Artem Shydliukh | Searcharxiv

arXiv2026

Free-electron laser-based extended wide-field mid-infrared photothermal imaging for biomedical and microplastic analysis

Wide-field mid-infrared photothermal (MIP) imaging offers rapid labelfree chemical contrast for biomedical and polymer analysis. However, its field of view (FOV) is limited by the pulse intensity of conventional infrared lasers. Here, we present a wide-field MIP microscope that uses a high-power free-electron laser (FEL) rather than a quantum cascade laser (QCL) as the pump source to achieve a substantially larger FOV. Both implementations use counter-propagating beam paths with a 450 nm LED as the probe source and a CMOS camera that records images using a virtual lock-in detection scheme. QCL nanojoule pulse energies enables FOV of around 45 micrometers for widefield MIP imaging with a sub-micrometer resolution for polystyrene beads, Mycobacterium tuberculosis infected fixed tissues, and laryngeal cancer cryosections. IR spectra in the range of 1000-1800 wavenumbers can be reconstructed by tuning the QCL. FEL pulse energies of up to microjoules expand the FOV by a factor of nearly 20 as demonstrated by wide-field MIP imaging of polystyrene beads, single cells, and murine brain tissue. We discuss current challenges and further improvements to implement high-power IR lasers for wide-field MIP imaging with even larger FOVs in the context of biomedical research and diagnostics.

physics.optics↗

arXiv2026

Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing

High-quality Fourier Transform Infrared (FTIR) imaging usually needs extensive signal averaging to reduce noise and drift which severely limits clinical speed. Deep learning can accelerate imaging by reconstructing spectra from rapid, single-scan inputs. However, separating noise and baseline drift simultaneously without ground truth is an ill-posed inverse problem. Standard black-box architectures often rely on statistical approximations that introduce spectral hallucinations or fail to generalize to unstable atmospheric conditions. To solve these issues we propose a physics-informed cascade Unet that separates denoising and baseline correction tasks using a new, deterministic Physics Bridge. This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer to enforce spectroscopic constraints instead of learning statistical approximations. We benchmarked this approach against a standard single Unet and a traditional Savitzky-Golay/SNIP workflow. We used a dataset of human hypopharyngeal carcinoma cells (FaDu). The cascade model outperformed all other methods, achieving a 51.3% reduction in RMSE compared to raw single-scan inputs, surpassing both the single Unet (40.2%) and the traditional workflow (33.7%). Peak-aware metrics show that the cascade architecture eliminates spectral hallucinations found in standard deep learning. It also preserves peak intensity with much higher fidelity than traditional smoothing. These results show that the cascade Unet is a robust solution for diagnostic-grade FTIR imaging. It enables imaging speeds 32 times faster than current methods.

eess.IV↗