Deep learning-assisted reconstruction of Raman speckle images transmitted through biological tissues

Precise delineation of tumor tissues during surgery is hindered by the strong scattering and absorption of photons in biological tissues, which obscure molecular imaging signals and hinder intraoperative navigation. Raman bioimaging using surface-enhanced Raman scattering (SERS) nanoprobes, offers high molecular specificity and sensitivity but still suffers from severe degradation when the signals transmitted through scattering media. Here, we present a deep learning-assisted approach for reconstructing Raman speckle images obscured by biological scattering. We synthesized gap-enhanced resonance Raman tags (GERRTs) and used them to draw Raman patterns on paper. These patterns were tested to build the dataset of image pairs, including reference Raman images that collected directly from the patterns and speckle Raman images after signal transmitting through 4-mm porcine tissues. To recover these images, we employed a U-Net architecture featuring symmetric encoder-decoder pathways with skip connections, which effectively preserves high-resolution spatial details lost in scattering tissue. This model achieved superior reconstruction performance, with an average structural similarity index measure (SSIM) of 0.539 between reconstructed images and reference images, and a peak signal-to-noise ratio (PSNR) of 19.04 dB. This work provides a proof-of-concept framework for deep learning-assisted Raman imaging through scattering tissues and holds promise for advancing noninvasive optical molecular imaging and facilitating precise surgical navigation in oncology.