KEYWORDS: 3D image processing, Microscopy, Compressed sensing, Luminescence, In vivo imaging, Signal to noise ratio, Confocal microscopy, Reconstruction algorithms, Microscopes, 3D modeling
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.
KEYWORDS: Fluorescence lifetime imaging, Image segmentation, Microscopy, Denoising, In vivo imaging, Luminescence, Convolutional neural networks, Signal to noise ratio, Imaging systems, Signal processing
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal- to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots in vivo and ex vivo. By integrating image de- noising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental in vivo mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.
We propose and demonstrate a novel multiphoton frequency-domain fluorescence lifetime imaging microscopy (MPM-FD-FLIM) system that is able to generate 3D lifetime images in deep scattering tissues. The imaging speed of FD-FLIM is improved using phase multiplexing, where the fluorescence signal is split and mixed with the reference signal from the laser in a multiplexing manner. The system allows for easy generation of phasor plots, which not only address multi-exponential decay problems but also clearly represent the dynamics of the fluorophores being investigated. Lastly, a sensorless adaptive optics setup is used for FLIM imaging in deep scattering tissues. The capability of the system is demonstrated in fixed and living animal models, including mice and zebrafish.
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