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

Label-Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications

Autofluorophores are endogenous fluorescent compounds that naturally occur in the intra and extracellular spaces of all tissues and organs. Most have vital biological functions - like the metabolic cofactors NAD(P)H and FAD+, as well as the structural protein collagen. Others are considered to be waste products - like lipofuscin and advanced glycation end products - which accumulate with age and are associated with cellular dysfunction. Due to their natural fluorescence, these materials have great utility for enabling non-invasive, label-free assays with direct ties to biological function. Numerous technologies, with different advantages and drawbacks, are applied to their assessment, including fluorescence lifetime imaging microscopy, hyperspectral microscopy, and flow cytometry. Here, the applications of label-free autofluorophore assessment are reviewed for clinical and health-research applications, with specific attention to biomaterials, disease detection, surgical guidance, treatment monitoring, and tissue assessment - fields that greatly benefit from non-invasive methodologies capable of continuous, in vivo characterization.

Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Hyperspectral and multispectral imaging of cell and tissue autofluorescence is an emerging technology in which fluorescence imaging is applied to biological materials across multiple spectral channels. This produces a stack of images where each matched pixel contains information about the sample's spectral properties at that location. This allows precise collection of molecularly specific data from a broad range of native fluorophores. Importantly, complex information, directly reflective of biological status, is collected without staining and tissues can be characterised in situ, without biopsy. For oncology, this can spare the collection of biopsies from sensitive regions and enable accurate tumour mapping. For in vivo tumour analysis, the greatest focus has been on oral cancer, whereas for ex vivo assessment head-and-neck cancers along with colon cancer have been the most studied, followed by oral and eye cancer. This review details the scope and progress of research undertaken towards clinical translation in oncology.

Clinical applications of non-invasive multi and hyperspectral imaging of cell and tissue autofluorescence beyond oncology

Hyperspectral and multispectral imaging of cell and tissue autofluorescence employs fluorescence imaging, without exogenous fluorophores, across multiple excitation/emission combinations (spectral channels). This produces an image stack where each pixel (matched by location) contains unique information about the sample's spectral properties. Analysis of this data enables access to a rich, molecularly specific data set from a broad range of cell-native fluorophores (autofluorophores) directly reflective of biochemical status, without use of fixation or stains. This non-invasive, non-destructive technology has great potential to spare the collection of biopsies from sensitive regions. As both staining and biopsy may be impossible, or undesirable, depending on the context, this technology great diagnostic potential for clinical decision making. The main research focus has been on the identification of neoplastic tissues. However, advances have been made in diverse applications-including ophthalmology, cardiovascular health, neurology, infection, assisted reproduction technology and organ transplantation.

Band selection for oxygenation estimation with multispectral/hyperspectral imaging

Multispectral imaging provides valuable information on tissue composition such as hemoglobin oxygen saturation. However, the real-time application of this technique in interventional medicine can be challenging due to the long acquisition times needed for large amounts of hyperspectral data with hundreds of bands. While this challenge can partially be addressed by choosing a discriminative subset of bands, the band selection methods proposed to date are mainly restricted by the availability of often hard to obtain reference measurements. We address this bottleneck with a new approach to band selection that leverages highly accurate Monte Carlo (MC) simulations. We hypothesize that a so chosen small subset of bands can reproduce or even improve upon the results of a quasi continuous spectral measurement. We further investigate whether novel domain adaptation techniques can address the inevitable domain shift stemming from the use of simulations. Initial results based on in silico and in vivo experiments suggest that 10-20 bands are sufficient to closely reproduce results from spectral measurements with 101 bands in the 500-700 nm range. The investigated domain adaptation technique, which only requires unlabeled in vivo measurements, yielded better results than the pure in silico band selection method. Overall, our method could guide development of fast multispectral imaging systems suited for interventional use without relying on complex hardware setups or manually labeled data.

Quantification of irrigated lesion morphology using near-infrared spectroscopy

There are currently limited means by which lesion formation can be confirmed during radiofrequency ablation procedures. The purpose of this study was to evaluate the use of NIRS-integrated RFA catheters for monitoring irrigated lesion progression, ex vivo and in vivo. Open-irrigated NIRS-ablation catheters with optical fibers were fabricated to sample tissue diffuse reflectance. Spectra from 44 irrigated lesions and 44 non-lesion sites from ex vivo swine hearts (n = 15) were used to train and evaluate a predictive model for lesion dimensions based on key spectral features. Additional studies were performed in diluted blood to assess NIRS signatures of catheter-tissue contact status. Finally, the potential of NIRS-RFA catheters for guiding lesion delivery was evaluated in a set of in vivo pilot studies conducted in healthy pigs (n = 4). Model predictions for lesion depth (R = 0.968), width (R = 0.971), and depth percentage (R = 0.924) correlated well with measured lesion dimensions. In vivo deployment in preliminary trials showed robust translational consistency of contact discrimination (P < 0.0001) and lesion depth parameters (< 3% error). NIRS empowered catheters are well suited for monitoring myocardial response to RF ablation and may provide useful intraprocedural feedback for optimizing treatment efficacy alongside current practices.

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

PURPOSE

Multiple studies have shown that spectral analysis of tissue autofluorescence can be used as a live indicator for various pathophysiological states of cardiac tissue, including ischemia, ablation-induced damage, or scar formation. Yet today there are no percutaneous devices that can detect autofluorescence signals from inside a beating heart. Our aim was to develop a prototype catheter to demonstrate the feasibility of doing so.

METHODS AND RESULTS

Here we summarize technical solutions leading to the development of a percutaneous catheter capable of multispectral imaging of intracardiac surfaces. The process included several iterations of light sources, optical filtering, and image acquisition techniques. The developed system included a compliant balloon, 355 nm laser irradiance, a high-sensitivity CCD, bandpass filtering, and image acquisition synchronized with the cardiac cycle. It enabled us to capture autofluorescence images from multiple spectral bands within the visible range while illuminating the endocardial surface with ultraviolet light. Principal component analysis and other spectral unmixing post-processing algorithms were then used to reveal target tissue.

CONCLUSION

Based on the success of our prototype system, we are confident that the development of ever more sensitive cameras, recent advances in tunable filters, fiber bundles, and other optical and computational components makes it possible to create percutaneous catheters capable of acquiring hyper or multispectral hypercubes, including those based on autofluorescence, in real-time. This opens the door for widespread use of this methodology for high-resolution intraoperative imaging of internal tissues and organs-including cardiovascular applications.

Key factors behind autofluorescence changes caused by ablation of cardiac tissue

Radiofrequency ablation is a commonly used clinical procedure that destroys arrhythmogenic sources in patients suffering from atrial fibrillation and other types of cardiac arrhythmias. To improve the success of this procedure, new approaches for real-time visualization of ablation sites are being developed. One of these promising methods is hyperspectral imaging, an approach that detects lesions based on changes in the endogenous tissue autofluorescence profile. To facilitate the clinical implementation of this approach, we examined the key variables that can influence ablation-induced spectral changes, including the drop in myocardial NADH levels, the release of lipofuscin-like pigments, and the increase in diffuse reflectance of the cardiac muscle beneath the endocardial layer. Insights from these experiments suggested simpler algorithms that can be used to acquire and post-process the spectral information required to reveal the lesion sites. Our study is relevant to a growing number of multilayered clinical targets to which spectral approaches are being applied.

Application of unsupervised learning to hyperspectral imaging of cardiac ablation lesions

Atrial fibrillation is the most common cardiac arrhythmia. It is being effectively treated using the radiofrequency ablation (RFA) procedure, which destroys culprit tissue and creates scars that prevent the spread of abnormal electrical activity. Long-term success of RFA could be improved further if ablation lesions can be directly visualized during the surgery. We have shown that autofluorescence-based hyperspectral imaging (aHSI) can help to identify lesions based on spectral unmixing. We show that use of k -means clustering, an unsupervised learning method, is capable of detecting RFA lesions without a priori knowledge of the lesions' spectral characteristics. We also show that the number of spectral bands required for successful lesion identification can be significantly reduced, enabling the use of increased spectral bandwidth. Together, these findings can help with clinical implementation of a percutaneous aHSI catheter, since by reducing the number of spectral bands one can reduce hypercube acquisition and processing times, and by increasing the spectral width of individual bands one can collect more photons. The latter is of critical importance in low-light applications such as intracardiac aHSI. The ultimate goal of our studies is to help improve clinical outcomes for atrial fibrillation patients.