Brandt Profile

Jacob Brandt

at Univ of Notre Dame

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Proceedings Article | 2 March 2022 Presentation + Paper

Low dosage 3D volume fluorescence microscopy imaging using compressive sensing

Varun Mannam, Jacob Brandt, Cody Smith, Scott Howard

Proceedings Volume 11966, 1196603 (2022) https://doi.org/10.1117/12.2607515

KEYWORDS: 3D image processing, Microscopy, Compressed sensing, Luminescence, In vivo imaging, Signal to noise ratio, Confocal microscopy, Reconstruction algorithms, Microscopes, 3D modeling

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Fluorescence microscopy has been a significant tool to observe long-term imaging of embryos (in vivo) growth over time. However, cumulative exposure is phototoxic to such sensitive live samples. While techniques like light-sheet fluorescence microscopy (LSFM) allows for reduced exposure, it is not well suited for deep imaging models. Other computational techniques are computationally expensive and often lack restoration quality. To address this challenge, one can use various low-dosage imaging techniques that are developed to achieve the 3D volume reconstruction using a few slices in the axial direction (z-axis); however, they often lack restoration quality. Also, acquiring dense images (with small steps) in the axial direction is computationally expensive. To address this challenge, we present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage. We present the theory and experimentally validate the approach. To demonstrate our technique, we capture a 3D volume of the RFP labeled neurons in the zebrafish embryo spinal cord (30 μm thickness) with the axial sampling of 0.1 μm using a confocal microscope. From the results, we observe the CS-based approach achieves accurate 3D volume reconstruction from less than 20% of the entire stack optical sections. The developed CS-based methodology in this work can be easily applied to other deep imaging modalities such as two-photon and light-sheet microscopy, where reducing sample photo-toxicity is a critical challenge.

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