Waterhouse, DJ;
Stoyanov, D;
(2022)
Optimized spectral filter design enables more accurate estimation of oxygen saturation in spectral imaging.
Biomedical Optics Express
, 13
(4)
pp. 2156-2173.
10.1364/BOE.446975.
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Abstract
Oxygen saturation (SO2) in tissue is a crucially important physiological parameter with ubiquitous clinical utility in diagnosis, treatment, and monitoring, as well as widespread use as an invaluable preclinical research tool. Multispectral imaging can be used to visualize SO2 non-invasively, non-destructively and without contact in real-time using narrow spectral filter sets, but typically, these spectral filter sets are poorly suited to a specific clinical task, application, or tissue type. In this work, we demonstrate the merit of optimizing spectral filter sets for more accurate estimation of SO2. Using tissue modelling and simulated multispectral imaging, we demonstrate filter optimization reduces the root-mean-square-error (RMSE) in estimating SO2 by up to 37% compared with evenly spaced filters. Moreover, we demonstrate up to a 79% decrease in RMSE for optimized filter sets compared with filter sets chosen to minimize mutual information. Wider adoption of this approach will result in more effective multispectral imaging systems that can address specific clinical needs and consequently, more widespread adoption of multispectral imaging technologies in disease diagnosis and treatment.
| Type: | Article |
|---|---|
| Title: | Optimized spectral filter design enables more accurate estimation of oxygen saturation in spectral imaging |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/BOE.446975 |
| Publisher version: | https://doi.org/10.1364/BOE.446975 |
| Language: | English |
| Additional information: | Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146757 |
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