Low dosage 3D volume fluorescence microscopy imaging using compressive sensing
Not on the map. Why?
Abstract (arXiv)
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) allow 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 (30um thickness) with the axial sampling of 0.1um 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 3 Jan 2022.
Builds on
5 of 22 references.
References · 5
- 2020Machine learning for faster and smarter fluorescence lifetime imaging microscopycited by 5
- 2020Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEscited by 4
- 2020Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networkscited by 4
- 2020Packet Compressed Sensing Imaging (PCSI): Robust Image Transmission over Noisy Channelscited by 1
- 2021Deep learning-based super-resolution fluorescence microscopy on small datasetscited by 1
Built on by
Not cited yet.