Hybrid Raman and Partial Wave Spectroscopy Microscope for the Characterization of Molecular and Structural Alterations in Tissue

We present a hybrid Raman spectroscopy (RS) and partial wave spectroscopy (PWS) microscope for the characterization of molecular and structural tissue alterations. The PWS performance was assessed with surface roughness standards, while the Raman performance with a silicon crystal standard. We also validated the system on stomach and intestinal mouse tissues, two closely-related tissue types, and demonstrate that the addition of PWS information improves RS data classification for these tissue types from R2 = 0.892 to R2 = 0.964 (norm of residuals 0.863 and 0.497, respectively). Then, in a proof-of-concept experiment, we show that the hybrid system can detect changes in intestinal tissues harvested from a tumorigenic Villin-Cre, Apcfl/wt mouse. We discuss how the hybrid modality offers new abilities to identify the relative roles of PWS morphological features and Raman molecular fingerprints, possibly allowing for their combination to enhance the study of carcinogenesis and early cancer diagnostics in the future.

Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems

Significance

Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed.

Aim

We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems.

Approach

We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system.

Results

We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35    dB (SNR), ∼ 8.65    a . u . (contrast), and ∼ 0.67    a . u . (BM score).

Conclusions

The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.