Optimization of wavelength selection for multispectral image acquisition: a case study of atrial ablation lesions

Abstract

Abstract:In vivo autofluorescence hyperspectral imaging of moving objects can be challenging due to motion artifacts and to the limited amount of acquired photons. To address both limitations, we selectively reduced the number of spectral bands while maintaining accurate target identification. Several downsampling approaches were applied to data obtained from the atrial tissue of adult pigs with sites of radiofrequency ablation lesions. Standard image qualifiers such as the mean square error, the peak signal-to-noise ratio, the structural similarity index map, and an accuracy index of lesion component images were used to quantify the effects of spectral binning, an increased spectral distance between individual bands, as well as random combinations of spectral bands. Results point to several quantitative strategies for deriving combinations of a small number of spectral bands that can successfully detect target tissue. Insights from our studies can be applied to a wide range of applications.

Application of unsupervised learning to hyperspectral imaging of cardiac ablation lesions

“…We have recently demonstrated the ability of autofluorescence hyperspectral imaging to reveal ablated tissue using linear unmixing protocols. [10][11][12][13][14] Here, we have shown that k-means, an approach that does not require a priori knowledge of tissue spectra, can be also an effective means to detect lesions from aHSI hypercubes. The average accuracy for detection by k-means (k ¼ 10) using 31 features was about 74% when compared to reference images.…”

“…8,9 Therefore, our group has been exploring a visualization approach called autofluorescence hyperspectral imaging (aHSI) and has shown that it is effective in revealing ablation-induced damage including the highly collagenous human left atrium. [10][11][12][13][14] To implement the aHSI approach during the RFA procedure, one has to deliver ultraviolet (UV) light (λ ¼ 365 nm) to the heart by an optical fiber threaded into a percutaneous catheter. 15,16 This allows illumination of the endocardial atrial surface, which is highly autofluorescent.…”

Key factors behind autofluorescence changes caused by ablation of cardiac tissue

“…The important practical implication of our studies is the dramatically reduced amount of spectral information needed to reveal the ablated tissue. In the past, we have attempted to approach this issue mathematically 12 , 42 . In those studies, we used combinatorial tools to screen all possible wavelength combinations from the Auf-HSI datasets for a minimal number of wavelengths that can still provide good-quality outcomes.…”

A Percutaneous Catheter for In Vivo Hyperspectral Imaging of Cardiac Tissue: Challenges, Solutions and Future Directions

“…However, if the number of collected spectral wavelengths is discrete and the bands are wide and/or separated, it is then called multispectral imaging. Spectra from each pixel are extracted and matched to existing spectral libraries or, alternatively, spectra are sorted using principal component analysis or other types of mathematical algorithms to reveal major spectral signatures in a sample [30,31]. Based on the probability that the spectrum of an individual pixel matches a chosen target spectrum, a grey scale component image is then formed for each target.…”

“…That is why we use term 'hyperspectral' when describing our findings. Our recent post-acquisition analysis of continuous spectra from these hyperspectral datasets revealed the most informative wavelengths, allowing us to consider use of fewer and wider bands [30,38]. In order not to confuse our readers by juggling between the two terms while referring to our previous publications and bench datasets, we decided to continue to refer to this technology as hyperspectral, although the final iteration of the clinical device is most likely to be classified as 'multispectral'.…”