CARS-imaging guidance for fs-laser ablation precision surgery

Ultrabroadband two-beam coherent anti-Stokes Raman scattering and spontaneous Raman spectroscopy of organic fluids: A comparative study

Spontaneous Raman spectroscopy is a well-established diagnostic tool, allowing for the identification of all Raman active species with a single measurement. Yet, it may suffer from low-signal intensity and fluorescent background. In contrast, coherent anti-Stokes Raman scattering (CARS) offers laser-like signals, but the traditional approach lacks the multiplex capability of spontaneous Raman spectroscopy. We present an ultrabroadband CARS setup which aims at exciting the full spectrum (300-3700 cm-1) of biological molecules. A dual-output optical parametric amplifier provides a ~7 fs pump/Stokes and a ~700 fs probe pulse. CARS spectra of DMSO, ethanol, and methanol show great agreement with spontaneous Raman spectroscopy and superiority in fluorescent environments. The spectral resolution proves sufficient to differentiate between the complex spectra of L-proline and hydroxyproline. Moreover, decay constants in the sub picosecond range are determined for individual Raman transitions, providing an additional approach for sample characterization.

[Multimodal spectroscopic imaging : A new, powerful tool for intraoperative tumor diagnostics]

BACKGROUND

The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization.

OBJECTIVE

Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology.

METHODS

Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG).

RESULTS

By using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in a label-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into a compact microscope or endoscopic probe concepts.

CONCLUSIONS

The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.

Real-time precision opto-control of chemical processes in live cells

Precision control of molecular activities and chemical reactions in live cells is a long-sought capability by life scientists. No existing technology can probe molecular targets in cells and simultaneously control the activities of only these targets at high spatial precision. We develop a real-time precision opto-control (RPOC) technology that detects a chemical-specific optical response from molecular targets during laser scanning and uses the optical signal to couple a separate laser to only interact with these molecules without affecting other sample locations. We demonstrate precision control of molecular states of a photochromic molecule in different regions of the cells. We also synthesize a photoswitchable compound and use it with RPOC to achieve site-specific inhibition of microtubule polymerization and control of organelle dynamics in live cells. RPOC can automatically detect and control biomolecular activities and chemical processes in dynamic living samples with submicron spatial accuracy, fast response time, and high chemical specificity.

There is a need to control molecular activities at high spatial precision. Here the authors report a real-time precision opto-control technology that detects a chemical-specific optical response from molecular targets, and precisely control photoswitchable microtubule polymerization inhibitors in cells.

An achromatic metafiber for focusing and imaging across the entire telecommunication range

Dispersion engineering is essential to the performance of most modern optical systems including fiber-optic devices. Even though the chromatic dispersion of a meter-scale single-mode fiber used for endoscopic applications is negligible, optical lenses located on the fiber end face for optical focusing and imaging suffer from strong chromatic aberration. Here we present the design and nanoprinting of a 3D achromatic diffractive metalens on the end face of a single-mode fiber, capable of performing achromatic and polarization-insensitive focusing across the entire near-infrared telecommunication wavelength band ranging from 1.25 to 1.65 µm. This represents the whole single-mode domain of commercially used fibers. The unlocked height degree of freedom in a 3D nanopillar meta-atom largely increases the upper bound of the time-bandwidth product of an achromatic metalens up to 21.34, leading to a wide group delay modulation range spanning from -8 to 14 fs. Furthermore, we demonstrate the use of our compact and flexible achromatic metafiber for fiber-optic confocal imaging, capable of creating in-focus sharp images under broadband light illumination. These results may unleash the full potential of fiber meta-optics for widespread applications including hyperspectral endoscopic imaging, femtosecond laser-assisted treatment, deep tissue imaging, wavelength-multiplexing fiber-optic communications, fiber sensing, and fiber lasers.

Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.