A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization.

Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm2 and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.

Time-gated interferometric detection increases Raman scattering to fluorescence signal ratio in biological samples.

Attainable levels of signal-to-background ratio (SBR) in Raman spectroscopy of biological samples is limited by the presence of endogenous fluorophores. It is customary to remove the ubiquitous fluorescence background using postacquisition data processing. However, new approaches are needed to reduce background contributions and maximize the fraction of the sensor dynamical range occupied by Raman photons. Time-resolved detection using pulsed lasers and time-gated measurements can be used to address the signal-to-background problem in biological samples by limiting light detection to nonresonant interaction phenomena with relaxation time scales occurring on sub-nanosecond time scales, thereby excluding contributions from resonant phenomena such as fluorescence. A time-gated Fourier-transform spectrometer was assembled using a commercially available interferometer, a single channel single-photon avalanche diode and time tagging electronics. A time gate of 300 ps increased the signal-to-background-ratio of the 1440 cm-1 Raman band from 36% to 69% in an olive oil sample hereby demonstrating the potential of this approach for autofluorescence suppression.

Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization.

SIGNIFICANCE: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery. AIM: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system. APPROACH: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1 cm2. Machine learning classifiers were trained and validated on porcine adipose and muscle tissue. RESULTS: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95 mm2 in ∼3 min using a support vector machine. CONCLUSIONS: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.