Indexed in

License and use

Citations

4

CITATIONS

4 Total citations

4 Recent citations

n/a Field Citation Ratio

n/a Relative Citation Ratio

Cited 4 times in

Altmetrics

Grant support

The authors would like to thank neurosurgeons and staff of the Hospital Universitario 12 de Octubre. This work was supported by the TALENT-HIPSTER (High Performance Systems and Technologies for E-health and Fish Farming) (PID2020-116417RB-C41) research project, funded by the Spanish Ministry of Science and Innovation, and by the European project STRATUM (3D Decision Support Tool for Brain Tumor Surgery) under Grant No. 101137416.

Analysis of institutional authors

Share

Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification

Publicated to:Journal Of Biomedical Optics. 29 (9): - 2024-09-01 29(9), DOI: 10.1117/1.JBO.29.9.093510

Authors: Martín-Pérez, A; de Ternero, AM; Lagares, A; Juarez, E; Sanz, C

Affiliations

Abstract

Significance: Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time. Aim: Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification. Approach: We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. Results: The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at lambda=422 nm, two for HbO(2) at lambda=542 nm and lambda=576 nm, and one for water at lambda=976 nm. Conclusion: The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

Keywords