An enhanced depth-resolution Raman spectroscopy technique with rapid acquisition is proposed, leveraging spatial light modulator (SLM) based programmable cone-shell excitation and compressive sensing. To tackle the issues of inadequate depth resolution, substantial signal interference, and inefficient acquisition in conventional point-by-point scanning Raman detection (i.e., detection of one depth at a time), this study employs an SLM and an axicon lens to form a programmable cone-shell excitation with high and flexible depth selectivity, and uses the compressive sensing algorithm to reconstruct the sparsely collected Raman data, thus significantly improving depth resolution and detection time efficiency. The two-layer agar phantom experiment demonstrates that the proposed method can achieve better depth sensitivity and resolution, a peak signal-to-noise ratio (PSNR) close to 20 dB, and a detection time efficiency of approximately 500 times better than that of the conventional method. This study presents an efficient and non-contact Raman detection approach for rapidly and precisely examining different depths in layered tissues like skin, which significantly enhances the Raman technique’s potential for clinical applications.
Restricted Access
You are currently viewing a placeholder content from Vimeo. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from YouTube. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from Facebook. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from Google Maps. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from Google Maps. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from Mapbox. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from OpenStreetMap. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More InformationYou are currently viewing a placeholder content from X. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More Information