Micromolar chemical imaging by high-energy low-photodamage Coherent Anti-stokes Raman Scattering (HELP-CARS)
Guangrui Ding, Dingcheng Sun, Yifan Zhu, Rong Tang, Hongli Ni, Yuhao Yuan, Haonan Lin, Ji-Xin Cheng

TL;DR
HELP-CARS significantly enhances chemical imaging sensitivity by combining high-energy excitation, low photodamage, and advanced denoising, enabling micromolar detection levels and detailed biological analysis.
Contribution
This work introduces a novel HELP-CARS platform that boosts Raman excitation efficiency and reduces background noise using pulse chirping and deep learning, surpassing previous limitations.
Findings
Achieved ~300x increase in excitation efficiency
Improved signal-to-background ratio by 11 times
Enabled micromolar sensitivity for live cell imaging
Abstract
Coherent anti-Stokes Raman scattering (CARS) microscopy offers label-free chemical imaging capabilities, but its performance is constrained by small Raman scattering cross-section, strong non-resonant background (NRB), and limited signal-to-noise ratio (SNR). Here, we introduce a high-energy, low-photodamage CARS (HELP-CARS) platform designed to overcome these physical limitations. By employing a 1-MHz non-collinear optical parametric amplifier (NOPA) with extensive pulse chirping, HELP-CARS increases the coherent Raman excitation efficiency by ~300 times and improves the signal-to-nonresonant background ratio by 11 times, while inducing negligible damage during live cell imaging. Furthermore, to remove non-independent noise and physically entangled non-resonant background, we incorporate self-supervised deep-learning denoising and background removal based on the Kramers-Kronig…
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Taxonomy
TopicsSpectroscopy Techniques in Biomedical and Chemical Research · Combustion and flame dynamics · Ocular and Laser Science Research
